To mitigate these pressures, forward-thinking insurance companies in Southeast Asia are looking for new ways to use artificial intelligence (AI) to create efficiencies across the value chain. And, in doing so, they’re finding new opportunities for growth.     

In Singapore, for example, 50% of respondents to the Salesforce Connected Financial Services Report said they would switch to a new insurer if they offered a better digital experience. 

However, face-to-face engagement and the human advisory experience is still important in Southeast Asia to build customer trust, especially in high impact and personalised products like Life and Health (L&H) insurance. While in the Property & Casualty (P&C) insurance business, there has been an increasing momentum to go full digital and touchless.

There is a generational aspect to this human/digital balance too. For example, the more seasoned generations typically prefer some human touch in the customer journey, while young consumers usually prefer more digital experiences. 

Meanwhile, advancements in AI – particularly the rapid commercialisation of GenAI products through 2023 – have led insurers to adopt AI in various functions.

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1. Adopt a data-first approach to leverage AI

Data has always been crucial for managing risk, determining claims, and setting premiums. In addition, it’s also a critical tool actuaries use to set the prices and rules that give insurers confidence that they can cover claims while staying solvent and regulatory compliant. 

In this way, data is foundational to the insurance sector’s financial health and ability to mitigate risk. The advent of AI has heightened the importance of data in insurance to even higher levels, because AI is only effective when insurers use rich, interconnected, trusted datasets. 

For example, Thailand-based online car insurer, Roojai, is able to utilise granular data sets to focus on optimising the customer journey from origin to conclusion. This has contributed to a 25% reduction in cost per conversion, and a 16% increase in conversions.

To adopt a similar data-first approach, insurers can use Salesforce Customer 360 to: 

• Connect and unify customer data to enhance downstream applications with a 360-degree customer view. For example, Salesforce Customer 360 and MuleSoft connect your departments and customer data to provide a single, shared view of your customers. 

• Benefit from generative AI without compromising data thanks to best-in-class security guardrails and enterprise security standards. For example, the Einstein Trust Layer uses guardrails like dynamic grounding, zero data retention, and toxicity detection, to protect data privacy and security and improve AI results accuracy. 

• Grows deeper policyholder connections and increases productivity. For example, Data Cloud translates raw data into intelligence that enables insurance agents to visualise all customer engagement and activity, segment audiences, and prioritise cross-sell opportunities.   

• Enhance customer engagement with personalised interactions and insights. For example, a connected CRM gives your teams access to a single source of customer data they can use to personalise content and experiences to meet your customers’ unique needs. 

• Offer AI-driven insights, data analytics, and data visualisation across departments. For example, Tableau Analytics can be used to create intelligent experiences across the company with augmented analytics tools such as one-click storytelling with automated discovery, real-time recommendations, and narrative explanations with natural language generation.

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2. Use automation technology to streamline operational processes

Swift underwriting is essential to deliver a seamless insurance sales experience to customers. This has been a challenge for insurance companies because underwriting often requires extensive amounts of information processing and decision-making. Imagine managing extensive data on coverage, benefits, and pricing across numerous insurance plans, conducting rule validation, and workflows across various applications. This can significantly lengthen turnaround times.

In addition, underwriting is more than just a desktop task. It involves collaboration with various partners and customers. For example, insuring a High Net Worth Individual (HNWI) in Asia might require assessments of health, lifestyle, and financials. This sometimes involves third-party services, which adds further complexity. 

Similarly, commercial property insurance requires thorough property assessments, sometimes with onsite surveys by risk engineers. These comprehensive processes all contribute to longer turnaround times for the customer. 

Insurers can streamline insurance processes by leveraging industry solutions. For example, Salesforce Financial Services Cloud automates underwriting and pricing, optimises workflows and collaboration, reduces turnaround time, and thus enhances sales conversion and Customer Lifetime Value (CLV) by empowering agents to focus on effective sales engagement and opportunities for cross-selling and up-selling.

