Here are 9 AI use cases for startups that help you achieve your goals, while improving customer satisfaction, team productivity, and growth.

What we’ll cover:

1. AI in sales: Build stronger customer relationships
2. AI in customer service: Automate support to boost satisfaction
3. AI for marketing: Reach the right audience with targeted campaigns
4. AI in operations: Save time and cut costs
5. AI in finance: Budget smarter and boost security
6. AI in HR: Make hiring easier and boost employee satisfaction
7. AI in security: Keep your data and customers safe
8. AI in product development: Stay ahead of market trends
9. AI in data analysis: Turn insights into action

1. AI in sales: Build stronger customer relationships

Leaders responsible for CRM and AI understand the criticality of data readiness, and in ASEAN, they are rising to meet this moment. AI transforms how startups handle sales, from forecasting to relationship-building. Among AI use cases for startups, sales applications allow teams to build stronger customer connections and predict future needs.

Sales forecasting with AI

AI tools can study past sales data to see trends and predict busy or slow periods. With this information, you can plan better and allocate resources effectively.

• Streamline resource allocation with AI insights.
• Make smarter decisions through clear forecasting.

Sales teams can leverage AI-powered customer relationship management (CRM) like Salesforce to boost efficiency. With tools like Agentforce, your sales team can gain a deeper understanding of customer needs and deliver more relevant recommendations, creating meaningful connections that drive sales. ating lead tracking and allowing reps to focus more on customer relationships.

Improving customer relationships with AI

AI-powered CRM tools store and analyse customer data so you can offer relevant deals and communication. Tasks like data entry are automated, saving your team time.

• Personalise messaging to make customers feel valued.
• Save time by automating tasks and focusing on relationships.

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2. AI in customer service: Automate support to boost satisfaction

Great customer service can set your startup apart. With AI, you can speed up response times and ensure each customer receives the support they need. This is one of the most widely adopted AI use cases for startups and SMBs, as it enables efficient and scalable customer service.

AI-powered support

AI tools like chatbots and virtual assistants allow you to provide support 24/7. They can answer common questions, solve simple issues, and send complex ones to human agents through collaborative tools like Small Business Slack, creating a smoother experience for customers. In fact, 87% of small business teams use AI to personalise the customer journey across channels, helping enhance customer satisfaction and resolve identity issues.

• Get 24/7 customer support with an AI agent and help customers any time. 
• Resolve customer issues faster with automating routine questions, reducing wait time. 

Reading customer sentiment with AI

AI can analyse feedback from surveys, social media, and customer service interactions to understand customer sentiments better. This helps you fix issues before they grow, keeping customers happy and loyal.

• Build customer loyalty by resolving issues quickly to foster trust.
• Improve product quality by using customer insights to better meet needs.

3. AI in marketing: Reach the right audience with targeted campaigns

AI marketing tools give you insights into your audience, allowing you to create more targeted campaigns that drive higher engagement. AI use cases for startups in marketing are among the most popular due to their ability to increase customer engagement and conversion rates.

Personalised campaigns for higher engagement

With AI, you can use customer data to send highly personalised messages, increasing engagement. With this approach, you’ll see a better return on investment (ROI) and have the flexibility to make real-time adjustments as you track your campaign’s progress. Around 82% of small business marketing teams also leverage AI to offer real-time deals, ensuring customers receive relevant offers when they’re most interested.

• Achieve higher ROI with targeted campaigns that drive more conversions.
• Make real-time adjustments as AI tracks and fine-tunes your messaging.

Predicting trends with AI

AI-powered predictive tools help you spot trends and prepare for customer needs. From product recommendations to ad adjustments, AI lets you stay ahead of the curve.

• Reduce risk by avoiding overstocking or shortages with trend forecasting.
• Stay adaptable by quickly adjusting to meet changing customer needs.

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4. AI in operations: Save time and cut costs

With AI and solutions like SMB Commerce storefront, you can reduce operational hurdles, managing orders and inventory more effectively while cutting down on costs.

Automate routine tasks

AI can handle repetitive tasks like data entry, invoicing, and team workflows. This reduces human errors and lets your team focus on more important work. 88% of SMBs use AI for data integration and process automation, helping reduce repetitive work and improve efficiency.

• Reduce costs with automation for repetitive tasks.
• Ensure clean data with fewer errors for reliable insights.

Optimise store performance with AI

AI can track your storefront performance so you can be more informed and make better decisions: 

• Use your data to build valuable insights about purchases and transactions.
• Reduce issues by using AI to predict problems for proactive action.

5. AI in finance: Budget smarter and boost security

AI-driven finance tools help startups budget, forecast, and detect fraud. Financial management is a critical AI use case for startups as it enhances budgeting accuracy and security.

Accurate budgeting and forecasting

AI tools can predict revenue, expenses, and cash flow, allowing you to create budgets that support growth while minimising risk.

• Create smarter budgets with reliable forecasts
• Drive growth decisions using valuable financial insights.

Real-time fraud detection

Data intelligence scans transactions to spot suspicious activity, adding an extra layer of security to protect your business.

• Receive instant alerts for irregular transactions
• Strengthen customer trust with reliable fraud detection

6. AI in Human Resources: Easy hiring and boosting employee satisfaction

For human resources, AI tools can improve recruitment and boost engagement, both of which contribute to a positive work environment. HR-related AI use cases for startups make the hiring process faster and more inclusive.

Simplifying hiring with AI

Make hiring easy by using AI agents to review resumes to find candidates with the right skills, speeding up hiring and reducing bias.

• Build inclusivity through skills-focused hiring.
• Shorten the hiring process with automation.

Increasing employee engagement

Boost your team morale with AI-backed analysis. AI analyses employee feedback to measure satisfaction, giving you insights to make a better team environment.

• Reduce turnover by increasing employee satisfaction.
• Enhance support through actionable insights for your team.

7. AI in security: Keep your data and customers safe

Startups often handle sensitive information, making cybersecurity essential. 72% of business leaders in Singapore cite privacy and security concerns as a barrier to purchasing AI, while 92% agree that trust is critical when partnering with an AI vendor. AI-driven cybersecurity is one of the most crucial AI use cases for startups, offering proactive threat detection. 

Spotting cyber threats

With AI monitoring network activity and user behavior, you can detect unusual patterns and prevent breaches before they happen, strengthening your business’s security.

• Monitor network security 24/7 with AI.
• Flag unusual patterns instantly with quick alerts.

Securing access

Stay secure with AI that controls who has access to data, making sure only authorised users can view sensitive information.

• Prevent breaches with controlled access to avoid unauthorised data use.
• Increase efficiency through automated access management to save time and reduce errors.

8. AI in product development: Stay ahead of market trends

AI assists in product development by spotting customer needs and market trends, helping you create products that customers want. Product development is an innovative AI use case for startups, enabling them to stay competitive.

Identify trends

Spot patterns with AI that can analyse data to reveal customer preferences and trends, helping you create products that meet demand.

• Match customer needs with trend insights to create desired products.
• Find opportunities by analysing trends to identify market gaps.

Optimise features based on feedback

AI can scan customer feedback to prioritise features, helping you develop products faster and more effectively.

• Streamline development with data-based choices to speed up timelines.
• Enhance satisfaction by prioritising popular features to keep customers happy.

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9. AI in data analysis: Turn insights into action

Data analysis is a core AI use case for startups, allowing them to analyse large datasets, uncover patterns, and support data-driven decision-making.

Business intelligence and reporting

With an AI-powered solution like Data Cloud, you can analyse your data to generate meaningful reports, which helps to quickly identify opportunities and challenges — helping you stay competitive.

• Turn data into clear, strategic reports for actionable insights.
• Make informed decisions faster with AI.

Predictive analytics for various business functions

Predictive AI uses historical data to predict future outcomes, from customer behavior to sales performance. This helps startups make proactive, data-backed decisions.

• Anticipate trends and challenges to reduce uncertainty.
• Enhance forecasting accuracy for greater confidence.

Data visualisation and dashboards

AI-powered dashboards present data in a visually appealing and easy-to-understand format, making it easier to communicate findings and track key metrics in real-time.

• Track data in real time with visual dashboards that update instantly.
• Simplify data presentation for easier accessibility and understanding.

Final thoughts on AI use cases for startups

Your startup is ready to be powered by AI. By exploring AI use cases for startups and SMBs with tools like Salesforce’s Starter Suite, SMB Commerce storefront, and Agentforce, you’re setting up for a smarter, more connected future. This blog has shown how AI can make a difference in areas like sales, marketing, customer service, operations, security, finance, and HR.

With the right tools, you can get your customer data ready for AI with Starter Suite — try it out for free.

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But in this fast-moving technical landscape, using personal data requires extra special handling. This is especially true when it comes to building and maintaining a trusted relationship with your customers, while remaining compliant with the growing number of regulations around the world.

Why data privacy compliance is non-negotiable

Law No. 27 of 2022 on Personal Data Protection (PDP Law) in Indonesia, Personal Data Protection Act (PDPA) in Singapore, Personal Data Protection Act (PDPA) in Malaysia, set standards for data protection.

Organisations face growing pressure to protect individual privacy rights while navigating an increasingly complex regulatory landscape. At the same time, consumers are more aware and concerned about how their personal information is collected, used, and safeguarded. By strengthening privacy and compliance frameworks, businesses can build trust with consumers and reduce legal risks and potential fines.

So, how do you keep customer data secure and protected while staying compliant with global regulations? Here are five steps to help you strengthen data privacy and compliance in your organisation.

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1. Understand the data you have — and classify it

Categorising data by sensitivity — such as personal information, financial data, or health records — is crucial for effective data management and protection. It’s vital to understand what a piece of data is, how it can be used, and the protections around it. In turn, that allows you to implement targeted security measures and access controls that align with regulations. 

Proper data classification also helps pinpoint where sensitive information resides within an organisation. It sets you up to apply appropriate safeguards, such as encryption, pseudonymisation, or anonymisation.

This clear classification not only maintains compliance with data protection laws but also supports quick responses to data subject access requests, sticking to the principles of data minimisation and purpose limitation, all while creating a trusted relationship with your customers. 

Salesforce provides a free data classification tool that simplifies the process of classifying every standard and custom field, helping you identify your most sensitive data and incorporate it into your security and privacy policies.curity and privacy policies.

2. Audit and update your access controls

With your data classified, you can now assess who in your organisation should have access to what data. Audit your access controls and determine whether access rights are appropriate. 

Check if any accidental or intentional over-permissioning occurred, and review and update access permissions based on employee roles, data sensitivity, and regulatory requirements. Doing so can mitigate risks associated with data breaches and non-compliance. 

By proving the process of access control management, you’re prepared for a number of global regulatory inspections or audits while ensuring only those who absolutely need access to sensitive data can see it. You can manage access controls effectively by using tools that allow you to set permissions at various levels, ensuring that only authorised individuals can access sensitive data.

Salesforce allows you to manage access at a user, objective, and field level using the permissions and access settings. This approach helps maintain security and compliance across your organisation.

3. De-identify data in your testing environment

One way companies experience data breaches is by using real data in their testing environments. Everyone wants realistic data to test their application. But by anonymising or pseudonymising sensitive information, organisations can simulate real-world scenarios without compromising individuals’ privacy rights or experiencing a data breach. 

