As brands look for ways to get closer to consumers, more than half of marketers, 75% of marketers are already rolling up their sleeves and experimenting with or fully implementing AI. Our most recent State of Marketing survey found that top Marketing AI use cases are related to automation, highlighting the importance of scaling up speed and effectiveness. 

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Let’s take a look at 9 ways using AI and agents as a digital assistant can increase the effectiveness and efficiency of your campaigns. It’s time to say goodbye to the redundant manual campaign tasks marketers wish they didn’t have to do – and let AI and agents help make the most of your time.

Table of contents

1. Make better decisions with automated data analysis and insights
2. Increase engagement and conversions with agent-driven audience segmentation
3. Anticipate your customer’s needs with predictive analytics
4. Save time with agent-driven end-to-end campaign assistance
5. Streamline workflows with campaign automation
6. Clearly show campaign success with performance tracking and reporting 
7. See what works best with A/B testing
8. Grow revenue with lead scoring and nurturing
9. Improve communication with internal collaboration tools

1. Make better decisions with automated data analysis and insights

AI can analyse large volumes of campaign data, including customer behaviour, campaign performance metrics, and market trends. It can identify patterns, extract insights, detect correlations, and provide actionable recommendations to improve campaign strategies and targeting.

You’ll get a deeper understanding of customers and campaign performance, enabling you to make informed decisions and find success faster.

Many brands are turning to AI agents, such as Agentforce, which uses secure data from your CRM to help you with tasks in a conversational way. For example, you can ask questions in the flow of work, just like you speak with a co-worker. Unlike chatbots and copilots, agents’ ability to plan and reason enables them to take action on the data, making recommendations and even doing things like build digital storefronts, draft custom code, and create data visualisations. And when needed, the agent can seamlessly hand off to human employees with a summary of the interaction, an overview of the customer’s details, and recommendations for what to do next. (Back to top)

2. Increase engagement and conversions with agent-driven audience segmentation 

After analysing the customer data, your AI agent can then segment audiences based on demographics, behaviour, preferences, purchase history, and other important attributes. AI eliminates the manual effort required for segmenting audiences and targets specific customers with more relevant offers. 

When you’re able to personalise messaging for different segments, you’ll see campaigns succeed more. (Back to top)

3. Anticipate your customer’s needs with predictive analytics 

AI predictive models use historical data to forecast customer behaviour, such as likelihood to convert, churn, or engage with specific campaign elements. 

This helps you stay one step ahead to proactively address customer needs and budget resources effectively. (Back to top)

4. Save time with agent-driven end-to-end campaign assistance

Creating a full end-to-end campaign with unique content frequently can be one of the most time-consuming tasks for many marketers — but an AI agent can help with using the right data foundation. Agentforce Campaigns powered by natural language prompts (NLP) and grounded by real-time data in Data Cloud can generate not only a campaign brief and target audience segment, but it can also save time creating content — such as ad copy, email subject lines, and social media posts — that resonates with your customers. 

You can provide the finishing touches to make sure the content is in your voice and tone. It can also improve your content by analysing performance data, identifying high-performing elements, and suggesting improvements. (Back to top)

5. Streamline workflows with campaign automation

AI and agents can automate various aspects of campaign execution, such as scheduling and deploying ads, sending targeted emails, or managing social media posts. This reduces manual effort and ensures that your campaign runs on time. 

What can you do with the time freed up, thanks to AI and agents? Focus on strategy and innovative ideas, helping you build lasting customer relationships. (Back to top)

6. Clearly show campaign success with performance tracking and reporting 

According to our State of Marketing report, high-performing marketers are able to analyse data in real time, giving them an advantage when it comes to responding to and optimising campaign performance.

Your AI agent can automate the tracking and reporting of campaign performance metrics — in ways that anyone can understand. AI can generate real-time dashboards and visually-pleasing customised reports, giving you and your stakeholders a clear view of campaign performance and key metrics, without the need to do it all by hand. 

This helps you make data-driven decisions, optimise campaigns on-the-go, and demonstrate the value of your efforts to stakeholders. (Back to top)

7. See what works best with A/B testing 

AI can perform A/B tests on campaign elements, such as ad variations, landing pages, or email designs. It analyses performance data, identifies winning variations, and helps you continuously refine your strategies. (Back to top)

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8. Grow revenue with lead scoring and nurturing 

With AI, you can automate lead scoring by analysing lead data, behaviour, and engagement history. It assigns scores to leads based on their likelihood to convert and deliver personalised content to move prospects through the sales funnel. 

With AI’s lead scoring, your team can focus on the most promising leads and nurture relationships at scale. (Back to top)

9. Improve communication with internal collaboration tools

AI shines as your digital assistant when handling internal collaboration needs. You can use this technology to automate messaging in your department, as well as project management, task assignment, and file sharing. Teams can even apply workflow automations that schedule meetings, send reminders, or organise files — taking care of the little details so you can focus on campaign success.

AI is transforming campaign management by allowing teams to automate manual tasks, freeing marketers to work on more big-picture ideas. With AI as your ally, you can streamline your campaigns, see better results, and start focusing on your next successes. (Back to top)

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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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In Singapore, 96% of business leaders feel their data holds untapped potential, yet many are struggling to translate this into meaningful business outcomes. 

To explore how ASEAN businesses are overcoming hurdles and blazing new trails for an AI-driven future, Salesforce gathered insights from hundreds of Singaporean analytics, IT, and business leaders. Their perspectives, alongside those from 10,000 global leaders, are featured in our State of Data and Analytics Report.

Here are key takeaways from Singapore’s leaders — including practical tips on how you can make the most of your data and seize the AI opportunity.