In addition, Slack, brings conversations, collaboration, and automation together, making collaboration and communication between underwriters, sales agents, product managers, and third party service providers organised and aligned. Past underwriting data and knowledge are made easily accessible through Slack’s AI-powered search.

And real-time analytics and visualisation, along with natural language queries, empower employees to make informed decisions quickly.

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3. Recruit multi-generational customers with innovative digital engagement

As a new, affluent young customer group emerges in the region, insurance engagement is shifting from limited touchpoints to more frequent contact. 

For example, Singapore-based insurer Singlife understands the importance of connecting with their customers on their preferred channels, and replaced post-delivered policy documents with engaging digital experiences. The aim is to make buying insurance as seamless an experience as shopping on any other online platform.

In addition, innovative telematics-based rewards, digital health concierges, health thought leadership, and engaging digital methods like embedded finance and gamified social media campaigns are all helping insurance companies to more effectively engage with customers online. 

At the same time, social media platforms that attract diverse user groups enable insurers to personalise their marketing efforts. 

Salesforce Marketing Cloud is one tool insurers are using to achieve this. It enables real-time, hyper-personalised engagement with native integration across digital platforms. Marketing Cloud also leverages Einstein AI for automated, customised customer journeys and sophisticated analytics for marketing performance and ROI insights.

This approach ensures insurers remain competitive and effective in a rapidly evolving market.

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Seize today’s opportunities with next-gen solutions

The insurance industry stands at a pivotal juncture, marked by both challenges and opportunities. 

For insurers, the path ahead includes adopting trusted AI and data strategies, automating and augmenting insurance processes, and captivating and retaining multi-generational customers through diverse channels. 

Leveraging platforms like Salesforce’s multi-cloud solutions will be crucial in integrating these initiatives, enabling insurers to not only satisfy current needs but also stay future proof. 

This strategic approach will drive sustainable growth and resilience in the ever-evolving insurance landscape in Southeast Asia.

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What is new, though, is how accessible AI is. When Turing first asked, “Can machines think?” he was met with so many barriers, including computing limitations and cost. A better question might have been, “Can we even afford to find out?” Now, there’s been such an explosion of AI offerings that 94% of business leaders see AI as essential to their work. But, with better access and more choices, AI implementation has become less an abstract vision for the future, and more akin to a New Year’s resolution — you know you should do it, if only you could get started.

Which brings us to today’s big question: How can your business get started on an AI journey, and in a way that reduces costs, increases value for your customers, protects your data, and doesn’t leave your people behind?

Where to start your AI implementation

In my role leading Salesforce Professional Services, I speak with business leaders around the world who are facing this challenging question. AI in its current state, with its myriad uses and capabilities, lends itself perfectly to my team’s advisory work. When AI can be used for anything from sales to customer service to marketing to backend code development, choosing where to start can feel overwhelming. So, before we help you build the roadmaps for your AI journeys, step one is finding what fits your goals.

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Salesforce Professional Services offers teams of trusted advisers providing specialised solutions for businesses in need. Advisers help you align on a vision, establish a tailored roadmap, and get faster value.

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Where will AI add value?

A journey needs a destination. From that outcome, we calculate a roadmap using the best route to get to a successful AI implementation. This means thinking about the end goal first (the business version of “manifesting”). Typically, goals for AI implementation fall into one of three categories:

1. Increasing revenue where AI unveils new market opportunities and streamlines operations 
2. Reducing costs where, through automation and process optimisation, AI reduces operational costs and enhances overall business efficiency
3. Driving customer loyalty where AI creates personalised experiences to help customers feel valued and understood, which builds and maintains loyalty, and in turn, translates to increased revenue and reduced costs

Once you figure out which of these goals aligns with your current business needs, we can get on the road(map). 

Recalibrate expectations

Knowing the destination doesn’t make the journey predictable. The technology may be more widespread now, but AI can still surprise. 

Consider the example of a retail company with a disastrous customer service call centre. Their high abandonment rates and low net promoter scores (NPS) indicate terrible customer satisfaction. Initially, they might focus an AI solution on the front end, like a customer service chatbot. But, on deeper exploration, they realised a better understanding of customer needs will provide a bigger benefit.