This practice ensures that any data classified as personally identifiable information (PII) in the first step (such as names, addresses, and social security numbers), is not exposed during software testing, reducing the risk of data breaches or unauthorised access. 

De-identification is key to data minimisation because it ensures you use only the necessary data for testing. This limits the risk of data exposure and keeps you in line with privacy laws. By using solid de-identification techniques and following ethical data practices, you can protect sensitive information and build stronger trust with your customers.

Solutions that protect sensitive data in secure testing environments, like Data Mask, are available to assist in de-identifying data. These tools can help you create policies to mask or replace sensitive information with non-identifiable data — using methods like random characters, similarly mapped words, pattern-based masking, or even deleted data. Pairing these tools with data classification (mentioned in the first step) ensures all your sensitive data is included.

Additionally, consider solutions that provide complete visibility into your testing environments and manage security. With tools like Security Center, you can centrally monitor, view, and manage your security health across multiple environments from a single platform, making it easier to maintain a strong compliance and security posture with actionable insights.

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4. Set up monitoring and alerts on sensitive data 

With your data classified, access controls in place, and apps tested for privacy compliance, it’s time to set up monitoring, logging, and alerting systems to keep everything secure.

Tracking and logging user activities lets you keep an eye on access patterns, spot anomalies, and respond quickly to potential security issues. Proactive and real-time alerts can help you catch and block unwanted activity and can stop data leaks before they happen. 

By logging all of the actions in your system, you can research issues, learn from past behaviour, and improve monitoring management. Logging also sets you up to provide evidence of compliance during regulatory inspections or in response to data subject access requests.

Organisations can use toolsets to enhance compliance with data regulations and ensure data privacy. With tools like Event Monitoring, organisations can monitor security, track application performance, and glean product intelligence insights using event logs. 

It’s important to have solutions that proactively find security threats and respond effectively, respond to audits with ease by storing and querying event data using SOQL, and stay on top of compliance requirements.

5. Provide customer controls for consent and preference management 

Lastly, one of the most critical aspects of a privacy and compliance program is respecting your customers’ wishes for their data use. Complying with data subject requests, practicing data minimisation, and managing consent effectively are key to complying with global privacy laws.

Regulations emphasise individuals’ rights to access, delete, and revoke consent for their data. By quickly addressing these requests, organisations uphold privacy rights and avoid legal risks and fines associated with non-compliance. 

Implementing data minimisation ensures you collect and retain only the essential data, reducing the impact of potential breaches. And effective consent management means getting clear and informed consent before processing personal data, fostering transparency and trust. These practices will strengthen data protection and organisational credibility, showcasing commitment to ethical data handling practices in accordance with evolving privacy laws.

At the final step, consider solutions to help manage consent and data requests, allowing you to handle data privacy efficiently and maintain compliance. For instance, Privacy Centre is a suite of data management tools built to help you manage components of data privacy laws. It allows you to create, monitor, and track requests, automatically fulfilling data subject access and right-to-be-forgotten requests.

Customers can easily update their consent and preferences by hosting forms on your website or in Experience Cloud and updating their consent and preference data to your organisation, next-Gen Marketing Cloud, or Data Cloud. And you can de-identify, delete, or move personal and sensitive data.

With the right tools and practices in place, including de-identification, deletion, or relocation of personal data, you’ll maintain a classified, permission-minimised, and secure data environment, ready to tackle data privacy compliance with confidence.

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Most importantly, generative AI technology can save time on tedious processes, so you can provide better care for your customers and focus on big-picture strategies. Let’s dig into how generative AI can help your business do more, and learn more about large language models.

What are large language models?

Generative AI is powered by large machine learning models that are pre-trained with large amounts of data that get smarter over time. As a result, they can produce new and custom content such as audio, code, images, text, simulations, and video, depending on the data they can access and the prompts used. 

To put things into everyday context, large language models provide answers depending on how a question is phrased. For example, “what are LLMs and how can they help my business?” versus “what are LLMs and what value can they bring to my business?” will yield different results. Although the questions are similar, responses can vary by context.

Because these models use natural language processing and machine learning capabilities, LLMs respond in a human-like, coherent, and relatable way. As a result, they excel in tasks such as text translation, summarisation, and conversations. 

With generative AI helping businesses perform these tasks, trust has to be at the core of your efforts. To make sure you’re using this technology responsibly, you can invest in a customer relationship management platform that has an AI-focused trust layer — which anonymises data to protect customers’ privacy. 

A trust layer built into a generative AI landscape can address data security, privacy, and compliance requirements. But to meet high standards, you must also follow guidelines for responsible innovation to ensure that you’re using customer data in a safe, accurate, and ethical way.

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How do large language models work?

Advancements in computing infrastructure and AI continue to simplify how businesses integrate large language models into their AI landscape. While these models are trained on enormous amounts of public data, you can use prompt templates that require minimal coding to help LLMs deliver the right responses for your customers.

Furthermore, you can now create private LLMs trained on domain-specific datasets that reside in secure cloud environments. When a LLM is trained using industry data, such as for medical or pharmaceutical use, it provides responses that are relevant for that field. This way, the information the customer sees is accurate.   

Private LLMs reduce the risk of data exposure during training and before the models are deployed in production. You can improve prediction accuracy by training a model on noisy data, where random values are added in the dataset to mimic real world data before it’s cleaned. 

It’s also easier to maintain an individual’s data privacy using decentralised data sources that don’t have access to direct customer data. As data security and governance become a top priority, enterprise data platforms that feature a trust layer are becoming more important.

Businesses can also leverage how LLMs work with other kinds of AI. Imagine using traditional AI to predict what customers may plan to do next (based on data from past behaviour and trends), and then using a LLM to translate the prediction results into actions. 

For example, you can use generative AI to build personalised customer emails with offers, create marketing campaigns for a new product, summarise a service case, or write code to trigger actions such as customer recommendations. 

These large language models save time and money by streamlining manual processes, freeing up your employees for more enterprising work. 

Now that you’ve learned what generative AI can do, let’s see how you can use it to help your business. 

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4 ways generative AI can help your business

The sky’s the limit when it comes to ways you can use generative AI for your business. 

LLMs are great at recognising patterns and connecting data on their own. Predictive and traditional AI, on the other hand, can still require lots of human interaction to query data, identify patterns, and test assumptions.

Feeding from customer data in real time, generative AI can instantly translate complex data sets into easy-to-understand insights. This helps you and your employees have a clearer view of your customers, so you can take action based on up-to-date information.

Now let’s dive into some use cases where large language models can help your business.

Using sentiment analysis to gain context into post-purchase actions

Sentiment analysis can help marketing, sales, and service specialists understand the context of customer data for post-purchase actions. For example, you can use LLMs to segment customers based on their data, such as using poor reviews posted on your brand’s website. These insights can help you act immediately on negative feedback. A great marketing strategy would be sending a personalised message offering the customer a special deal for a future purchase. This can help improve brand loyalty, customer trust, retention, and personalisation.

Generating email text for marketing campaigns

Text generation can help marketers reduce the time that they spend preparing campaigns. Generative AI can produce recommendations, launch events, special offers, and customer engagement opportunities for your social media platforms. Then, you can polish up the text to make sure it’s in your company’s voice and tone. For example, you can use the copy produced by generative AI to deliver personalised emails informing customers about a new product launch. This helps to improve personalisation, giving your customers a more consistent experience.

Surfacing related cases for service agents 

Case summarisation can help service agents to quickly learn about customers and their previous interactions with your business. Cases provide customer information such as feedback, purchase history, issues, and resolutions. Generative AI can help with recommending similar customer cases, so an agent can quickly provide a variety of solutions. This results in faster resolutions, time and cost savings, and happier customers. 

Automating basic code generation

Automation helps developers and integration specialists generate code for basic but fundamental tasks. For example, you can use code written by large language models to trigger specific marketing automation tasks, such as sending offers and generating customer message templates. This way, the overall language is consistent, personalised for the customer, and in your company’s voice. Automation can save time and improve productivity, allowing developers to focus on tasks that require more attention and customisation.

When used as part of a hybrid AI strategy, large language models can complement various predictive capabilities and drastically improve productivity. While generative AI can do so much, this technology still needs human guidance to be most effective for businesses. Generative AI can surface the insights you need to make decisions that can move your business forward. 

Think of it like a smart, automated assistant for your company, handling time-consuming tasks so your employees can work on complex problem-solving. When you blend the power of generative AI with the knowledge and expertise your company can provide, you’ll be able to do more for your customers.

Urvi Shah, Staff Technical Writer, contributed to this blog post.

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Marketers often approach problems through an intuitive lens. Engineers, on the other hand, use an analytical, structured approach to make things work. When it comes to AI, engineers have been coding for years. This experience helps them understand how to effectively communicate with AI, including how to structure prompts and refine instructions to achieve desired outcomes. Recognising the benefits of the different mindsets at play and blending the elements can help marketers think about AI, data, and technical challenges in a new way. 

The good news is there’s a new wave of AI that will make marketing problem solving easier. Agentforce is a team of autonomous agents that work side-by-side with your employees to extend your workforce and serve your customers 24/7. These intelligent agents can include anything from answering simple questions to resolving complex issues — even multi-tasking. Agents turn AI from a reactive tool into a proactive assistant that takes the tedious work out of everyday marketing tasks. 

A large language model (LLM) alone relies on vast amounts of data to generate standard responses. Agents, on the other hand, require specific knowledge to address business-specific challenges. Agentforce works by giving teams tools, services, and agents that can tap into the power of LLMs and their connected business data to identify what work needs to be done, build a plan to complete the work, and then execute the plan, autonomously. Let’s take a deeper look. 

What you’ll learn

• Detailed agentic prompts, smarter marketing problem solving
• Creative problem-solving
• Visualising
• Improving and experimentation
• Systems thinking

Detailed agentic prompts, smarter marketing problem solving

Just as a skilled engineer provides precise instructions to achieve the desired outcome, the quality of agent outputs depends heavily on the quality of input. Agents require well-structured information and clear guidance. Instead of simply providing vast amounts of data, marketers should focus on providing relevant information and clear instructions. This involves transforming human-readable information into a format suitable for the agent, ensuring the agent has the necessary context to effectively execute tasks.

The key components of an effective prompt can include:

• Clear and concise objective: Clearly articulate the goal of the task. For example, instead of “Analyse the data,” specify “Analyse the data to identify the top 5 most profitable customer segments.”
• Contextual information: Provide relevant background information, such as data sources, constraints, and any specific requirements. For example, “Analyse customer purchase history from the past year, considering customer demographics and purchase frequency.”
• Desired output format: Specify the desired output format, such as a summary report, a list of recommendations, a data visualisation, or a specific action plan.
• Constraints and limitations: Define any constraints or limitations, such as budget restrictions, time constraints, or ethical considerations. For example, “Ensure all recommendations comply with data privacy regulations.”