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Takeaway #1: Without trusted data, your AI can’t deliver

It’s no surprise that 9 in 10 analytics and IT decision-makers in Singapore agree that trustworthy data is more important than ever. Every AI use case — from predictive analytics to IT management — runs on data. But your AI solutions won’t produce the results you’re looking for if you aren’t working with quality, trusted data. Simply put: flawed data leads to flawed results.

Yet, only 59% of analytics and IT leaders in Singapore are fully confident in the accuracy of their data. This gap shows there’s still plenty of work to do, which is why improving data quality emerged as a top priority for many businesses in the region.

So, how do you make sure your data is “trustworthy”? As data volumes grow, what are the best practices to ensure quality and consistency? Here are a few things to ask yourself:

• Do you have a clear and consistent data collection and data entry process? 
• Has your data been cleaned and normalised?
• Is your data protected and secure? 
• Do you have a governance framework addressing all aspects of the data life cycle, including access and control, management, privacy, security, compliance and regulatory requirements? 
• Is the data fit for the purpose? Does it follow ethics and integrity expectations?

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Takeaway #2: Getting more value from your data is easier when your data and business strategies are aligned

Data generation is exploding worldwide, and ASEAN is no exception. The digital ecosystem in this region is expected to triple in size by 2030, jumping from US $300 billion to nearly US $1 trillion. Data usage per user in Southeast Asia is set to skyrocket from 9.2 GB per month in 2020 to 28.9 GB by 2025.

Amid all this growth, 98% of Singaporean business leaders agree that data and analytics improve decision-making. However, two major challenges are holding many back: the sheer volume of data and the time it takes to turn that data into insights.

Our survey found that 82% of analytics and IT leaders in Singapore say their organisations struggle to drive business priorities with so much data. Only 63% feel their data strategy is fully aligned with business goals, making it clear why their second-highest priority — after improving data quality — is aligning data strategies to better harness the volume for meaningful business outcomes.

Understanding your business objectives is crucial when planning your data strategy. Start by clearly defining your goals, then map out the specific data needed to achieve them. With high volumes of data, it’s even more important to be selective — focus on the data that will have the greatest impact, and ensure you have the right tools in place to leverage it effectively. This targeted approach helps to drive much-needed insights across the organisation and reduce data silos that impede collaboration and decision-making.

The results? Better financial performance, higher productivity, and a stronger competitive edge. Research from McKinsey Global Institute shows that data-driven companies are 23 times more likely to acquire customers, 6 times more likely to retain them, and 19 times more likely to be profitable.

Takeaway #3: Higher data maturity opens doors to growth

When an organisation is data mature, we mean it’s well advanced on its data transformation journey — using data to drive decisions, fuel innovation, and plan for the future. But according to our research, only 28% of Singaporean businesses describe their data maturity as best-in-class.

Why does this matter? Because data maturity brings clear rewards. McKinsey’s research shows that data-driven companies achieve goals faster and see at least 20% higher contributions to earnings before income taxes.

Singaporean IT and analytics leaders report that a well-developed data culture — where everyone has the insights needed to be data-driven — leads to outcomes like higher cost savings, better productivity, improved customer service, more innovation, and faster decision-making.

And as AI becomes more integral to business strategies, data maturity is also a key indicator of AI success. Globally, organisations with higher data maturity are twice as likely to have the quality data needed to use AI effectively and recognise the growth benefits that come with a well-executed AI strategy

Unified data is the foundation that enables AI — first predictive, then generative, and now agentic — to work smarter and more autonomously. Agentforce taps into this unified data to deliver automated, intelligent responses and actions across your business. For example, when a Service Agent is connected to your order management system, knowledge articles, policies, and purchase history in Service Cloud, it can autonomously handle refunds, exchanges, and inquiries — boosting customer satisfaction while freeing your team for higher-value tasks.

With the right data foundation, agents streamline processes and unlock growth by helping businesses scale efficiently, improve retention, and generate new revenue through cross-selling and loyalty-building initiatives. Humans with agents can transform business growth — but it all starts with data maturity.

AI and Data Transformation in Singapore

Businesses that thrive are the ones that adapt, and today, data transformation is one of the most important adaptations a company can make. With AI poised to be as pervasive as the internet, companies must be ready to unlock its potential — but great AI starts with great data.

Yet, 84% of Singaporean business leaders are worried about missing out on the opportunities that generative AI presents. With AI set to revolutionise industries, Singaporean businesses that focus on improving data maturity, strengthening governance, and aligning data strategies with business goals will be the ones best positioned to succeed in the AI era.

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• How To Build a Data Culture: What Leaders Need To Know
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• The 2024 Connectivity Benchmark Report: Key Trends Shaping APAC’s Digital Landscape

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The service landscape in Singapore is evolving at a breakneck pace. The Sixth Edition State of Service report provides a pulse check on the priorities, challenges, and opportunities of over 5,500 service professionals worldwide. 

As customer service trends reveal that customers are setting their expectations sky-high, organisations are under more pressure to deliver personalised service at scale while delivering more value to the business. But there’s good news, too. The introduction of artificial intelligence and enhanced data capabilities are transforming how quality service is delivered, offering exciting opportunities to boost agent productivity and generate revenue.

Singapore’s Customer Service Trends: Everyone Wants More, from Customers to Executives

In Singapore, the stakes are high and climbing higher. 81% of service professionals in the region report that customers are more demanding than ever, with expectations of fast, personalised service at every interaction.

It won’t come as a surprise, then, that globally, customer experience topped the list of both priorities and challenges for service organisations. The biggest priority for service decision-makers is improving the customer experience, while their biggest challenge is keeping pace with customer expectations. 

Multiplying the burden on service, the surge in customer demands correlates with an anticipated increase in cases, with 65% of service professionals in Singapore bracing for higher volumes in the coming year. 