AI can play a critical role here in customer service automation and also in analysing feedback and purchasing patterns. But getting to this shift in perspective requires stepping back, looking at your business process, and finding inefficiencies or potential improvements. AI can emerge as more than a singular tool and instead as a strategic force, combining the best of computer science and data to reach business goals. 

Make the AI business case

As the business goals begin to come into focus, an important strategic checkpoint is clarifying the reasoning and justification for the AI implementation. When rolling out any big project or new technology, there should be a hard look at benefits, disadvantages, cost, and risk.

But, since AI has the potential to be a more transformative technology than others, and comes in many different shapes and sizes, it’s even more important to take this disciplined approach. Think of this as the last exit before the highway.

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Prioritise trust

Again and again, one of the top concerns about AI is trust. Luckily, that’s our #1 value at Salesforce. That means we strongly believe in addressing concerns about data security, privacy, ethical use of AI, and trust right at the onset. Transparency and clear communication about responsible AI practices are crucial. 

The most common questions that I’ve encountered are:

• “Where’s my data going again?” Understanding the flow and storage of data is fundamental. Once the data is collected and stored, it needs to be managed with the utmost care and respect for privacy.
• “Who are you sharing it with?” This is the heart of data-sharing policies. Data sharing should be governed by strict protocols and transparency, ensuring that information is only shared where necessary and under stringent conditions.
• “Is it protected?” The security of all data is vital. Implementing robust security measures to safeguard data against breaches and unauthorised access is a top priority in any AI implementation.

These valid concerns echo the early days of software as a service (SaaS), when businesses were initially hesitant to embrace that new technology. We’ve since seen that SaaS has transformed the landscape of software delivery and usage. AI has the potential to have an even greater impact. But this can’t happen if we don’t address issues up front and create trust.

Shape your company’s AI plan with a (human) AI Coach

When the Turing Test was first introduced in 1950, it was originally called “the imitation game” — the idea being if a computer could successfully imitate a human, then the answer to the question, “Can a machine think?” would be a definitive, “Yes!” Though it’s up for debate whether the Turing Test is still a useful measurement, the fact that it’s being debated at all means we’re not quite ready to go human-free.

Readiness for AI implementation transcends technology. There needs to be a comprehensive evaluation of AI’s potential business value, organisational data quality, the trustworthiness and security of the AI solution, and an organisation’s adaptability —  not to mention preparing for a new way of working. This is where Salesforce Professional Services’ trusted advisers come in. 

We bring the specialists and technology together with our AI Coach program. Through this process, we evaluate a company’s overall readiness, including internal skills and expertise, existing technology infrastructure, data preparedness, governance, and ultimately build the roadmap for long-term success. 

For now, the human part of AI might be the most important. Make it the experts at Salesforce Professional Services.

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You can’t scan the headlines lately without seeing buzz around generative artificial intelligence (AI). The product innovations are only beginning. But even with the best technology out there, you’ll still be faced with a key question: How can you implement AI at scale in a way that maximises the return on your investment? Let’s look at one model company you can learn from.

Breaking down silos

Schneider Electric, a global energy management and industrial automation company, has formalised an AI program under a new Chief AI Officer and scaled it to every corner of the company. Its vision, “data and AI first,” is already paying dividends. For example, the company has saved millions by using AI to more accurately forecast and manage inventory demand. 

The backstory you might need

Enterprise AI use has already doubled since 2017, but few companies are seeing significant return on their upfront costs, and a majority have failed to scale AI beyond the pilot stage. Analysts say the reasons include a lack of skills, complex programming models, upfront costs, and a lack of business alignment.

What you can do now

Take cues from Schneider Electric: 

• Formalise AI efforts under one senior executive 
• Understand the immense impact of AI – this is not like any technology that’s come before
• Hire dedicated AI and data experts
• Consider creating an AI centre of excellence to work with business unit leaders on AI projects

AI success requires AI at scale

Schneider had already been using AI in a decentralised fashion for years when, in 2021, it began its AI at Scale initiative and appointed its first Chief AI Officer, Philippe Rambach, to formalise its AI strategy.