Let’s use elements of the “Engineering Habits of Mind,” a taxonomy developed by the UK’s Royal Academy of Engineering, to break down how marketers can better prompt agents to solve everyday marketing problems, and think about AI like an engineer. (Back to the top)

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1. Creative problem solving

Engineers are trained to clearly define the problem before they solve it. Rather than jumping straight into problem-solving, marketers should first ask, “What is the core challenge we’re trying to address?”

With this question at the centre, marketers can think like engineers by simplifying problems into smaller, manageable parts. This might include breaking down customer journeys into stages or jobs to be done, identifying pain points in each stage, and tackling them individually.

Engineering is often described as a team sport, meaning engineers often collaborate with other engineers to solve problems. Marketers can do the same by discussing problems and creative solutions with a cross-functional team of stakeholders. 

Solve with agents: Marketers can use Agentforce as a collaborator when solving problems by asking the agent questions about the underlying data. Marketers don’t need to be able to code or write queries to explore all of the rich data they have access to. Agents use plain, natural language to ask and answer questions. Marketers can start by asking an agent to describe the data, to summarise what’s missing, and to assess any trends in the data. Then, they can begin to explore more by asking follow up questions:

1. What are the poorest performing campaigns for the last quarter? For each campaign, provide specific reasons for its underperformance.
2. What segments saw the largest attrition in the last six months? For each segment, identify potential contributing factors to churn and quantify the impact of churn on revenue for each segment.

These prompts can ultimately help marketers understand what problems to solve. (Back to the top)

2. Visualising

Engineers are skilled at taking an abstract concept and bringing it to life. Many marketers may be adept at this too. They can dream of a campaign with a catchy email promotion, a witty tagline, or an engaging social media post and translate that vision into a concrete plan with measurable objectives. 

However, bringing these visions to life often involves numerous manual tasks and time-consuming processes. Agents simplify this process, helping marketers to more effectively translate their creative visions into successful realities with the right prompts.

Solve with agents: Agentforce helps by making campaigns incredibly easy to create, turning a vision into reality quickly and efficiently. Once marketers understand what problems to solve, they can access the agents ready-to-use skills to build an end-to-end campaign.

Using a AI prompts, marketers can generate campaign briefs, target audence segments, personalised content, and customer journeys based on user-defined goals and guidelines. Here’s how:

1. Campaign Creation: By using natural language prompts to describe campaign goals, the agent will ground that prompt in data from Data Cloud, as well as the company’s brand guidelines to generate a brief, target audience segment, email and SMS content, build a customer journey in Flow, and even provide a campaign summary. 

Let’s consider a marketer looking to build a comprehensive campaign plan for the launch of a new product. The marketer could use the prompt below to communicate to the agent exactly what they are looking for:

“Generate a comprehensive marketing campaign for the launch of our new summer product line, including target audience segmentation, creative briefs for social media, email, and display ads, and a proposed customer journey map. Consider our brand guidelines and budget constraints.”

2. Personalisation Decisioning: Agentforce can help marketers scale 1:1 personalisation by autonomously delivering the right content, products, and offers for each customer based on their profile.

Based on the detailed campaign the agent generated, the marketer then wants to target the identified target audience with a personalised social media messaging. This prompt to the agent could look like:

“Create five unique social media post ideas for our target audience of millennial parents, each with personalised messaging and relevant visuals. Ensure the content aligns with our brand voice and leverages current trends in parenting and family lifestyle.” (Back to the top)

3. Improving and experimentation

Engineers might be known as people who make things work, but they’d say they’re people who make things work better. An engineer’s work is never done; they’re constantly tinkering to build a better mousetrap. 

Marketers can take this to heart by continuously improving their work. Continuous improvement can take the form of testing campaigns using A/B content tests, trying out new webinar topics, delivering ads on a new social network, or cohort analysis on holdout and control groups. Best of all, marketers can use the data-driven insights to prove what works. 

Engineers often release an MVP, or minimally viable version of the product, to test the waters. Marketers can do this too, by launching smaller pilot campaigns before full-scale rollouts. This can save time and resources if things don’t go as planned. Teams can then learn from the outcomes and rapidly improve rather than waiting to launch a “perfect” campaign.

Solve with agents: One of the biggest benefits of assistive agents is freeing up a marketer’s  time by automating repetitive tasks that might involve a lot of manual work. Agents streamline workflows, making it easier to use existing marketing tools and adapt to new ones. For example, Agentforce can help marketers test their campaigns with an agent’s performance optimisation skill. Post-launch, the agent can optimise paid media by autonomously identifying and pausing low-performing ads, recommending optimisations, and adjusting metrics with auto-created goals. Marketers can even write a prompt to ask the agent: 

“Based on all of the campaign data for my company, what is the best way to create an A/B test to improve engagement?” 

Agents can look at all of the customer and campaign data in the data lake, and devise an A/B test to test the hypothesis. Best of all, agents are not just for analysing, but they can take action to execute the A/B tests quickly and efficiently. (Back to the top)

4. Systems thinking

Engineers see projects as interconnected systems. A big challenge marketers face is understanding just how everything is connected. Customers want a smooth experience, no matter how they choose to connect with a business. In fact, customers’ number one frustration is a disconnected experience. Yet, only 31% of marketers are fully satisfied with their ability to unify customer data sources.

Agentforce helps marketers overcome the fragmentation that often exists between departments and systems. Breaking down data silos is arguably the most important aspect of agents, a capability that previous AI tools couldn’t address. Agents serve as a central point of integration, pulling in customer data from various sources and ensuring a consistent brand experience across all touchpoints. Their skillsets allow customers to interact with a brand as one entity, rather than siloed departments lacking holistic data. 

Marketers can think like engineers by recognising the interdependencies in the entire customer lifecycle, like how the whitepaper on the website can influence the sales funnel and the marketing onboarding campaigns. 

Solve with agents: Agentforce can help by following a lead from the initial lead capture through to intent, purchase, and post-sales success. Marketers can ask agents questions to analyse and summarise the lifecycles of all of their customers, to see where channels might drop off or leads fall out of the funnel. These questions could include:

1. What is the typical customer journey across all touchpoints? 
2. What are the bottlenecks or areas where customer engagement significantly declines?
3. Visualise this data and provide insights into potential causes for these drop-offs.

These insights can be used to test or adapt new campaigns.

For example, an agent can autonomously greet visitors and offer to assist them with product or service recommendations, or suggest relevant resources to learn more. This process could include capturing the contact information needed to make the recommendations more tailored, registering the customer for a webinar, providing a relevant gated asset, or scheduling a follow up appointment with a sales rep.

From the information collected, agents can then power intelligent lead nurture journeys by mapping contact information, evaluating leads based on predefined scoring models, routing qualified leads to the appropriate systems and individuals, and orchestrating tailored follow-up sequences. They can also organise flow-driven personalised experiences based on a lead’s behaviour and past interactions with the brand. (Back to the top)

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The mindset shift to thinking like an engineer for marketers can refresh your approach to using AI and Agentforce. Once you reframe your approach, you find yourself back at the beginning, recognising that marketing problem solving will always be important. As a marketer, you can improve your agentic prompts and practice interacting with an agent using consumer tools. You’ll soon start to recognise the difference between effective and poor prompts and how you can improve them with more specifics. Your work as a marketer is never done, but with Agentforce, it only gets easier.  

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Welcome to the dawn of the Agentic AI Era. Starting now, almost any business—from individual contributors to executives—can orchestrate not just human workforces, but digital labour as well. We’ll see trust and accountability as the bedrock for an evolution unfolding in three stages: specialised agents mastering discrete tasks, multi-agent systems collaborating seamlessly, and enterprise-level orchestration rewriting how businesses operate. 

Salesforce’s AI Research’s role is to shape the future of enterprise AI. Here’s our vision for how agentic systems will advance, and what will be needed from humans to help them along the way.

The Evolution of AI Agents: From Rules to Reasoning

The progression of AI agents mirrors the development of machine learning itself. Traditional rule-based systems like Robotic Process Automation (RPAs) were capable of executing precise sequences but stumbled when faced with variations. These early implementations required substantial technical overhead and consulting services, creating a high barrier to entry for many organisations.

The last few decades have witnessed incremental and breakthrough advances that have transformed how machines process information—evolving from rigid automation to more flexible, adaptive, and far more efficient learning systems. Agents built with modern platforms like Agentforce can understand context, adapt to new situations, and handle broad task spectrums. But what’s even more exciting is where we’re headed: self-adaptive agents enabled by multi-agent reasoning—agents that can learn from their environment, improve through experience, and collaborate both with humans and agents from our enterprise customers, partners, vendors, and even the personalised AI assistants of consumers, which are becoming a bigger part of their lives every day.  We are only at the beginning of a three-stage future for Enterprise AI agents. 

Three Stages of Enterprise AI Agents

Just as music evolved from single-note melodies to complex symphonies, AI agents are progressing from solo performers to orchestrated ensembles. Each stage builds upon the last, creating richer, more nuanced interactions in the enterprise environment.

Stage 1: “Monophonic” AI – The Specialised Contributor

In the first stage of agentic evolution, specialised agents excel at defined tasks within particular industries, bringing unprecedented efficiency and accuracy to routine but crucial business operations. They represent the foundation of enterprise AI adoption, handling discrete tasks with a level of consistency and speed that transforms departmental workflows. They also are masterful at providing the benefits of AI’s advancements to date, like predictive next best actions and product recommendations, highly personalised to each customers’ preferences and behaviours. And generative guidance, marketing language and correspondence of the highest caliber, for customers, service and sales reps—humans and bots alike. 

In commerce, for example, they revolutionise inventory and account management. Indeed, agents don’t just handle basic inventory checks; they proactively monitor stock levels across multiple locations, predict seasonal demands, and generate real-time account summaries that flag unusual patterns or opportunities. Tasks that once required hours of human analysis can now be completed in seconds, with greater accuracy and depth, yielding optimised, personalised, and almost “magical” experiences for the retail customer. 

Service operations see similar transformations. Beyond basic billing summarisation, these agents analyse customer interaction patterns, automatically categorise, and prioritise service requests, and generate predictive insights about customer needs. They spot trends in customer behaviour that might indicate satisfaction issues or expansion opportunities, providing service teams with actionable intelligence rather than raw data. The result is customer service that feels effortless, ambient and almost invisible to the end customer – their issue is now often resolved before they even knew there was one. 

In financial services, agents redefine customer service efficiency. When processing dispute acknowledgments, they analyse transaction histories, identify patterns of potentially fraudulent activity, and automatically trigger relevant security protocols. For financial planning, they generate comprehensive analyses by correlating market data, individual client histories, and broad economic indicators. When used correctly, these agents will afford businesses unprecedented back-office efficiency, and will inform next-generation retail banking, investment guidance and wealth management experiences for consumers.

Stage 2: “Polyphonic” AI – The Seamless Collaborators

This stage introduces orchestrated collaboration between specialised agents within the same company, collaborating together toward a common business goal. In this case, an “orchestrator agent” coordinates multiple specialists working in concert, similar to how a restaurant’s general manager orchestrates talented hosts, servers, managers, chefs, prep cooks, and expediters to work together to earn that coveted Michelin star.

What does polyphonic AI look like for a complex business operation? Consider a customer service scenario where multiple agents work invisibly together to support a loyal retail customer’s request ticket to exchange sizes of an off-season SKU. 