These projections underscore a critical challenge: delivering personalised service at scale – a requirement for maintaining customer loyalty and satisfaction. Self-service, including knowledge-powered help centres, customer portals, and AI-powered chatbots, stands out as a win-win solution.

There’s a growing desire for self-service, with 61% of customers globally reporting they prefer it for fixing simple issues. And while self-service now solves 54% of customer issues worldwide at organisations that use it, the pressure is on to get self-service right. 72% of customers won’t reuse a company’s chatbot after just one negative experience, so there’s little room for error. 

Adding to these growing demands is the awareness that customer service has evolved far beyond a simple support function and cost centre. Today, it’s at the forefront of revenue generation. In Singapore, 80% of service organisations are expected to ramp up their contribution to the bottom line. 

This dual pressure requires organisations to not only maintain but amplify service quality, so they can meet the expectations of customers and revenue.

Customer Service is Expanding – in Budget, Headcount and Channels

To meet these soaring demands, service departments in Singapore are ramping up their resources. About 77% of service professionals in the region anticipate an increase in budget, while 71% expect to expand their headcount, suggesting that Singaporean organisations are preparing to scale up operations to rise to the challenge of today’s customer service trends.

A diversification in service channels – now averaging thirteen different modes of customer engagement for organisations in Singapore — higher than the global average of twelve. This indicates a strategic move to interact with customers across multiple platforms.

There’s a clear trend in favour of an omnichannel service experience. High-performing organisations provide service across a broader range of channels than underperformers, making it more important than ever to meet customers where, when, and how they want to engage. Live chat, in particular, has been adopted by 9 in 10 high performers globally – but only among 60% of underperformers.

Infographic: The State of Service in Singapore

Customer Service AI & Data Promises Scale

Artificial intelligence’s role in transforming service operations is becoming increasingly critical as organisations look to technology as the solution for tougher workloads and more demanding customers. 

With 47% of Singaporean organisations fully implementing customer service AI, and 43% exploring or experimenting with the technology, AI’s rollout in Singapore’s service landscape is well underway. 

The benefits are clear: 97% of service professionals in these AI-equipped organisations acknowledge the time-saving benefits of AI, while 94% see cost reductions. 

Service organisations are using AI to increase agent efficiency and productivity, which increases their ability to provide customers with prompt and personalised experiences. The top three use cases for AI in Singapore are automated summaries and reports, customer-facing intelligent assistants, and service responses, with plenty more exciting applications emerging.

As service organisations commit to AI, they’re also putting a focus on trustworthy and connected data. 87% of service professionals in Singapore say better access to data from other teams would improve the support they provide, pointing to the importance of a comprehensive data strategy to underpin AI.

In response, 72% of these service organisations report an increase in investment in data integration efforts next year, pointing towards a strategic push to enhance the efficacy and responsiveness of service operations through better data accessibility and connected, efficient service systems.

How Does Your Service Organisation Stack Up?

The trends are clear and the data is compelling: service in Singapore has been forever changed by new customer demands, organisational imperatives, and AI capabilities. 

These are just a few insights into the nature of service today. To learn more about what your customers want and how you can wow them with a strategic approach to customer service – from self-service to the contact centre to the field – read the Sixth Edition State of Service report.

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The service landscape in the Philippines is evolving at a breakneck pace. The Sixth Edition State of Service report provides a pulse check on the priorities, challenges, and opportunities of over 5,500 service professionals worldwide. 

As customer service trends reveal that customers are setting their expectations sky-high, organisations are under more pressure to deliver personalised service at scale while delivering more value to the business. But there’s good news, too. The introduction of artificial intelligence and enhanced data capabilities are transforming how quality service is delivered, offering exciting opportunities to boost agent productivity and generate revenue.

Customer Service Trends from the Philippines: Everyone Wants More, from Customers to Executives

In the Philippines, the stakes are high and climbing higher. 83% of service professionals in the region report that customers are more demanding than ever, with expectations of fast, personalised service at every interaction.

It won’t come as a surprise, then, that globally, customer experience topped the list of both priorities and challenges for service organisations. The biggest priority for service decision-makers is improving the customer experience, while their biggest challenge is keeping pace with customer expectations. 

Multiplying the burden on service, the surge in customer demands correlates with an anticipated increase in cases, with 70% of service professionals in the Philippines bracing for higher volumes in the coming year. 

These projections underscore a critical challenge: delivering personalised service at scale – a requirement for maintaining customer loyalty and satisfaction. Self-service, including knowledge-powered help centres, customer portals, and AI-powered chatbots, stands out as a win-win solution.

There’s a growing desire for self-service, with 61% of customers globally reporting they prefer it for fixing simple issues. And while self-service now solves 54% of customer issues worldwide at organisations that use it, the pressure is on to get self-service right. 72% of customers won’t reuse a company’s chatbot after just one negative experience, so there’s little room for error. 

Adding to these growing demands is the awareness that customer service has evolved far beyond a simple support function and cost centre. Today, it’s at the forefront of revenue generation. In the Philippines, 89% of service organisations are expected to ramp up their contribution to the bottom line — higher than the global average of 85%. 

This dual pressure requires organisations to not only maintain but amplify service quality, so they can meet the expectations of customers and revenue.

Customer Service is Expanding – in Budget, Headcount and Channels

To meet these soaring demands, service departments in the Philippines are ramping up their resources. About 82% of service professionals in the region anticipate an increase in budget, while 78% expect to expand their headcount — higher than the global averages of 80% and 76%, respectively. This suggests that organisations in the Philippines are preparing to scale up operations to rise to the challenge of today’s customer service trends.