Madhu Hosadurga, global vice president of enterprise AI at Schneider, said it’s important to have such a top-down approach.  

“If you want to drive AI at scale and get value from it, top management has to motivate it as a corporate-wide objective,” said Hosadurga. “Without the C-suite, everyone tries different things at a departmental and individual level.”

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He said a departmental approach typically involves highly technical people that understand the technology but “lack the influence and power to make change management happen.” 

Bring business and tech leaders together to scale AI

The company has implemented a global hub and spoke AI operating model. Each business function “spoke” (marketing, sales, service, etc.) has an AI product owner and change agent who works with the tech competency centre “hub” to find new uses for AI, deliver the technology, and ensure employee adoption. The hub is comprised mainly of technologists who help the business leaders identify AI opportunities and put them into use. 

For example, supply chain leaders wanted to use AI for, among other things, balancing inventory based on projected demand, and its ability to deliver based on those projections. With 200 factories and tens of thousands of suppliers, it’s impossible for humans to ensure optimal inventory levels, Hosadurga said. 

AI analytics and predictive modeling helped it reduce inventory levels to avoid a glut while balancing its ability to efficiently deliver products like transformers, switches, and prefabricated substations. He said that improvement alone has resulted in about $15 million in savings, measured by how much excess inventory it reduced, and capital allocated to other projects. 

“We targeted $5 million to $10 million in value, so that was a pleasant surprise,” he said, adding that it plans to use new AI capabilities to pare an additional five percent of inventory. 

Hire AI and data experts for better decision-making

Schneider’s AI at Scale program included adding more than 200 AI and data experts. These two are inexorably linked, as AI is the linchpin to extracting more value from data and therefore making better, faster decisions. 

For many business leaders, it’s still a challenge. Salesforce research shows a deep disconnect between business leaders and their data. Half of business leaders lack understanding of data because it’s complex or not accessible, and the vast majority aren’t using it to make better decisions. 

According to Yuval Atsmon, senior partner at McKinsey, this is a missed opportunity. 

“For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today,” he said on a recent podcast.  

It’s extremely hard to synthesise huge amounts of data, let alone detect patterns, make recommendations and predictions. This is the promise of AI-driven systems. 

Hosadurga offered this advice for companies looking to formalise their own AI program:

• Bring AI to the mainstream. Don’t view it as just another tool in your tech toolbox but as a new business capability that can change the way you operate, sell to customers, and enhance your employee experience.

• Organise with IT and business partnering from the get-go. Often, AI is relegated to the IT team. When that happens, IT will ask the business for a use case, but the business usually doesn’t know what to do with AI. At Schneider, people come together from both sides, with a mix of about 70% business and 30% tech. 

• Don’t wait until your data is perfect, in terms of quality and being all in one place, before embarking on a companywide AI initiative. “Many organizations believe they can’t use AI without perfect data,,” Hosadurga said, “but it’s more of a mindset issue where each business use case has to find the data, which is there in one form or another or in different places.” 

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AI is not like other technology

Business people dominate most AI projects at Schneider, Hosadurga said, which is one thing that makes it different from any other technology project. 

“Every use case — and we have use cases in almost every function — has people from both the AI Hub and business,” Hosadurga said.

It’s entirely possible to deliver AI at scale, but unlike some other major business technologies, AI requires an entrepreneur’s mindset.

“If you look at a typical IT culture, things are well defined, you know what you get from them and they can be programmed with a long-term plan,” he said. “But AI tools move so fast that it requires a very agile, quick-win, fail-fast culture. We operate more like a standup where we find an idea, incubate it quickly, and move on to the next phase.” 

Schneider Electric, which invests tens of millions of dollars in AI each year, plans to apply more AI and automation to its finance, sales, marketing, IT, and human resources functions over the next year. The company has launched an AI knowledge library, featuring blogs, ebooks, podcasts, training, courses, and other resources, prepared by its AI experts, so others can learn from its experience. 