• A front-line service agent processes the initial customer enquiry
• An inventory specialist checks product availability across locations
• A logistics agent calculates shipping options and timelines
• A billing expert reviews account history and payment options, and most importantly:

The orchestrator agent coordinates all these inputs into a coherent, effective, on-brand and contextually relevant response for the human at the helm to review, refine, and share with the customer.

When implemented well, this multi-agent approach, with an “orchestrator agent” serving its “orchestrator human” delivers powerful AI-driven advantages: The system achieves enhanced reliability by leveraging specialised, trusted agents focused on specific domains, while reducing hallucinations since each agent operates within a narrower scope. This distributed approach also strengthens security by isolating sensitive data handling to specific agents. Perhaps most importantly, the ecosystem offers seamless scalability—organisations can continuously add new specialised agents to expand capabilities as needs evolve.

Stage 3: “Ensemble” AI – The Enterprise Orchestrators

The final stage—the ideal stage—adds sophisticated agent-to-agent (A2A) interactions across organisational boundaries, creating entirely new patterns of business relationships. Beyond traditional B2B and B2C models, we see the emergence of B2A (business-to-agent) and even B2A2C interactions where AI agents serve as intermediaries for work and transactions.

Consider a simple car rental scenario: A customer’s personal AI agent negotiates with a rental company’s business AI agents. The customer’s agent optimises for the best price and value, while the rental company’s agent aims to maximise revenue through add-on services. But the business agent must balance aggressive sales tactics against the risk of losing the deal to competitors. These interactions can be governed by sophisticated “game theory” principles, requiring advanced negotiation skills and protocols, risk management under uncertainty, verification mechanisms to ensure trust along the way, not to mention the ability to deftly resolve conflict.  

Now, imagine this scaling to ever-more complex enterprise processes we see across industries: from supply chain optimisation to customer experience orchestration. Whether you’re a consumer or enterprise employee, ensemble AI will mean that you’ll have an assistant to perform complex orchestration and meaningful collaboration per your personalised needs and wishes. And in order to achieve this, we as humans have some work ahead of us. 

Non-Negotiable Imperatives: Trust and Accountability

As we deploy increasingly sophisticated agent systems, two fundamental principles must guide every decision: trust and accountability. 

Building Trust

Trust in the era of agentic AI extends far beyond technical safeguards against toxicity, bias, and hallucinations. Recent Salesforce research shows 61% of customers believe AI developments make trustworthiness more critical than ever‌—and they’re right. We’re entering territory that demands deep organisational confidence in the symbiotic relationship between humans and AI.

This confidence builds on four essential foundations.

First is the bedrock of accuracy and boundaries‌ — ‌AI agents must operate within well-defined parameters while maintaining precision. Beyond preventing errors, these guardrails will create predictable, trusted partnerships that amplify collective intelligence.

Just as crucial is an agent’s self-awareness. Like any valued colleague, AI agents must acknowledge their limitations and know when to engage human expertise. This requires sophisticated handoff protocols that ensure seamless collaboration between artificial and human intelligence. For example, our AI Research team explores training methods to teach AI agents to flag areas of uncertainty and seek assistance when confronted with unrecognised challenges. Trained correctly, AI will know when not to attempt a guess but rather to come to a human and ask for help. 

For multi-agent systems, we will also need engagement protocols that are globally accepted and adopted. Think of it like this: Banks have global protocols, or rules to systemise the transfer of funds between individuals, businesses and countries. Traffic has protocols to ensure adherence to rules, governed by our universal traffic light colour system. The Internet has “IP” – our global Internet Protocol that allows for routing and addressing of packets of data to travel across networks and arrive at the correct destination.

So too will agents of the future need these protocols that are agreed upon and implemented universally, so that orchestrator agents can communicate, negotiate and collaborate with other business’s agents safely, ethically and for mutual benefits of both parties. This “ensemble” level of engagement will need to be fast, efficient and fair. Without such protocols in place, we’re at risk of agent-to-agent “spam” at best, and fraud and other dangers at worst. 

Finally, as our AI agent workforce grows, so must our security measures. As with any technology, humans with malicious intentions can also wield AI, designing and training “AI Worms” for the purposes of data breaches or to attempt to hijack other AI agents to disclose private customer data. Enhanced protection, privacy controls, and continuous monitoring mustn’t be seen as mere technical requirements—they’re essential to maintaining the trust that transforms AI from a tool we use into a partner our businesses will grow with.

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Ensuring Accountability

As organisations deploy AI agents that make thousands of decisions per second, we must establish clear frameworks for responsibility and oversight to ensure we have a plan for if and when things go wrong. This requires a comprehensive approach. Below is a starting point for C-Suite teams overseeing an agent implementation effort.

• Clear chains of responsibility for agent decisions. When an AI agent makes a consequential decision, there should be no ambiguity about who’s accountable. This may even mean establishing new roles like “AI Operations Officers” who have both the authority to oversee agent deployments and the responsibility when issues arise.
• Robust systems for detecting and correcting incomplete information, biases, hallucinations, or toxic outputs—before they impact your business. This goes beyond basic safety checks to include continuous monitoring of agent decisions, real-time intervention capabilities, and systematic audit trails. Just one example of this is our research team’s recent advancements in retrieval-augmented generation (RAG), dramatically improving how our AI systems access and verify information. These innovations enable rapid evaluation and course-correction—ensuring that AI systems deliver accurate, reliable results that humans and businesses can trust.
• Defined processes for human oversight and intervention that balance autonomy with control. We need to move past the simple notion of “human in the loop” to develop sophisticated frameworks for when and how humans should intervene in agent decisions. This means creating guidelines and org-wide standard ways of communicating with agents as well as clear escalation pathways that maximise agent autonomy for routine tasks while keeping human judgment central to high-stakes decisions.
• Structured approaches for making things right when mistakes occur. This includes not just technical rollback procedures, but also clear protocols for customer communication, remediation, and systematic improvements to prevent similar issues.
• New legal and compliance frameworks that explicitly address AI agent accountability. The current regulatory landscape wasn’t designed for autonomous AI agents making business decisions. We need to work proactively with regulators to develop appropriate governance structures. proactively with regulators to develop appropriate governance structures.

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Looking Ahead: The Scientific Method Meets Enterprise Innovation

The path to deploying truly interactive AI systems demands executive foresight: we must apply the same stringent scientific standards that produced these advances to their real-world implementation. Success won’t be just decided by the number of AI agents deployed or implementation speed, but by how thoughtfully enterprise leaders and technologists orchestrate their integration with existing workforce protocols, processes, and preferences.

As we advance our understanding of agent collaboration, shared learning, and human-AI interaction, we’re discovering principles backed by reproducible research and empirical evidence. Drawing on Salesforce’s decades of enterprise CRM success and expertise in business logic and optimisation, we’ve infused Agentforce with deployment strategies that ensure our systems are not just powerful, but trustworthy and accountable in meeting the needs of our customers’ business—and the humans that run them.

The future isn’t about humans versus AI – it’s about humans with AI working in concert, each using their unique strengths. Agents will become—and with the launch of Agentforce, indeed already are— a true workforce multiplier, enabling teams to tackle previously impossible tasks. The time to begin this transformation is now, and the scientific method will light our way forward: through careful hypothesis testing, meticulous measurement, and continuous refinement based on evidence. Just as every breakthrough experiment begins with a hypothesis, every successful AI transformation begins with a vision—and ends with validated truth.

Resources

• Salesforce AI Research website
• Follow us on X: @SFResearch

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Artificial intelligence (AI) is transforming the job market at lightning speed. AI has the potential to create new job opportunities in Southeast Asia. And these new jobs aren’t just plentiful. They’re rewarding. In Vietnam, for instance, AI engineers are among the top earners in Vietnam’s IT industry with salaries of US$1,110-2,060 a month, excluding bonuses.

AI won’t replace you, but someone skilled in working with AI could gain a competitive edge. That’s why developing the right skills to collaborate with AI is more critical than ever. To help you stay ahead of the curve, we’ve curated this list of the top 10 human-AI collaboration skills you need to thrive in the workplace.

These skills will help you develop effective human-AI partnerships. By understanding AI, its role in the workforce, and the skills needed for success, you can prepare for the days and years ahead.

The benefits of human-AI collaboration in the workplace

But before we explore the skills of the future, it’s important to understand why the ability to work effectively with AI matters so much.

AI can make work faster and more efficient in areas like sales, service, marketing, inventory management, and factory operations.

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The future of work is changing, but it doesn’t mean humans are fully replaceable. George Hanson, the Chief Digital Officer at Mattress Firm, put it this way during an interview on Experts of Experience, “The value I see in AI is as an aid to humans, as opposed to replacement of humans.”

AI is revolutionising the way we work by enhancing efficiency, increasing productivity, and uncovering insights that might otherwise go unnoticed. However, behind every AI success story lies human effort and ingenuity. While AI tools can augment ‌ — ‌ and sometimes even replace ‌ — ‌ certain tasks, the real magic happens with human guidance. It’s people who train these systems, collaborate with them, interpret their outputs, and ultimately make the final decisions. At its core, workplaces still rely on human judgement, creativity, and the unique perspectives only we can bring.

What this means for you is there’s a massive opportunity to stand out, enhance your skills, and drive exceptional results. By fostering human-AI collaboration, you can combine your unique strengths with the capabilities of the technology to achieve remarkable outcomes. 

Top 10 collaborative skills for human-AI teams 

Below are the most important technical, analytical, and soft skills needed for the jobs of the future. Keep ahead in the changing job landscape by adopting these skills. You’ll also be able to boost your team’s productivity and elevate your career to new heights.

Skill 1: Understanding Generative AI

Develop a foundational grasp of generative AI—how it works, its capabilities, and its limitations. You don’t need to be a programmer, but knowing the basics is essential for effective collaboration. Focus on these key areas:

• Large Language Models (LLMs): These models, like ChatGPT, excel at creating text, summarising content, drafting emails, and answering questions.
• Machine learning systems: Perfect for analysing large datasets, making predictions, or finding patterns, like forecasting customer demand or personalising recommendations.
• Limitations and Ethical Use: Understand the boundaries of generative AI, including biases, data privacy concerns, and when human oversight is critical.

Skill 2: Prompt engineering

Think of prompt engineering as having a conversation with the AI‌. ‌It’s all about asking the right questions in the right way. AI systems work best when given clear instructions. Plus, you’ll get trusted results with accurate, relevant prompts grounded in your own data. Learn how to define exactly what you need, provide context, set boundaries, or use examples. Only then can you ensure the AI delivers useful and accurate outputs for your tasks. 

Skill 3: Familiarity with AI tools and platforms

Competence with popular AI platforms and tools is important. Staying up-to-date with new AI technologies helps you adapt quickly and improve your use of AI systems. Keep a close eye on AI developments in your industry. Subscribe to relevant publications, attend conferences, and follow thought leaders in the AI space.

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Skill 4: Judging the credibility of an answer

AI-generated insights can’t always be trusted at face value. It can sometimes generate information that sounds convincing, but isn’t entirely accurate. That’s why important to learn how to assess their relevance and accuracy and identify potential biases. Think of yourself as a fact-checker for your AI assistant. It’s crucial to evaluate its responses critically, verify facts, and stay aware of possible inaccuracies. By taking an active role in validating AI-generated insights, you can make more confident, informed decisions.