A diversification in service channels — now averaging thirteen different modes of customer engagement for organisations in the Philippines — exceeds the global average of twelve. This indicates a strategic move to interact with customers across multiple platforms.

There’s a clear trend in favour of an omnichannel service experience. High-performing organisations provide service across a broader range of channels than underperformers, making it more important than ever to meet customers where, when, and how they want to engage. Live chat, in particular, has been adopted by 9 in 10 high performers globally — but only among 60% of underperformers.

Infographic: The State of Service in Phillipines

Customer Service AI & Data Promises Scale

Artificial intelligence’s role in transforming service operations is becoming increasingly critical as organisations look to technology as the solution for tougher workloads and more demanding customers. 

With 41% of Filipino organisations fully implementing customer service AI, and 36% exploring or experimenting with the technology, AI’s rollout in the Philippines’ service landscape is well underway. 

The benefits are clear: 92% of service professionals in these AI-equipped organisations acknowledge the time-saving benefits of AI, while 92% see cost reductions. 

Service organisations are using AI to increase agent efficiency and productivity, which increases their ability to provide customers with prompt and personalised experiences. The top three use cases for AI in the Philippines are service responses, customer-facing intelligent assistants, and agent-facing intelligent assistants, with plenty more exciting applications emerging.

As service organisations commit to AI, they’re also putting a focus on trustworthy and connected data. 85% of service professionals in the Philippines say better access to data from other teams would improve the support they provide, pointing to the importance of a comprehensive data strategy to underpin AI.
In response, 83% of these service organisations report an increase in investment in data integration efforts next year, pointing towards a strategic push to enhance the efficacy and responsiveness of service operations through better data accessibility and connected, efficient service systems.

How Does Your Service Organisation Stack Up?

The trends are clear and the data is compelling: service in the Philippines has been forever changed by new customer demands, organisational imperatives, and AI capabilities. 

These are just a few insights into the nature of service today. To learn more about what your customers want and how you can wow them with a strategic approach to customer service – from self-service to the contact centre to the field – read the Sixth Edition State of Service report.

Elevate Customer Service with Data-Driven Insights

Dive into the Sixth Edition State of Service report for valuable insights to optimise your service operations, from self-service to the contact centre to the field.

Download now

Read more

• Everything You Need to Know About AI in Customer Service
• 36 Customer Service Statistics To Move Your Business Forward
• Inside the Sixth Edition of the State of Service Report

The post AI is Helping Filipino Organisations Meet Rising Customer Service Demands appeared first on Salesforce.

The service landscape in Indonesia is evolving at a breakneck pace. The Sixth Edition State of Service report provides a pulse check on the priorities, challenges, and opportunities of over 5,500 service professionals worldwide. 

As customer service trends reveal that customers are setting their expectations sky-high, organisations are under more pressure to deliver personalised service at scale while delivering more value to the business. But there’s good news, too. The introduction of artificial intelligence and enhanced data capabilities are transforming how quality service is delivered, offering exciting opportunities to boost agent productivity and generate revenue.

Indonesia’s Customer Service Trends: Everyone Wants More, from Customers to Executives

In Indonesia, the stakes are high and climbing higher. 91% of service professionals in the region report that customers are more demanding than ever, with expectations of fast, personalised service at every interaction.

It won’t come as a surprise, then, that globally, customer experience topped the list of both priorities and challenges for service organisations. The biggest priority for service decision-makers is improving the customer experience, while their biggest challenge is keeping pace with customer expectations. 

Multiplying the burden on service, the surge in customer demands correlates with an anticipated increase in cases, with 74% of service professionals in Indonesia bracing for higher volumes in the coming year. 

These projections underscore a critical challenge: delivering personalised service at scale – a requirement for maintaining customer loyalty and satisfaction. Self-service, including knowledge-powered help centres, customer portals, and AI-powered chatbots, stands out as a win-win solution.

There’s a growing desire for self-service, with 61% of customers globally reporting they prefer it for fixing simple issues. And while self-service now solves 54% of customer issues worldwide at organisations that use it, the pressure is on to get self-service right. 72% of customers won’t reuse a company’s chatbot after just one negative experience, so there’s little room for error. 

Adding to these growing demands is the awareness that customer service has evolved far beyond a simple support function and cost centre. Today, it’s at the forefront of revenue generation. In Indonesia, 74% of service organisations are expected to ramp up their contribution to the bottom line. 

This dual pressure requires organisations to not only maintain but amplify service quality, so they can meet the expectations of customers and revenue.

Customer Service is Expanding – in Budget, Headcount and Channels

To meet these soaring demands, service departments in Indonesia are ramping up their resources. About 80% of service professionals in the region anticipate an increase in budget, while 68% expect to expand their headcount, suggesting that organisations are preparing to scale up operations to rise to the challenge of today’s customer service trends.

A diversification in service channels – now averaging twelve different modes of customer engagement for organisations in Indonesia – indicates a strategic move to interact with customers across multiple platforms.

There’s a clear trend in favour of an omnichannel service experience. High-performing organisations provide service across a broader range of channels than underperformers, making it more important than ever to meet customers where, when, and how they want to engage. Live chat, in particular, has been adopted by 9 in 10 high performers globally – but only among 60% of underperformers.

Infographic: The State of Service in Indonesia

Customer Service AI & Data Promises Scale

Artificial intelligence’s role in transforming service operations is becoming increasingly critical as organisations look to technology as the solution for tougher workloads and more demanding customers. 

With 57% of Indonesian organisations fully implementing customer service AI — higher than the global average of 49% — and 29% exploring or experimenting with the technology, AI’s rollout in Indonesia’s service landscape is well underway. 

The benefits are clear: 96% of service professionals in these AI-equipped organisations acknowledge the time-saving benefits of AI, while 98% see cost reductions. 