“It’s as applicable as Excel in business,” Hosadurga said “It’s everywhere.” 

How Schneider Electric generates up to 500 sales opportunities a day

The company created a Digital Opportunity Factory that uses AI to identify sales prospects with an unmet need.

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What you need to know

What your company can do now

• Review generative AI products on the market and see what makes sense for your business. 
• Ask: Do I need to build it internally or work with a third-party vendor, like Salesforce, to add its products to our tech stack?
• Be aware of any risks.

The exec summary

The climate around GAI is moving at breakneck speed and regulators are trying to understand how the technology may affect businesses and the public. Here are some recent headlines:

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Responsible Creation of Artificial Intelligence

The backstory on generative AI regulations 

Concerns around artificial intelligence (AI) date back years when discussions covered possible job loss, inequality, bias, security issues, and more. With the rapid growth of generative AI after the public launch of ChatGPT in November 2022, new flags include:

• Privacy issues and data mining: Companies need to have transparency around where they’re gathering data and how they’re using it. 
• Copyright concerns: Because GAI tools pull from vast data sources, the chance of plagiarism increases. 
• Misinformation: False information could spread more quickly with AI chatbots, which also have created entirely inaccurate stories called hallucinations.
• Identity verification: Is what you’re reading created by a human or chatbot? There is the need to verify articles, social media posts, art, and more.
• Child protection: There’s been a call to ensure children and teenagers are protected against alarming, AI-generated content on social media.

This has all prompted regulators worldwide to investigate how GAI tools collect data and produce outputs and how companies train the AI they’re developing. In Europe, countries have been swift to apply the General Data Protection Regulation (GDPR), which impacts any company working within the EU. It’s one of the world’s strongest legal privacy frameworks; the U.S. does not have a similar overarching privacy law. That may change, with calls for more generative AI regulations.. 

“These are a lot of the same concerns we’ve seen previously wash up on the shores of the technology industry,” Gilliam-Moore said. “Right now, regulatory efforts, including investigations, seem to focus on privacy, content moderation, and copyright concerns. A lot of this is already addressed in statute, so regulators are trying to make sure that this is fit for purpose for generative AI.”

What considerations do businesses need to make? 

Companies continue to wonder how these tools will impact their business. It’s not just what the technology is capable of, but also how regulation will play a role in how businesses use it. Where does the data come from? How is it being used? Are customers protected? Is there transparency?

No matter where your company does business or who you interact with — whether developing the technology for other companies to use or interacting directly with consumers — ensure you speak with lawyers who are following generative AI regulations and can help guide you through your process. 

“Talking with your trusted advisers is always a good first step in all of this,” Gilliam-Moore said. “Innovation is happening at an incredible speed. So the conversations we’re having now could become stale in the next six months.”

Regulators have been concerned about how companies collect data and how that information gets delivered to users. Having an acceptable use policy – an agreement between two or more people (like a business and its employees or a university and students) outlining proper use when accessing a corporate network or internet — can help safeguard compliance. In addition, it is important to show data provenance, a documented trail that can prove data’s origins and where it currently sits. 

“Without data, none of this works,” Gilliam-Moore said.

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How can small businesses stay compliant? 

Larger corporations can often invest in the research and development around the technology, especially to stay compliant. Smaller businesses may not have the resources to do their due diligence, so asking vendors and technology partners in their ecosystem the right questions becomes important. 

While Salesforce is taking steps to develop trusted generative AI for its customers, those customers also work with other vendors and processors. They need to stay aware of potential harms that may exist and not just trust blindly. Gilliam-Moore said smaller companies should ask questions including:

• Are you GDPR compliant? 
• Are you HIPAA, or whichever law regulates your industry, compliant?
• Do you have an acceptable use policy?
• What are your certifications? 
• What are your practices around data? 
• Do you have policies that try to provide guardrails around the deployment of this technology?

“If you’re a smaller company, you may need to rely upon the due diligence of your third-party service providers,” Gilliam-Moore said. “Look at the privacy protocols, the security procedures, what they identify as harms and safeguards. Pay close attention to that.”