Skill 5: Knowing what problem to solve 

AI isn’t the answer to every challenge‌ — ‌you must know when to use it. AI shines with repetitive, time-consuming, or data-heavy tasks, helping you work faster and smarter. But for creative thinking or complex decisions, people are still the experts. When you understand what AI is good at (and what it’s not), you can let it take care of the busy work, giving your team more time to focus on higher-level work.

Skill 6: Data literacy

Being data-literate simply means understanding the basics of how data is structured and how it’s used. AI systems rely heavily on data for training, analysis, and decision-making, so understanding how data works is crucial to use and interpret AI outputs. You should be familiar with:

• Different types of data: Learn the difference between structured and unstructured data. Think neatly organised rows in a spreadsheet versus a collection of customer reviews or social media posts.
• Data-gathering methods: From surveys to web scraping, knowing how data is collected helps you gauge its reliability and relevance.
• Data interpretation techniques: Spotting trends, comparing metrics, and asking questions like, “What story is this data trying to tell?”

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Skill 7: Adaptation and flexibility

AI is always evolving, so you need to be flexible and continuously upskill yourself to keep pace with technological advancements. Developing a growth mindset and embracing lifelong learning is key. 

Invest in your own learning by following thought leaders, keeping up on trends, and exploring training programmes that help you develop the skills needed to work effectively with AI. Platforms like Trailhead offer a fun and engaging way to upskill, with an extensive library of AI-powered learning tools to get you started.

Skill 8: AI translation

Insights are useless if they can’t be shared. A critical skill is distilling AI-driven information into clear, actionable takeaways that customers and partners can easily understand. Mastering the art of translating AI outputs will significantly boost your value and ability to help your organisation embrace AI.

Skill 9: Curiosity and experimentation

While AI excels at automation and supporting roles, uniquely human skills like creativity remain indispensable. Cultivate a mindset of curiosity‌ — ‌questioning assumptions, exploring why things are done a certain way, considering whether systems can be improved, and thinking creatively about implementing new approaches. With AI handling many of your routine tasks, use the freed-up time to experiment, innovate, and try new ideas. 

Skill 10: Ethical judgment 

AI systems are built by humans and are influenced by the biases and moral perspectives of their creators. This means you have to rely on your own ethical judgement to determine whether something is appropriate or responsible to move forward with. As AI becomes widespread, understanding its ethical implications is essential. You’ll need to make real-time decisions about when, where, and how to use it responsibly. 

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A final thought on AI education

The future of work isn’t about humans versus machines. It’s about reskilling and learning how to work with AI to do incredible things together. By building these 10 essential skills, you’re not just preparing for what’s ahead, you’re setting yourself up to thrive in a world full of new possibilities.

Think of AI as your sidekick, not your replacement. Human-AI collaboration is here to make your job easier, free up your time, and help you focus on what you do best. With a curious mindset, a willingness to learn, and a focus on using AI responsibly, you can unlock opportunities that take your career to the next level.

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As customers expect faster, more personal, and helpful service, businesses must invest in AI education to empower their teams with the skills and knowledge to harness this technology effectively. Upskilling teams in both CX strategies and AI tools is key to staying competitive and meeting these new demands. 

Why now is the time for AI in customer service

AI isn’t just a trend — it’s changing the way companies help customers. It can help teams solve problems faster, make better recommendations, and give customers what they need before they have to ask. By implementing AI into your customer experience strategy, you can future-proof your team and help your organisation stay competitive.

How AI is already changing customer success

• Personalised service: New insights into customer data can help predict what each person needs and suggest products or solutions that fit them best.
• 24/7 help: AI agents can answer questions any time of day, freeing up your team to work on the more complex interactions.
• Improved customer feedback: Analysis of real-time customer feedback helps teams know what’s working and what’s not, making it easier to improve experiences as they happen.
• Easier order tracking and returns: Customers can track their orders and return items seamlessly and without human involvement.
• Better pricing: Dynamic pricing can help meet the needs of each customer, giving them tailored deals while accounting for market changes.
• Fast problem solving: Companies can identify and fix bugs, mistakes, or problems before customers notice them. 
• VIP service for loyal customers: AI can identify top customers and make sure they get VIP treatment and professional service.
• Smarter interactions: Companies can gauge the opinions of an individual customer using individual sentiment analysis. This enables companies to better understand customers’ emotions, leading to more thoughtful and timely responses and better customer service.

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AI education for exceptional CX

Getting started with AI means reskilling your team to feel confident in using it. This process takes time, but it’s worth the effort. And as your team’s AI literacy grows, they’ll start identifying new ways to use the technology. 

Here’s how to build an effective training program for AI skills and encourage employee engagement.

Assess current skills

Identify knowledge gaps, skill gaps, and areas for improvement. Make sure you address core AI literacy and technical skills as well as soft skills that support success with AI tools.

AI literacy and technical skills:

• Foundational AI knowledge: Help your team understand the basics of AI, like machine learning, natural language processing (NLP), and predictive analytics.
• Data analysis: Train your team, as they become more familiar with AI, to understand AI insights and use data to make smart decisions.
• AI tool proficiency: Offer hands-on training with AI-powered CRM systems, chatbots, and other tools they’ll use in their daily routine.
• AI-driven customer journey mapping: Show them how to use AI to map customer journeys and improve personalisation for better customer experiences.
• Customer data privacy and security: Ensure your team is equipped to handle customer data securely and responsibly while benefiting from AI insights.

Soft skills: 

• Critical thinking: Encourage critical thinking, so your team can evaluate AI outputs and make informed decisions when needed.
• Adaptability: Create a culture that embraces learning and tech adoption to keep pace with evolving AI.
• Emotional intelligence: Stress the importance of empathy and the human touch in customer interactions, as these skills complement AI capabilities. It’s all about balance. 
• Collaboration, not replacement: Train your team on when to rely on automation and when a human touch makes all the difference.

By empowering your team with these skills, you’re preparing them for the future of AI. They’ll be confident, skilled, and ready to deliver awesome customer experiences every step of the way.

Develop tailored training

Create personalised learning paths to address the range of AI skills required for your organisation and where team members currently stand. This will help your team focus on what’s relevant to them, speed up improvements, and secure buy-in as team members see themselves in your approach to AI adoption. Hold regular check-ins to assess progress and adjust what topics and resources come next.

AI education in action  

Give your team the opportunity to practice working with AI systems in controlled environments. Mix workshops, online courses, and real-world applications to cement their knowledge in different contexts. This is an ongoing effort, giving your team direct experience as new tools, features, and methods prove to deliver more value. Free hands-on AI courses and certifications are helpful resources your teams can easily access to broaden their exposure to AI.

Encourage collaboration

Share knowledge across teams and departments. A collaborative AI approach ensures that your team adopts best practices company-wide. Encourage team members to proactively share their insights and experiences using new AI tools and skills. This way, different people can try out various options and keep everyone aligned and informed on what’s working and what’s not. 

Reward participation

Recognise employees who embrace AI in their roles. When team members see the impact of AI on their success, engagement and competence skyrockets. Because AI is rapidly changing, it’s useful to constantly explore new ways of measuring progress and how data is understood. Participation gives your team a stake in your organisation’s success with AI.

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Continuous learning: The heart of AI success

Getting the most out of AI is all about having a plan for continuous learning. With technology evolving so quickly, what works today might need an upgrade tomorrow. That’s why keeping your CX team in learning mode is essential for staying competitive and delivering great experiences every day. AI isn’t a ‘set it and forget it’ tool. It’s a journey where your team can grow, adapt, and improve.

Encourage your team to stay curious. By exploring new AI tools and techniques, they’ll keep their skills sharp and be ready to jump on the latest innovations as they appear. Regularly refreshing your training programs to reflect these advancements is key. Offer a mix of structured learning, like workshops and online courses, along with informal opportunities for peer learning and collaboration. A culture that champions learning helps your team adapt to new AI-driven solutions faster, so they’re always ready to deliver agile, responsive customer experiences.

And don’t forget to support your team along the way. Whether it’s access to the latest AI tools, time for ongoing training, or the freedom to experiment, weaving continuous learning into your organisation’s AI strategy sets your team up for success. Investing in their growth means keeping them at the forefront of innovation, and equipped to meet your customers’ needs with confidence and expertise.

The future of CX with AI

AI is here to stay, and when used well, it can transform the way you deliver customer experiences. By investing in your team’s AI skills, you’re empowering them to serve customers more effectively, adapt to change with confidence, and lead in a dynamic world.

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AI agent myth #1: They’re just glorified chatbots

Chatbots and agents are fundamentally different in terms of complexity and functionality. Bots are about retrieving data and answering questions. Agents are about taking action. 

Bots use predefined rules and scripted responses to answer questions, and they do not deviate from them. For example, they’re widely used in customer support for frequently asked questions like “where’s my order?” or “what is your return policy?” 

That rigidity limits their usefulness. Bots don’t understand more complex contexts and can’t be creative in their problem-solving. Unlike more advanced AI tools, they’re not self-learning. This means every time a change is made to, say, company policy, it must be made manually within the AI. Bots do not get smarter over time. They’re programmed to retrieve data and respond to routine, predictable questions. They excel at this, but that’s as far as they go. 

Agents go far beyond simple Q&A. Fully autonomous agents can perform complex, multi step tasks without direct human intervention, while semi-autonomous agents involve a “human in the loop” to trigger certain types of requests. Unlike chatbots, agents can process vast amounts of data, make decisions, and learn from their environment, allowing them to manage workflows, optimize processes, and make strategic recommendations. They often incorporate more advanced AI techniques, such as reinforcement learning and decision-making algorithms, which help them act proactively and adapt to changing conditions.

It’s the difference between, say, a bot that simply analyzes your sales data, and an agent that analyzes data and uses it to adjust inventory levels, update marketing strategies, and communicate with suppliers. 

AI agent myth #2: They’re unpredictable and uncontrollable

Autonomous agents may conjure memories of films like “2001: A Space Odyssey” and “The Terminator,” where AI systems go rogue, with dire consequences. But, in fact, the most effective agents today use sophisticated tools and techniques to guard against errors and hallucinations, and have safety and trust at their core. 

Central to this is a reasoning engine that generates an action plan based on what a user is trying to do. It evaluates and refines the plan, extracting data from customer relationship management (CRM) and other systems. It decides which business process to use based on the request, and repeats the process until it gets it right, getting smarter every time.  

If a requested task seems outside the guardrails set by an organization (including user permissions), the reasoning engine acts as a check, automatically pulling in a human for oversight. 

“Helping an agent perform accurately and understand what it is not allowed to do is a complex task,” said Krishna Gandikota, manager of solution engineering at Salesforce. “But a reasoning engine helps the AI plan and evaluate its approach before it takes an action. It will also determine whether it has the right skills and information to take the action.” 

This decision-making process, Gandikota said, is enhanced by the agent’s ability to continuously learn from its interactions and experiences to refine and improve its responses over time.