Service organisations are using AI to increase agent efficiency and productivity, which increases their ability to provide customers with prompt and personalised experiences. The top three use cases for AI in Indonesia are service responses, customer-facing intelligent assistants, and automated summaries and reports, with plenty more exciting applications emerging.

As service organisations commit to AI, they’re also putting a focus on trustworthy and connected data. 97% of service professionals in Indonesia say better access to data from other teams would improve the support they provide, pointing to the importance of a comprehensive data strategy to underpin AI.

In response, 81% of these service organisations report an increase in investment in data integration efforts next year, pointing towards a strategic push to enhance the efficacy and responsiveness of service operations through better data accessibility and connected, efficient service systems.

How Does Your Service Organisation Stack Up?

The trends are clear and the data is compelling: service in Indonesia has been forever changed by new customer demands, organisational imperatives, and AI capabilities.

These are just a few insights into the nature of service today. To learn more about what your customers want and how you can wow them with a strategic approach to customer service – from self-service to the contact centre to the field – read the Sixth Edition State of Service report.

Elevate Customer Service with Data-Driven Insights

Dive into the Sixth Edition State of Service report for valuable insights to optimise your service operations, from self-service to the contact centre to the field.

Download now

Read more

• Everything You Need to Know About AI in Customer Service
• 36 Customer Service Statistics To Move Your Business Forward
• Inside the Sixth Edition of the State of Service Report

The post AI is Leading the Charge in Indonesia’s Customer Service Transformation appeared first on Salesforce.

An AI chatbot is a computer program designed to simulate human-like conversation through text or voice. In contrast to older rules-based chatbots, which have been around for decades, AI chatbots are created by training large language models (LLMs) on vast datasets of human conversations and knowledge, which helps the chatbot to understand natural language and generate relevant, contextual responses. 

Here, you’ll discover how to clarify your needs, cut through the chatbot clutter, and find the best AI chatbot for the job.

What you’ll learn:

• The benefits of using AI chatbots
• How to pick the best chatbot
• The AI chatbot revolution

Dig into our latest customer service research

High-performing organisations are using data, AI, and automation to deliver faster, more personalised service. Find out how in the 6th State of Service report.

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The benefits of using AI chatbots

Thanks to AI chatbots, gone are the days of canned robotic responses, endless hold times, or making customers wait days for an email reply. Having always-on digital assistants available to deliver fast and personalised service is now the norm.

But chatbots don’t just benefit customers — they’re transformative for businesses, too. Every customer interaction is an opportunity for bots to uncover insights quickly and systematically, and potentially pinpoint ones you may have missed otherwise.

In an age where data is king, chatbots can collect and analyse conversational data from direct interactions with your customers. What are their biggest pain points? What questions keep coming up? With that knowledge, you can fine-tune your products and deliver even better service and support for your customers. (Back to top)

How to pick the best AI chatbot

With new chatbot options constantly hitting the market and offering varying specialties, it’s important to sort through the clutter and determine what is the best AI chatbot for your business. Understanding your specific needs, comparing costs, exploring customisation options, utilising your company’s existing data, and weighing functionality are all important factors to consider when deciding which AI chatbot to use.

To get started, consider these questions to pick the best AI chatbot for your business:

1. What are your goals?

There are specialised chatbots for everything from customer service to sales to marketing. So, the first step is to identify how a chatbot will help you accomplish your business goals. “What is the business outcome you want? For example, reduce cost, increase sales, or minimise customer service volume?” said Aron Kale, director of product management on the Salesforce AI team. “Getting really clear on what you want out of your chatbot is an important first step.”

For example, if providing better, more efficient customer service is your goal, prioritise chatbots that are adept at resolving common customer queries. Or, if automating repetitive internal tasks is the priority, look for chatbots that will integrate with existing systems and can handle multi-step workflows. If you’re focused on finding a solution to help increase sales and drive more conversions, find an AI assistant that can do things like make personalised customer recommendations and manage transactions. 

A broader view is also beneficial to consider the ways an AI chatbot could make a bigger long-term impact. Are there downstream key performance indicators that your AI chatbot could affect?

For example, an HR chatbot could improve employee productivity and engagement by providing around-the-clock employee support, answering questions about company benefits, or sending automated reminders for project deadlines. A sales chatbot might boost conversion rates and lower customer acquisition costs by helping sales teams zero in on the highest-quality prospects. Or a customer service chatbot can improve satisfaction, loyalty, and retention metrics by providing personalised customer assistance. 

Whatever your goals are, it’s important to understand the varied abilities of the different AI chatbot solutions on the market. Chatbots run the gamut, from those that are purely question-and-answer solutions to tools that can resolve issues independently from start to finish. Map out the ideal level of conversational complexity, task automation, language support, and human-AI interaction for your business.

2. Where does your data live?

One of the most important factors to consider when picking the best AI chatbot for your business is access to your data. How easily the tool can tap into your company’s own data can mean the difference between a richly personalised output and one that’s generic and unhelpful. 

“Think about it in terms of what your human workers need in order to do their jobs well. Those are the same things your chatbot needs,” Kale explained. “So if you want your chatbot to focus on customer service issues, your chatbot needs to know your policies and look up an order status. Which you can pull in through tools like Data Cloud and MuleSoft.”

Think about the wealth of information contained in your company’s customer records, product information, service histories, internal policies, prospective customer data, and more. This data is a goldmine of insights, but it’s often spread across siloed databases, CRM software, conversation histories, and document repositories. Instead of giving generic responses, an AI chatbot that can access, understand, and seamlessly use this data can provide actionable answers that are specific to your business.