The need for trusted AI regulation

AI development can come with risks. This is why Salesforce supports tailored, risk-based AI regulation. It differentiates contexts and uses of the technology and ensures the protection of individuals, builds trust, and encourages innovation

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But where do you start? How do you add large language models (LLMs) to your infrastructure to start powering these applications? Should you train your own LLM? Customise a pre-trained open-source model? Use existing models through APIs?

Training your own LLM is a daunting and expensive task. The good news is that you don’t have to. Using existing LLMs through APIs allows you to unlock the power of generative AI today, and deliver game-changing AI innovation fast.

How can a generic LLM generate relevant outputs for your company? By adding the right instructions and grounding data to the prompt, you can give an LLM the information it needs to learn “in context,” and generate personalised and relevant results, even if it wasn’t trained on your data.

Your data belongs to you, and passing it to an API provider might raise concerns about compromising sensitive information. That’s where the Einstein Trust Layer comes in. (More on this later.)

What is an LLM?

Large language models (LLMs) are a type of AI that can generate human-like responses by processing natural-language inputs.

Check out our AI glossary

In this blog post, we’ll review the different strategies to work with LLMs, and take a deeper look at the easiest and most commonly used option: using existing LLMs through APIs. 

As Salesforce’s SVP of technical audience relations, I often work with my team to test things out around the company. I’m here to take you through each option so you can make an informed decision.

1. Train your own LLM (Hint: You don’t have to)

Training your own model gives you full control over the model architecture, the training process, and the data your model learns from. For example, you could train your own LLM on data specific to your industry: This model would likely generate more accurate outputs for your domain-specific use cases than a general-purpose model. 

But training your own LLM from scratch has some drawbacks, as well:

• Time: It can take weeks or even months.
• Resources: You’ll need a significant amount of computational resources, including GPU, CPU, RAM, storage, and networking.
• Expertise: You’ll need a team of specialised Machine Learning (ML) and Natural Language Processing (NLP) engineers.
• Data security: LLMs learn from large amounts of data — the more, the better. Data security in your company, on the other hand, is often governed by the principle of least privilege: You give users access to only the data they need to do their specific job. In other words, the less data the better. Balancing these opposing principles may not always be possible.

2. Customise a pre-trained open-source model (Hint: You don’t have to)

Open-source models are pre-trained on large datasets and can be fine-tuned on your specific use case. This approach can save you a lot of time and money compared to building your own model. But even though you don’t start from scratch, fine-tuning an open-source model has some of the characteristics of the train-your-own-model approach: It still takes time and resources, you still need a team of specialised ML and NLP engineers, and you may still experience the data security tension described above.

3. Use existing models through APIs

The last option is to use existing models (from OpenAI, Anthropic, Cohere, Google, and others) through APIs. It’s by far the easiest and most commonly used approach to build LLM-powered applications. Why? 

• You don’t need to spend time and resources to train your own LLM.
• You don’t need specialised ML and NLP engineers.
• Because the prompt is built dynamically into users’ flow of work, it includes only data they have access to.

The downside of this approach? These models haven’t been trained on your contextual and private company data. So, in many cases, the output they produce is too generic to be really useful.

Get started with an LLM today

The Einstein 1 Platform gives you the tools you need to easily build your own LLM-powered applications.

Unlock your own LLM

A common technique called in-context learning can help you get around this. You can ground the model in your reality by adding relevant data to the prompt. 

For example, compare the two prompts below:

Prompt #1 (not grounded with company data):

Write an introduction email to the Acme CEO.

Prompt #2 (grounded with company data):

You are John Smith, Account Representative at Northern Trail Outfitters.

Write an introduction email to Lisa Martinez, CEO of ACME.

Acme has been a customer since 2021.

It buys the following product lines: Edge, Peak, Elite, Adventure.