The most effective AI agents are those that are contextually aware and grounded in the most relevant data. There are a few ways to do this. One is a technique called retrieval augmented generation (RAG), which finds the best information to use, then creates new responses based on it. Another is through context-aware search, called semantic search, which surfaces the most recent and relevant data required for a task. 

Agentforce uses Data Cloud, which has these techniques built-in. For even more accurate results, Data Cloud uses zero copy technology, which allows AI agents to access data ingested from diverse data sources in real time, without having to actually move, copy or modify it.  

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AI agent myth #3: They’re complicated, time consuming and expensive to set up

You might think technologies as impactful as agents would require months of complex development and integration, and millions of dollars. But agents powered by generative AI and large language models (LLMs) can be set up in minutes with prebuilt topics, which are the areas of interest the agent is designed to handle, and actions, which are tasks the AI agent performs. 

There are already a handful of out-of-the-box agents for customer service, commerce, sales coaching, and more. But there are also low-code options to quickly build customisable agents. By using natural language processing (NLP), if you can describe it, you can build a custom agent. 

Tools like Agent Builder even auto-suggest guardrails to help an agent do its job safely. Using the NLP description of the job you want the agent to do, Agent Builder finds semantically similar resources within your app’s metadata. This gives it an awareness of how your business works, and auto-suggests knowledge and actions to best complete the job.  

“All the sophistication is already there in the platform,” said Gandikota. “The Einstein Trust layer, the reasoning engine, the vector database (for RAG and semantic search) are all automatically engaged. You can build an army of agents with a platform that brings it all together in the most trusted and open way.”  

AI agent myth #4: They’re always fully autonomous

Agents don’t always have to be 100% autonomous. Their level of autonomy varies depending on their purpose and the complexity of their tasks. However, agents are most effective when they’re paired with humans to drive customer success and positive business outcomes. 

In a semi-autonomous situation, agents support workers in decision-making and carrying out tasks, and usually require intervention to approve decisions. For example, an agent in  financial services would analyse a client’s portfolio and make suggestions to the portfolio manager about how to optimize it, without actually taking those actions itself. 

With supervised autonomy, agents autonomously complete tasks but are constantly monitored by humans. This is especially important in safety-first and regulated industries like healthcare, insurance, transportation, and pharmaceuticals. 

Fully autonomous agents execute tasks without any human intervention. They retrieve data, analyse, make decisions, adapt, and take actions on their own. Even these agents, though, operate within the predefined guardrails designed by humans. 

“Agents don’t always have to be fully automated in taking action, but they do understand requests, and reason as to whether they can take the action on their own, and request human intervention when needed,” said Gandikota. 

AI agent myth #5: They won’t deliver real business value

Many organizations using GPT-based AI for generic, all-purpose tasks aren’t seeing the productivity gains or business value they expected. But agentic AI is very different. Whether it’s nurturing sales leads, brainstorming campaign ideas, or deflecting service calls, purpose-build agents are focused on one specific job, and doing it exceedingly well. 

The best part is they take action on your behalf. These targeted AI agents, designed to solve a specific problem, show infinitely more promise than generic AI that’s not attuned to your business needs. That’s why 82% of large companies plan to implement agents by 2027. 

Some companies aren’t sitting on the sidelines. Wiley, the educational publisher, has resolved over  40% more support cases since implementing an AI agent, outperforming its old chatbot. The company said agents help manage routine responsibilities, which frees up its service teams for more complex cases. Other early adopters, including OpenTable and ADP, are seeing even greater case resolution. 

According to research firm MarketsandMarkets, “AI agent adoption’s crucial determinant is the increasing demand for automation that enhances efficiency, scale, and decision-making. Agents offer an effective alternative through automating repetitive functions, analyzing big datasets, and providing real-time actionable insights.”

The market for agents, the firm predicts, will soar from $5.1 billion this year to $47 billion by 2030. 

It’s crucial for business leaders to separate fact from fiction. Misunderstanding autonomous AI agents can lead to missed opportunities or, worse, costly mistakes. With a clear understanding of agent’s capabilities and limitations, you’ll be better positioned to work more efficiently, and make smarter, more informed decisions.

Read more:

• What’s next for ASEAN businesses in 2025?
• What is Data Transformation and How Can it Help ASEAN Unlock AI’s Potential?
• The Sales AI Playbook: 7 Ways AI Can Help You Close More Deals

The post 5 Myths About AI Agents appeared first on Salesforce.

Digital transformation is all about taking processes that used to be done on paper and making them digital. Companies are jumping on board with this change to refresh how they do things. 

By going digital, companies can become more efficient to respond to customer’s needs and wants. Digital transformation also has the power to reshape business models, and company culture, and drive innovation and fundamental changes beyond just technology and deliver more value to customers.

Read the 3rd edition State of IT Report with insights and trends from over 4,000 IT leaders worldwide.

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What are the benefits of digital transformation?

Digitalisation is the automated process of using digitised information to simplify and improve established ways of working. It isn’t about changing how you do business or creating new types of businesses.

It’s about working faster and better now that your data is instantly accessible and not trapped in a file cabinet. Measuring the return on investment and maximising business value through ongoing evaluation of digital transformation strategies is crucial to ensure the success of these efforts.

1. Adds value to every customer interaction

Digital transformation is changing how business gets done and, in some cases, creating entirely new classes of businesses. Companies are revisiting everything they do. From internal systems to customer interactions, both online and in person. They’re asking important questions like:

“Can we change our processes to enable better decision-making, greater efficiencies, or a more personalised customer experience?”

In the digital age, businesses are finding innovative ways to leverage technology. A prime example is Netflix.

Originally a mail-order service, Netflix disrupted the brick-and-mortar video rental industry. Digital innovations enabled wide-scale streaming, allowing Netflix to compete with traditional broadcast and cable networks by offering a growing library of on-demand content at competitive prices. This effectively created a new industry of on-demand videos and TV shows.

That’s the true power of digital transformation: it can completely change the landscape, disrupt competition, and create new businesses and demand, ultimately adding greater value to every customer interaction.

2. Simplifies business processes and improves efficiency

The process of using digitised information to simplify and enhance established ways of working is called digitalisation. Note the word established. Digitalisation isn’t about changing how you do business or creating new types of businesses. It’s about improving efficiency now that your data is instantly accessible instead of trapped in a dusty file cabinet.

Consider customer service, whether in retail, field operations, or call centres. Digitalisation transformed service by making customer records easily retrievable via computer. 

While the fundamental methodology of customer service remained the same, the process of handling inquiries became much more efficient when searching through paper ledgers, which was replaced by entering a few keystrokes on a computer or mobile device.

As digital technology evolved, people began to explore new ways to use technology in business — not just to perform old tasks faster. This is when the idea of digital transformation began to take shape. Suddenly, with new technologies, new ways of doing things became possible.

3. Makes data more accessible and easier to share

Not so long ago, businesses kept records on paper, whether handwritten in ledgers or typed into documents. If you wanted to gather or share information, you dealt with physical documents and legacy systems like — papers, binders, Xeroxes, and faxes.

Then computers became mainstream, and most businesses converted those analog records into digital files. This process is called digitisation: converting information from analog to digital.

Finding and sharing information became much easier with digitisation, but businesses often used their new digital records to mimic old analog methods. 

Computer operating systems were even designed around icons of file folders to feel familiar to new users. While digital data was exponentially more efficient than analog, business systems and processes remained largely rooted in analog-era ideas about managing information.

4. Enhances customer service experiences by providing more connection options for customers

Digital transformations have reshaped how companies approach customer service. The old model involved waiting for customers to find you, whether in person or by calling an 800 number. The rise of social media has changed this dynamic, allowing progressive companies to meet customers on their preferred platforms.

While improving call centres and in-store service desks with digital technology is beneficial, true transformation occurs when businesses leverage all available technologies to enhance customer experiences. Social media has become an additional channel for better customer service, exemplifying this digital transformation.

Digital transformation encourages businesses to rethink traditional concepts of teams and departments. This doesn’t mean service reps must run marketing campaigns, but it can involve breaking down barriers between departments. By integrating service and marketing on social media, businesses can capture customer information, create personalised journeys, and efficiently route queries to service agents.

How to digitally transform your business?

A successful digital transformation is a complete business transformation. It’s crucial to keep this in mind if you’re seriously considering transforming your business. It’s not just about updating IT systems and apps. It’s a cultural shift and a way to reimagine all of your company’s processes and create better ways of doing things.
Small businesses can leverage a digital transformation mindset to build digital-first into their company culture.

Step 1: Assess your business data and processes

Remember that just as digital transformations are about business first and digital second. Problems with your business data may be signals to look more closely at how your company is doing business generally.

Laurie McCabe, Co-Founder and Partner at SMB Group, said it well: “In fact, it’s usually situations like these that make you realise you don’t have great visibility into your own business data or, even worse, have lost touch with what your customers want and need.”

Step 2: Identify gaps, problems, and areas for improvement

Start by conducting an internal assessment to pinpoint gaps, problems, and areas of difficulty. Here are some questions to consider:

• What’s your biggest problem? 
• What’s the key to your survival? 
• What technology changes are my competitors making?
• What could our business do better?

For very small or new businesses, the answers may be simple, such as needing more customers or implementing basic systems. Involve everyone in your company from the start, as your digital transformation will impact them all over time.

Even if the current path seems clear, remember that you’re building for the future. Your business will grow, whether that means more employees, increased revenue, or both. Flexibility should be part of your strategy so you can adapt as your business evolves.

Connecting with a Salesforce MVP online or in person can be an excellent way to help you create a small business digital transformation strategy.

Step 3: Get key stakeholders involved in the process

When leading a digital transformation, remember to be collaborative. If you have ten employees, all ten will be affected by the changes. Involve them early and seek their input. 

This will not only ensure better buy-in but also lead to stronger outcomes. Digital transformation impacts daily workflows and is designed to empower employees. So why not make them a part of the process from the beginning?
That’s why implementing change management is critical, as it helps key stakeholders adapt and adjust to the new workflows and transformation initiatives smoothly.

Step 4: Choose the right technology tools

Technology integration is key. It’s perhaps the number one area SMBs should be investing in. 

One of the biggest, easiest-to-make mistakes businesses make is investing in many different technologies that don’t integrate. 

SMBs need to stay focused on getting the capabilities they need now in a way that will scale as their businesses grow. Today’s business ecosystems and platforms make it easy for vendors and developers to build apps tailored to help SMBs grow. 

Adopting a scalable platform like Salesforce will help ensure that the processes and information in your company can flow as easily as possible. That’s the foundation upon which everything else can be built.

Here are some tools to consider that integrate well and can help digitally transform your business:

• Salesforce CRM: Manage customer relationships effortlessly. 
• Salesforce Marketing Cloud: Automate marketing and personalise messages.
• Salesforce Service Cloud: Simplify support requests for better customer service.
• Slack: Enhance team communication and collaboration.

Test the technology with a small group first to ensure it meets your needs, and get your team’s thoughts and conduct a needs-met analysis to find out if your team’s needs were met or exceeded.

Step 5: Build bridges to connect your data, employees, and customers

You don’t need to scrap everything and start over when beginning a digital transformation, even if you’re transitioning from a snarl of apps that don’t talk to each other. 