Let’s say that you run an online sports apparel store and a new customer asks the chatbot, “When will my order arrive?” A basic chatbot might reply with a standard line like “Orders typically ship in 3-5 business days.”

In contrast, a chatbot with access to your order management system would be able to respond in a much more personalised way: “Your order #913 for a Sacramento Kings jersey shipped yesterday via UPS. It’s expected to arrive this Thursday.” This kind of interaction goes a long way toward building brand loyalty, and the customer data needed to enable it likely already resides within your existing systems. 

A data-connected chatbot can also work wonders for your HR team. A chatbot that can tell employees exactly how many vacation days they have left or explain retirement plans based on their personal file makes for a great employee experience. And in sales, it can recommend products that complement a customer’s past purchases. In short, an AI chatbot that connects seamlessly to your company data is a true expert in your business, not just a generic assistant.

When comparing chatbots, understand how different solutions connect to various data sources. Can it integrate with common systems like Salesforce, SAP, or Microsoft Dynamics? Does it support APIs to link to custom databases? How does it handle data security? The best chatbot for your business is the one that not only understands natural language, but also transforms your data into an important asset that can build lasting brand loyalty. 

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3. What resources do you need to get going?

As you evaluate different chatbot solutions, it’s important to have realistic expectations around technical expertise, budget, time to invest in learning, and what the AI can and can’t do right out of the box. How easy it is to integrate into your existing system should be a consideration.

For example, is it a low- or no-code option that needs minimal training to get up and running? What level of technical skill is required for customisations or updates? Are there additional costs for scaling or advanced functionality down the road? Getting a strong handle on the real human effort and total cost of ownership needed to get the most out of your chatbot is key.

Finally, consider what internal data your chatbot needs to be trained on to be effective across your marketing, service, sales, and IT teams. Uniting all your structured and unstructured data under one roof is the best way to get the most out of it, and making that data available through a tool like Agentforce which is a proactive, autonomous application that provides specialised, always-on support to employees or customers.

And if this big, exciting new world of AI is all a bit overwhelming, it’s important to know that you can start small and build up. “It’s really possible to start small and really simple, with things like Q&A and knowledge articles,” Kale said. “It doesn’t have to integrate to 50 things right away — you can add use cases and expand it to meet different use cases over time.” (Back to top)

The AI chatbot revolution

As natural language processing capabilities continue to advance, we can expect these virtual assistants to become even more humanlike and intuitive. The pace of AI innovation will continue to pick up speed, making now the best time to learn these tools, Kale said. 

“The space is rapidly evolving. We’re seeing so much innovation, and businesses are seeing real value like never before,” he said. “It’s going to keep getting faster and faster, better and better. Start learning and getting hands-on now so that you’re well prepared as the technology evolves.” (Back to top)

Cassidy Myers-Sims contributed to this blog article.

The post 3 Easy Steps to Pick the Best AI Chatbot for Your Business appeared first on Salesforce.

What is big data?

There’s an overwhelming influx of data that characterises our daily interactions. This phenomenon, commonly referred to as big data, encompasses a vast and ever-growing collection of information. It extends beyond the traditional structured data found in relational databases to include unstructured data such as social media posts, sensor data, and weblogs. The sheer volume, variety, and velocity of this data present both challenges and opportunities for businesses and organisations.

The term “big data” was coined in the early 21st century to describe the exponential growth and complexity of data. Its defining characteristics are often summarised by the three Vs: Volume, Velocity, and Variety. Volume refers to the immense quantity of data generated daily. Velocity pertains to the rapid speed at which this data is produced and processed. Variety encompasses a diverse range of data formats, including structured, unstructured, and semi-structured data.

The sources of big data are as varied as the data itself. Social media platforms, e-commerce transactions, sensor networks, and scientific research contribute to this ever-expanding pool of information. The proliferation of smartphones, IoT (Internet of Things) devices, and cloud computing has further accelerated the growth of big data.

History of big data

The history of big data is relatively short, but it has already had a profound impact on the way we live and work. In the early days of computing, data was scarce and expensive to store. In the early days, raw data was often collected and stored without much processing, making it challenging to derive meaningful insights. As a result, businesses and organisations were forced to be very selective about the data they collected and stored. However, with the advent of cheaper storage and more powerful computers, it became possible to collect and store vast amounts of data. This led to the rise of big data.

The term “big data” was first coined in 2005 by Roger Mougalas. Mougalas used the term to describe the massive amounts of data that were being generated by the Internet and other digital sources. He argued that this data could be used to gain valuable insights into human behaviour and to improve decision-making.

In the years since Mougalas coined the term, big data has become a major force in business, government, and society. Big data is used to improve customer service, develop new products and services, and make better decisions. It is also used to study human behaviour, track disease outbreaks, and fight crime.

The potential of big data is enormous. However, there are also challenges associated with big data. One challenge is the sheer volume of data that is available. This data can be difficult to store, process, and analyse. Another challenge is the privacy of big data. Big data can be used to track people’s movements, habits, and preferences. This information can be used for good, but it can also be used for malicious purposes.

Despite the challenges, the potential of big data is too great to ignore. Big data is changing the world, and it is important to understand how it works and how it can be used.

Data-driven innovation

Data-driven innovation is the process of using big data analytics to analyse data and derive insights for informed decision-making. This can help organisations improve efficiency and productivity, develop new products and services, and improve customer service.

Data scientists and analysts play a crucial role in analysing data to uncover trends and patterns that can drive business decisions.

One example of data-driven innovation is the use of big data analytics to improve customer service. By analysing customer data, businesses can identify trends and patterns in customer behaviour. This information can then be used to develop targeted marketing campaigns, improve customer service strategies, and develop new products and services that meet the needs of customers.