In fact, the most effective solution is to bridge data silos and pull all information into a central space — rather than completely starting over.

The second part of the process is to unify your data, with the aim of creating a single, unified view of the customer. Once you’ve built bridges between fragmented information, you’ll be able to surface useful insights into customer behaviour and maximise the potential of new AI technologies like AI agents, generative AI, and robotic process automation (RBA).

Step 6: Consider outside help in mapping out a digital transformation strategy

Working with consultants, partners, and tech vendors can be great for SMBs because they have the depth of experience and knowledge to help you figure out the best paths to success. 

Experienced partners have likely helped other companies in similar situations and so can help you find the most direct paths to meaningful transformation. A great place to look for consulting partners is the consultant’s directory on the Salesforce AppExchange.

Many small business leaders hear the word “consultant” and instinctively flinch while reaching a hand to guard their wallets. Don’t assume that getting help is always too expensive — that’s simply not true.

Many large companies offer free advice or trainings for SMBs, like Salesforce Trailhead. Beyond free offerings, there are all sorts of ways to get advice without spending a lot.

Why are businesses going through digital transformations?

As digital technology advances and plays a larger role in our daily lives, businesses must keep up. The fundamental principle is simple: keep up, remain competitive with your digital transformation efforts, or fall behind.

Any change in business starts with customers. Customer satisfaction is key to success. Modern customer expectations are driven by digital technology innovations. The always-connected customer constantly sees new possibilities. When they discover new options, they want them from you, too. 

If you can’t offer what they seek, they’ll find someone else who can. The digitally connected world makes it easier than ever for customers to compare brands and switch with minimal effort.

1. Digital innovation shapes business across all industries

Digital transformation impacts every industry. Whether your business generates revenue through client services, digital media, or physical goods, technological innovations can transform your means of production, distribution, and customer service.

Depending on your business, your customer could be a consumer or a business-to-business (B2B) client. Let’s extend our perspective to also include your employees. 

As we’ll talk about in a moment, employee expectations are being driven by their own consumer experiences, particularly when it comes to digital innovation in the workplace.

2. Customers expect digital technology and innovation

Today’s customers are connected and empowered by the digital era. They’re connected 24/7 and increasingly want and expect the same around-the-clock access to the companies they do business with. 

Over half of customers surveyed for Salesforce’s report “State of the Connected Customer” (first edition) said that technology has significantly changed their expectations of how companies should interact with them. 

Salesforce’s research also reports that 57% of consumers said it’s absolutely critical or very important for companies they purchase from to be innovative. 
Otherwise, they might just look for new companies to buy from: 70% of respondents said new technologies have made it easier for them to take their business elsewhere.

3. Employee empowerment drives digital solutions

The Apple iPhone is a key driver in the adoption of consumer technology in the workplace. Although it wasn’t originally marketed to businesses, it quickly gained popularity. 

This prompted corporate IT departments to accommodate employees wanting to use iPhones instead of other devices. As a few major employers embraced the iPhone, its acceptance in the enterprise spread rapidly.

The iPhone disrupted the traditional approach to technology adoption in the workplace. Rather than IT leaders dictating which approved devices to use, employee demand for iPhones led IT departments to adapt. This trend continues today, with more consumer-grade technologies entering the workplace.

4. Digital-first employees are connected employees

Today, we live in a digital workplace. Millennials and Gen Z are strong proponents of the digital-first mentality. Having grown up with PCs, consumer electronics, and phone apps, both generations expect powerful, easy-to-use digital tools in the workplace, just as they do in their personal lives.

Digital transformations apply this mindset to empower employees. Just as consumers seek businesses that connect with them 24/7 via social media and other digital channels, today’s workforce thrives in environments that facilitate collaboration, access to information, and flexibility in how and where they work. Digitalisation is a powerful ally in this empowerment.

For small businesses, embracing digitalisation can be game-changing. It’s key to meeting customer expectations and empowering employee experiences while helping them do more with less.

The efficiencies gained from going digital, such as having a comprehensive shared database, leveraging customer data for personalised messaging, and enabling mobile connectivity — allow small teams to focus more on winning and retaining customers.

5. Digital innovations are transforming industries

Employees aren’t the only ones benefiting from easy, always-on access to information in the workplace; machines are getting smarter, too. 

Emerging technologies like artificial intelligence (AI) and machine learning, the Internet of Things (IoT), cloud analytics, and various sensors are transforming manufacturing, production, research, and virtually all facets of business across industries. The examples are never-ending.

Digital innovations like AI and the IoT are driving all manner of advancements in the production of everything from consumer goods to cars and trucks.

• Optimised manufacturing processes adapt to changing consumer demand. 
• Cloud-based software affords real-time visibility into supply chain logistics. 
• Customer experience mapping powered by machine learning surfaces key insights to help product planners, marketers, and budget makers do their jobs better.

Together, these and many other innovations are changing the way we do business from every conceivable angle.

Examples of digital transformation

What does digital transformation look like in practice, and how has it already changed how we do business? Let’s look at examples of digital innovations in marketing, sales, and service that build closer customer relationships and empower employees across all industries.

Examples of digital transformation in marketing

At a high level, the goal of digital transformation in marketing is to find more customers while spending less money. More specifically, digital marketing generates more quality leads and helps you get closer to all of your customers.

The shift from analog to digital marketing materials helps these efforts in two key ways. First, digital materials are generally cheaper to produce and distribute than analog media. Second, digital marketing opens the door to marketing automation, analytics tracking, and dialogue with customers in ways that analog never could.

Instead of planning a one-size-fits-all trip down the funnel, marketers can build 1-to-1 journeys that observe customer behaviours and shape the experience to best suit each buyer.

Digital transformation helps marketers connect with individual customers. Let’s look at some examples that detail how digitally transforming your messaging strategy can increase customer engagement and reduce costs.

Examples of digital transformation in sales

The traditional marketing and sales roles are being redefined in the digital age. It’s all about the data.

The ability to collect precise data on consumer behaviour allows marketing and sales teams to work in ways never before possible. The natural bond between marketing and sales becomes evident by studying consumer behaviour from the first touchpoint through the buying journey. Nurturing that bond leads to better collaboration.

Data makes every sales rep productive.

Salespeople benefit from access to better data. When marketing and sales teams share information across a CRM and sales reps keep their pipelines updated allows for information to flow freely.

More eyes on the same information create opportunities to share intelligence across the business. Someone from marketing ops might see a sales rep’s note about a prospect and share marketing campaign activities that help move the deal along.

Second, as information gathers, you can leverage digital innovations like artificial intelligence.

Digital transformation creates AI-driven sales techniques.

AI systems can comb through data to find useful patterns and insights. They study sales and marketing data from the end-consumer standpoint and determine the effectiveness of sales techniques. AI can identify which demographics are more likely to buy at certain times of the year.

With more datasets available, AI can mine marketplace information and your sales history. The systems look for correlations and patterns to give your teams a competitive edge. Combining AI insights with the knowledge of your teams realises digital transformation for sales.

Social selling strategies are a key component of digital transformations.

Consumer participation in social media has changed the buying process, so successful digital transformation must incorporate a social selling strategy. This medium offers opportunities for salespeople to connect and build relationships with prospects and longtime customers.

Examples of digital transformation in service

Customer service, and our ideas around where service begins and ends, are being upended by the digital era as much as any other part of business. Maybe more so.

With everything from pizza delivery to child care available at their fingertips, customers expect more companies and industries to embrace digital as their primary means of doing business. For service departments, that means greater expectations for 24/7 problem-solving on the customer’s channel of choice and greater opportunities to delight buyers and win more business.

Social media is the new customer service desk.

Listening and responding to customers across social media channels sounds daunting. However, tools for social customer service make it easy to highlight customer needs and measure brand sentiment.

Meeting your customers where they are is key to winning business. A digital transformation mindset can turn service calls into opportunities to grow your brand.

Collaboration is essential. The Salesforce “State of the Connected Customer” report shows that 84% of high-performing marketing leaders say that service collaborates with marketing on social inquiries, while just 37% of underperformers do. When information is freed from silos, teams collaborate more, and businesses perform better.

Self-service is a service agent’s best friend.

Remember when customer service meant calling a toll-free number for product questions or warranty claims? While call centres still exist, the digital age offers more flexibility for serving customers.

Self-service portals are a great example. These tools provide features like password reset, incident logging, service requests, and knowledge base searches. They can also include interactive services like chat, and social media feeds relevant to service issues.

User-friendly designs, such as suggestion fields and personalised profiles based on purchase and service histories, improve the customer experience. A good self-service portal can reduce demands on service agents. According to our research, customers value self-service: 59% of consumers and 71% of business buyers say it impacts their loyalty.

AI plays a key role in transforming service.

AI-powered chatbots can answer simple inquiries, reducing wait times for customers. They also free up personnel for more complex cases. When queries exceed a chatbot’s capabilities, natural language processing helps direct questions to the best expert.

Examples of digital transformation in banking

Banking has been transformed by digital technologies, benefiting many consumers. Not long ago, most transactions were handled in person by bank tellers. 

Automated teller machines (ATMs) streamlined transactions, extended business hours, and reduced wait times for cash withdrawals. Over time, ATM technology evolved to support cash and check deposits, more secure transactions, and multiple accounts, including credit cards and mortgages.

Recently, PCs and mobile devices have led to online and mobile banking, as well as cashless payment systems. Consumers now conduct more banking online, including paying bills and sending funds. 

Mobile banking apps allow users to deposit checks remotely, and payment systems like PayPal and Apple Pay enable consumers to make purchases with accounts linked to their phones, without cash or cards.

Examples of digital transformation in retail

Retail has been radically transformed in the digital era, impacting both the in-store experience and ushering in e-commerce.

Digital technologies have improved the retail experience for consumers and proprietors and enabled digital transformations such as loyalty cards, e-coupons, automated inventory, and retail analytics systems. 

Shoppers now show their phones at checkout for discounts, with digital systems tracking consumer behaviour and triggering personalised events like emails and SMS messages. Digital beacons can further enhance the in-store experience by linking to mobile apps to alert shoppers upon entering the store.

Retailers are experimenting with subscription-style sales using Internet of Things technology. For example, Amazon has Dash Buttons, which are IoT-enabled devices that trigger automated reordering of household items. Just click the button when running low, and a refill will be dispatched to your Amazon Prime account.

Examples of digital transformation in insurance

The impact of digital transformation in the insurance industry is driven by changing consumer expectations. Web and app-based self-service portals allow consumers to compare shops, enroll in coverage, use multiple agents and carriers, and file claims without needing to speak to an agent, saving time and money.

A notable aspect of digital transformation in insurance is the role of the Internet of Things (IoT). Inexpensive IoT-enabled sensors provide insurers with valuable data for forecasting and claim reviews. 

For example, in car insurance, in-vehicle sensors monitor driving habits and reward safe driving. Sensors connected to phones can deter texting while driving by disabling messaging apps, and connecting vehicles to wearable devices that measure blood alcohol content could help prevent drunk driving by temporarily disabling the engine.

What are some signs that a business needs a digital transformation?