Another example of data-driven innovation is the use of big data analytics to improve healthcare. By analysing patient data, healthcare providers can identify trends and patterns in patient health. This information can then be used to develop personalised treatment plans, improve patient outcomes, and reduce healthcare costs.

The potential of data-driven innovation is enormous. By harnessing the power of big data, businesses and organisations can improve their operations, develop new products and services, and make better decisions.

However, there are also challenges associated with data-driven innovation. One challenge is the sheer volume of data that is available. Another challenge is the privacy of big data. Businesses and organisations need to be careful about how they collect, store, and use big data. They need to make sure that they are protecting the privacy of their customers and employees.

Despite the challenges, data-driven innovation is a powerful tool that can help businesses and organisations improve their operations and make better decisions. By harnessing the power of big data, businesses and organisations can gain a competitive advantage and achieve success.

How Big Data Works with Structured and Unstructured Data

In order to understand big data, it’s important to know how it works. A data lake is often used to store unstructured big data, allowing for flexible data management and quick access. The big data process can be broken down into five key steps: data collection, data storage, data processing, data analysis, and data visualisation.

The first step in the big data process is data collection. This involves gathering data from a variety of sources, such as sensors, social media, and customer transactions. Once the data has been collected, it needs to be stored in a way that makes it easy to access and analyse. This is where data storage comes in.

The next step is data processing. This involves cleaning and preparing the data to ensure data quality, which may include removing duplicate data and correcting errors. This may involve removing duplicate data, correcting errors, and converting the data into a format that is compatible with the analysis tools that will be used.

Once the data has been processed, it can be analysed to identify patterns and trends. This involves using statistical and machine-learning techniques to identify patterns and trends in the data. This information can then be used to make informed decisions about everything from product development to marketing strategies.

The final step in the big data process is data visualisation. This involves presenting the results of the data analysis in a way that is easy to understand. This may involve creating charts, graphs, and other visual representations of the data.

Big data examples

Big data is being used by businesses across a wide range of industries to improve their operations and deliver better customer experiences. Here are a few examples:

• Retail: Big data is used by retailers to track customer purchases, analyse customer behaviour, and develop targeted marketing campaigns. Retailers use big data analysis to uncover customer preferences and optimise inventory management. This information can be used to improve the shopping experience, increase sales, and reduce costs.
• Healthcare: Big data is used by healthcare providers to improve patient care, reduce costs, and develop new treatments. Healthcare providers, as business users, leverage big data to enhance patient care and operational efficiency. This information can be used to identify patients at risk for certain diseases, develop personalised treatment plans, and track the effectiveness of treatments.
• Finance: Big data is used by financial institutions to detect fraud, assess risk, and develop new financial products. This information can be used to protect customers from financial crime, improve the efficiency of financial transactions, and develop new investment opportunities.
• Transportation: Big data is used by transportation companies to improve logistics, reduce costs, and improve safety. Big data helps transportation companies in resource management by optimising routes and reducing fuel consumption. This information can be used to optimise shipping routes, track the location of vehicles, and predict traffic patterns.
• Manufacturing: Big data is used by manufacturers to improve quality control, reduce costs, and develop new products. This information can be used to identify defects in products, optimise production processes, and develop new products that meet the needs of customers.

These are just a few examples of how big data is being used by businesses to improve their operations and deliver better customer experiences. As the volume, velocity, and variety of data continue to grow, we can expect to see even more innovative and groundbreaking uses of big data in the years to come.

Big Data Technologies in Today’s World

Big data has become an integral part of our daily lives and has revolutionised the way we interact with technology, businesses, and information. In today’s world, the amount of data created every day is simply mind-boggling. According to recent estimates, the global data creation is a staggering 2.5 quintillion bytes of data every single day, and this number is only expected to grow exponentially in the years to come.

The impact of big data can be seen across various industries and sectors. For instance, in the healthcare sector, big data is used to improve patient care, reduce costs, and develop new treatments. By analysing vast amounts of patient data, healthcare providers can identify trends and patterns, leading to more personalised treatment plans and better patient outcomes. Similarly, in the financial industry, big data plays a crucial role in detecting fraud, assessing risk, and developing innovative financial products.

The retail industry also leverages big data to enhance customer experiences and drive sales. By tracking customer purchases, analysing customer behaviour, and developing targeted marketing campaigns, retailers can gain valuable insights into consumer preferences and provide more personalised services. Big data also plays a significant role in the manufacturing industry, where it is used to improve quality control, reduce costs, and develop new products.

Furthermore, the entertainment industry has embraced big data to create more engaging and personalised experiences for consumers. By analysing user data, content providers can tailor recommendations, improve streaming quality, and develop new content that resonates with their audience.

The growth of the Internet of Things (IoT) has further amplified the significance of big data. With billions of devices connected to the internet, from smartphones and smartwatches to industrial sensors and home appliances, the volume of data generated is immense. This data holds valuable insights into consumer behaviour, operational efficiency, and asset tracking, enabling businesses to make informed decisions and optimise their operations.

The world of big data continues to evolve rapidly, presenting both opportunities and challenges for businesses and organisations. Harnessing the power of big data effectively requires robust data management strategies, advanced analytics capabilities, and a commitment to data privacy and security. By embracing big data and leveraging its potential, businesses can gain a competitive edge, drive innovation, and transform their operations.

Future of Big Data and Machine Learning

The future of big data is bright. As the amount of data in the world continues to grow, so too will the need for tools and technologies to process and analyse it. This growth will create new opportunities for businesses and organisations of all sizes to use big data to improve their operations, develop new products and services, and make better decisions.