Signs that your business is in need of a digital transformation can appear across different parts of your organisation. They may not scream, “It’s time to go digital!” or “Why aren’t you on Instagram?” Instead, they could manifest as a diverse set of business problems.

If one or more of the items on our checklist rings true, it might be time to think seriously about developing a digital transformation strategy.

1. Repeat business isn’t what it used to be

Customers not returning isn’t necessarily a sign of poor products or services; it could be due to competitors’ promotions, lack of follow-up, or other reasons. A digital transformation for your messaging strategy could reveal why repeats are dwindling.

2. Promotions are no longer generating leads

Are you measuring their impact? It’s hard to pinpoint the effectiveness of print campaigns, and last year’s digital strategies may no longer work. If your promotions aren’t effective, consider a new approach to marketing.

3. Cross-departmental complaints are rising due to a lack of collaboration and information sharing

The idea that sales and marketing don’t get along is outdated. Collaboration is essential, and making data accessible across departments is key.

4. Your technology feels old, and your employees want features they’re used to from consumer apps

Spreadsheets shouldn’t be your only tool. Modern business apps that integrate for data sharing and offer user-friendly experiences across devices are essential. If your current technology lacks these features, it may be time to explore better options.

Summing up

Digital transformation is a business transformation. It’s a transformation that’s being driven by the basic desire to make work better for everyone, from employees to customers. That’s why embracing digital transformation should be a key part of every business strategy.

The drivers we just walked through are some of the biggest reasons behind the massive changes rippling through the business world right now. Add to that, the need every business has to compete for and win customers. 

If your competitors are leveraging digital transformation to streamline production, expand distribution, build a better workplace for employees, and improve customer satisfaction, you’d better up your game, too.

Ready to transform your business with Salesforce Customer 360? Discover how a unified view of your customers can drive digital transformation.

The post What Is Digital Transformation and How to Implement It? appeared first on Salesforce.

RFQs are essential for effective project management. I’ll walk through everything you need to know about these handy documents, including an RFQ meaning, how they work, and how they differ from RFPs.

What does RFQ mean?

RFQ stands for ‘request for quote’ and is the process of asking another company, like a supplier or contractor, to submit price quotes or bids for a specific product, project, or service

A request for quotation has several essential details that you can provide to potential vendors, including:

• Product/service details: A breakdown of the specifications of your project.
• Company info: Information about your company, including your contact details.
• Submission deadline: By when you’d like the quote to be submitted.
• Delivery requirements: The timeframe for completing the project.
• Contract terms: Any legal considerations, such as penalties or liabilities.
• Evaluation criteria: Information about how you will judge all of the bids.

RFQs are valuable documents in dozens of industries worldwide. For instance, they’re often used by the government for public procurement. They’re also a common element of construction projects, wherein a firm sends RFQs to several potential suppliers when sourcing raw materials. 

RFQs are also invaluable in the tech space. Many technological projects, like building a data centre or installing a new IT solution, are complex and multi-faceted. Sending out an RFQ lets the organisation compare quotes to find the best partner. It also helps the company understand an estimated price range so they can budget accordingly.

What is the difference between an RFP and an RFQ?

Although a request for quote (RFQ) and a request for proposal (RFP) may seem similar, they are actually two different processes. 

An RFP is a document that a business creates to request information about a specific product or service. It’s created when an organisation doesn’t know exactly what it’s looking for. It’s all about gathering information. In contrast, an RFQ is sent when the business already has a project in mind and is looking for a price. 

With that in mind, an RFQ request contains more detailed information about the product or service the business seeks. It will detail exactly how the vendor can solve the business’s problem. Consider it an opportunity for the vendor to state their value proposition and win over the client.

When to use an RFQ vs RFP?

Let’s examine the key differences between RFPs and RFQs and explore when each is most appropriate for your digital procurement process. Understanding when to use an RFP versus an RFQ can streamline your decision-making and help you secure the best partnerships and services.

Knowledge of the project vs missing information

In general, you should use an RFQ when you already know all the details of the project you’re carrying out. You’ve already solved the problem. All that’s left to do is get a quote for someone to carry out the task to your specifications.

In contrast, you should use the RFP process when you haven’t quite figured things out. Perhaps you need the vendor to tell you how they’ll solve your problem. Maybe you haven’t ironed out the details and want the potential supplier to fill in the gaps. Either way, use an RFQ in this case.

Standard products vs complex projects

Similarly, if the product specifications are clear and standardised, an RFQ is a good choice. For instance, if you know you need 10,000 products by the end of the month, an RFQ will give you all of the information you require to make informed decisions. 

For a specific project that requires careful planning, an RFP request will help you get more information before you commit to a vendor. Building an app, for example, requires coding, wireframes, UI design, testing, quality assurance, and much more. There’s a lot more to consider than price when deciding on the ideal business to work with.

How to create an RFQ step-by-step?

The RFQ process isn’t too complicated, but it’s also crucial to get it right. Take the time to learn all of the best practices in our guide so you can nail the process every single time. Here are the steps to create an effective RFQ:

Step 1: Deciding on your approach

Before you start creating your document, you should decide on your preferred approach. Liaise with your team and stakeholders to clearly define the scope and specifications of your project. You should also decide on a preliminary budget. 

Following this, decide on a bid type. Do you want to approach a select few businesses or have companies approach you? There are a few different types to consider:

• Open bid: This involves opening your RFQ to all vendors. When a vendor bids, this bid will be visible to all other bidders. Essentially, this creates a bidding war to work with your business. It’s a great approach for transparency and cost management but may lead experienced companies to avoid working with you.

• Sealed bid: As with an open bid, sealed bids open the RFQ to every vendor. However, the vendors don’t know what other vendors offer during bidding. This avoids bias and favouritism by ensuring a level playing field for all bidders.

• Invitation for bid: With an invited bid, you conduct market research and reach out to specific potential vendors, asking them to submit their quotes for your review. This is useful when you already have a pool of companies you’d like to work with or when the project is so niche that you require a partner with specific expertise.

• Reverse auction: In a reverse auction RFQ, multiple suppliers bid for the chance to fulfil your order. The lowest bidder at the auction wins the contract. This is a viable approach for simple goods and services that multiple vendors can provide with no difference in quality.

Once you’ve chosen your bid type and aligned your organisation on the best approach, you can proceed to the next step.

Step 2: Preparing your document

Next, it’s time to prepare your document. This should clearly state your project requirements, such as specifications, quantity, payment terms, and delivery date. Here are all of the essential details to include:

• A brief introduction to your company and project. 
• Your business’s contact information.
• The vendor’s contact information.
• Submission requirements (what the vendor’s quote needs to include).
• Submission deadline (by when the vendor needs to send their quote).
• The project’s scope and specifications. 
• Any extra project resources, such as drawings or concepts. 
• The deadline for delivery of the project. 
• Selection process (what you’re looking for in your ideal vendor).
• Terms, conditions, and legal considerations.

Create a standardised RFQ document that you can send to every vendor. This will provide a level playing field, making it easier to compare prices and expertise. An RFQ template can help with this. We’ve provided one below for you to use.

Step 3: Send out your RFQ document

Made your document? Distribute it along with supporting resources to your chosen vendors. Depending on your industry and the project’s urgency, you can either send by email or direct mail. Remember to include a point of contact so they know how to reach you. 

Now, all that’s left to do is wait for the deadline. At this stage, it’s important to treat all vendors equally and keep an open mind. Leaving bias at the door is fair for your bidders and may help you secure a better deal than you’d initially anticipated.

Expert tip: Acknowledge every response you receive and keep your bid evaluation team informed about progress. Keeping tabs will ensure everyone stays in the loop and invested in the RFQ business process.

Step 4: Review the RFQs and choose your vendor

Review them once the deadline has passed and you’ve received all competitive bids. You and your team need to ask:

• Who is providing the cheapest quote?
• Which business can provide exactly what we need?
• Does this business’s value proposition align with our needs?
• Which vendor is truly providing the best value?

Your team should also evaluate the bidders based on your own internal factors. It’s all about considering what you need and whether that supplier can provide it. Price is a factor, but there’s little use choosing the cheapest option if they can’t offer the right service.

Step 5: Close the deal

Have you decided on your chosen vendor? Let them know promptly, but wait until they’ve signed the contract before you inform the other bidders. 

You’ll need to negotiate with your chosen vendor to iron out any details, such as deliverables, timeframes, and whether the pricing is fixed. Use contract lifecycle management software to create and distribute a robust contract that protects your business from operational risk. 

Once all contracts are signed, you should notify and thank the other bidders. Being courteous is important as you might want to work with the other vendors later. Encourage them to bid on future projects if and when they arise.

An RFQ template

Below, we’ve created a standardised template that you can populate with your business information. Here, you can break down your RFQ into buyer info and project details, seller information, and the evaluation process.

RQF example A-Z Construction

To show you how the template above could look in practice, let’s fill out the RQF to send to vendors for the imaginary A-Z Construction, who have just been tasked with building a new bridge over the Darling River. They need the construction materials, so they’ll send out an RFQ to find potential vendors.

RFQs best practices

Here are some best practices to keep in mind throughout the RFQ process. By following these tips, you’ll be better prepared to achieve accurate, streamlined results across your supply chain, ensuring efficiency at every step.

1. Be as clear and detailed as possible

The more clear you can be with your RFQ, the more likely you are to get responses with accurate quotes. Include details about your upcoming project, including the quantity, specifications, and delivery expectations.

2. Create a level playing field

You may miss an opportunity if you go into the RFQ process with your eye on one supplier. Keep an open mind and be fair to every vendor. Ensure they can access the same data and see the same document as everyone else. This makes it easier to compare options once submissions come through.

3. Create pre-qualification selection criteria

Reviewing 30-50 submissions at once can be very challenging if you’re reviewing each in kind. Choose some pre-qualification criteria to quickly pre-screen applications. 
For instance, you could filter out candidates who have never worked on a project like yours or instantly filter out vendors priced above a certain threshold. This would reduce the pool of applicants, saving time.

4. Check pricing structures carefully

When you review the quotes provided by each vendor, check carefully for hidden fees and contract terms. A potential supplier might offer the cheapest rates for a service, but vague contract terms may lead you to pay more down the line. It’s worth it to be diligent.

5. Protect yourself

When you enter the contract negotiation stage, clearly outline your terms and conditions. We suggest securing a product warranty to protect you if the materials are defective. You can also set discounts to protect you if the product arrives late. Outlining these TOCs is essential to contract management.

Summing up

RFQs are the most effective way to achieve cost savings on your next project. They give you the option to select the best deal from multiple vendors. They can also help you find reliable partners who understand your needs and how to meet them. 

Managing projects from start to finish requires excellent organisation and stellar communication. Salesforce can help with that. With our platform, you can view all of your data, teams, customers, and projects in one place. This makes it easier to see what needs to be done, when, and by who. 

Take control of your projects today and start driving results with data management by Salesforce. Browse our range of products today to learn how our platform can help you grow your bottom line. 

Working as a vendor? The Salesforce CPQ can help you quote quickly and accurately from anywhere. With our guided selling flows and discounting rules for sellers, you’ll have everything you need to provide fast quotes and win bids more frequently. Find out more about the Salesforce CPQ today.

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