One of the most important developments in the future of big data will be the continued growth of artificial intelligence (AI) and machine learning (ML). These technologies are already being used to automate many of the tasks associated with big data processing and analysis, and they will become even more powerful in the years to come. As AI and ML become more sophisticated, they will be able to identify patterns and trends in data that are invisible to the human eye. This will allow businesses and organisations to make even better decisions and to develop new products and services that are tailored to the needs of their customers.

Another important development in the future of big data will be the increasing use of data visualisation tools. These tools make it possible to present big data in a way that is easy to understand and interpret. This will allow businesses and organisations to communicate the results of their big data analyses to their stakeholders in a way that is clear and concise.

Finally, the future of big data will also see an increasing focus on data privacy and security. As more and more data is collected and stored, it is important to ensure that it is protected from unauthorised access and use. Businesses and organisations will need to invest in data security measures to protect their data from cyberattacks and other threats.

The future of big data is full of potential. As the amount of data in the world continues to grow, so too will the opportunities for businesses and organisations to use it to improve their operations, develop new products and services, and make better decisions.

The post What is Big Data and Why Does it Matter? appeared first on Salesforce.

Digital transformation isn’t just a trend. It’s a core shift to the business landscape, with IT leaders refining their strategies, headcounts and budgets to cater to growing customer expectations and project demands. The 2024 Connectivity Benchmark Report sheds light on the state of digital transformation worldwide, with the latest trends in AI, integration, automation and API management. So how do businesses in the APAC region measfure up in the evolving digital landscape?

IT leaders are optimistic about AI

The report reveals that 88% of organisations in APAC are already using AI, and adoption is continuing to grow. IT leaders in the region foresee an 89% increase in their usage of Large Language Models (LLMs) over the next three years, exceeding the 69% increase expected globally. Moreover, 86% are confident that AI will increase developer productivity at their organisations in that timeframe.

This optimism is a good thing, considering they’re simultaneously reporting a 39% increase in IT requests. AI will play an essential role in sustaining productivity under these demands, helping IT teams to manage growing workloads and expectations both efficiently and cost-effectively.

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Organisations need to get their data ready for AI

Despite the optimistic outlook, 69% of APAC IT leaders say their organisation is ill-equipped to harmonise data systems to fully leverage AI, with 82% pointing the finger at data silos as hindering digital transformation efforts. While this figure is lower than the 81% global average, organisations across APAC still have a way to go to break down silos and better integrate data across the business. 

Integration hurdles are blamed by IT leaders for stalling digital transformation for 82% of APAC organisations. Kurt Anderson, Managing Director and API Transformation Leader at Deloitte Consulting LLP explains, “A lack of integration is the top barrier to adopting emerging technologies, especially AI. And as demand grows for seamless, personalised customer experiences, the interoperability of systems is crucial for harnessing the full potential of data, AI, and automation. That’s why integration should be the cornerstone of every IT leader’s digital transformation efforts in 2024.”

The potential of AI is limited only by the data that organisations can connect it to, and the outcomes they can drive from it. The report shows that IT leaders across APAC are increasingly aware of these integration and automation challenges, and underscores the need for a robust data strategy, with a focus on data currency, reuse and access.

IT teams are under pressure, but workflow automation can help

With 98% of APAC IT teams struggling to integrate efficiently, workflow automation emerges as a solution. Robotic Process Automation can drive efficiency and reduce the workload on IT teams. As automation is demanded across businesses, IT often plays a gatekeeper role, but workflow automation permits other teams to self-serve. The global investment in RPA is now 31%, up significantly from 13% in 2021, as IT teams realise its potential.

Singapore Institute of Management (SIM) underwent its digital transformation with Salesforce and MuleSoft, integrating multiple back-end systems to streamline the end-to-end experience for its learners and administrators. Learners can now access courses with a single sign-on and automated processes encourage self-service and more efficient case management.  

APIs become a strategic lever for growth

APIs are now a staple in the digital ecosystem, with 99% of organisations using them to streamline data access and fuel growth. In APAC APIs and API-related offerings contribute to 33% of all revenue. Furthermore, APIs have contributed to increased revenue for 41% of APAC respondents and cut operational costs for 27%.

M1, Singapore’s most dynamic communications company, found its legacy on-premise API gateway was too labour-intensive and slowed down the delivery of new offerings. Supported by MuleSoft Professional Services, it migrated to a more agile solution in just 9 months and is now completing 13% more projects a year through API reuse, while saving 15 man-days per project. 

“I’m really excited about the scalability we have with MuleSoft. In a fast-paced industry, we now have the confidence that we can stay ahead of the game with a future-proofed environment and delight our customers as we grow our business,” says Chiam Chee Kong, Deputy Head of Software Engineering & Architecture at M1.

With outlets in Thailand and Malaysia, popular retail brand Lotus’s chose Salesforce and MuleSoft to unify its systems and data so it can provide more personalised and streamlined customer experiences. MuleSoft’s API reuse and prebuilt assets helped it complete its digital transformation in just 14 months – half the time it allotted. 
“We knew that with MuleSoft as our API gateway and integration layer we would be able to be more agile and transform more quickly,” says Wiphak Trakanrungsi, Head of Technology Software Development and Innovation at Lotus’s.

Digital transformation is the competitive advantage

An enterprise API strategy that facilitates data integration across applications will empower leaders to accelerate innovation and operationalise AI to drive business value and growth for the future. Through revenue generation, operational cost reduction and the promotion of self-service, integration, automation and APIs will help businesses maintain their competitive edge in 2024 and beyond.

Read the full 2024 Connectivity Benchmark Report for a comprehensive understanding of the digital transformation landscape in APAC and beyond.

2024 Connectivity Benchmark Report

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The post The 2024 Connectivity Benchmark Report: Key Trends Shaping APAC’s Digital Landscape appeared first on Salesforce.