Here is a list of Acme orders:

Winter Collection 2024: $375,286

Summer Collection 2023: $402,255

Winter Collection 2023: $357,542

Summer Collection 2022: $324,573

Winter Collection 2022: $388,852

Summer Collection 2021: $312,899

Because the model doesn’t have relevant company data, the output generated by the first prompt will be too generic to be useful. Adding customer data to the second prompt gives the LLM the information it needs to learn “in context,” and generate personalised and relevant output, even though it was not trained on that data.

The more grounding data you add to the prompt, the better the generated output will be. However, it wouldn’t be realistic to ask users to manually enter that amount of grounding data for each request. 

Luckily, Salesforce’s Prompt Builder can help you write these prompts grounded in your company data. This tool lets you create prompt templates in a graphical environment, and bind placeholder fields to dynamic data that’s available through the Record page, flows, Data Cloud, Apex calls, or API calls.

But adding company data to the prompt raises another issue: You may be passing private and sensitive data to the API provider, where it could potentially be stored or used to further train the model.

Use existing LLMs without compromising your data

This is where the Einstein Trust Layer comes into play. Among other capabilities, the Einstein Trust Layer lets you use existing models through APIs in a trusted way, without compromising your company data. Here’s how it works:

1. Secure gateway: Instead of making direct API calls, you use the Einstein Trust Layer’s secure gateway to access the model. The gateway supports different model providers and abstracts the differences between them. You can even plug in your own model if you used the train-your-own-model or customise approaches described above.
2. Data masking and compliance: Before the request is sent to the model provider, it goes through a number of steps including data masking, which replaces personal identifiable information (PII) data with fake data to ensure data privacy and compliance.
3. Zero retention: To further protect your data, Salesforce has zero retention agreements with model providers, which means providers will not persist or further train their models with data sent from Salesforce.
4. Demasking, toxicity detection, and audit trail: When the output is received from the model, it goes through another series of steps, including demasking, toxicity detection, and audit trail logging. Demasking restores the real data that was replaced by fake data for privacy. Toxicity detection checks for any harmful or offensive content in the output. Audit trail logging records the entire process for auditing purposes.

How the Einstein 1 Platform works

The Einstein 1 Platform abstracts the complexity of large language models. It helps you get started with LLMs today and establish a solid foundation for the future. The Einstein 1 Platform powers the next generation of Salesforce CRM applications (Sales, Service, Marketing, and Commerce), and provides you with the tools you need to easily build your own LLM-powered applications. Although Einstein 1 is architected to support the different strategies mentioned earlier (train your own model, customise an open-source model, or use an existing model through APIs), it is configured by default to use the “use existing models through APIs” strategy, which lets you unlock the power of LLMs today and provides you with the fastest path to AI innovation. 

The Einstein 1 Platform combination of Prompt Builder and the Einstein Trust Layer lets you take advantage of LLMs without having to train your own model:

• Prompt Builder lets you ground prompts in your company data without training a model on that data.
• The Einstein Trust Layer enables you to make API calls to LLMs without compromising that company data.

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“We know [current generative AI] has a tendency to not always give accurate answers, but it gives the answers incredibly confidently,” said Kathy Baxter, principal architect in Salesforce’s ethical AI practice. “So it can be difficult for individuals to know if they can trust the answers generative AI is giving them.” 

You might hear those in the computer science community call these inaccuracies confabulations. Why? Because they believe the psychological phenomenon of accidentally replacing a gap in your memory with a false story is a more accurate metaphor for generative AI’s habit of making mistakes. Regardless of how you refer to these AI blunders, if you’re using AI at work, you need to be aware of them and have a mitigation plan in place. 

The big trend

People have gotten excited (and maybe a little frightened, especially when used at work) about generative AI and large language models (LLMs). And with good reason. LLMs, usually in the form of a chatbot, can help you write better emails and marketing reports, prepare sales projections, and create quick customer service replies, among many other things. 

What is an LLM?

Large language models (LLMs) are a type of AI that can generate human-like responses by processing natural-language inputs. LLMs are trained on massive datasets, which gives them a deep understanding of a broad context of information. This allows LLMs to reason, make logical inferences, and draw conclusions. 

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In these business contexts, AI hallucinations may lead to inaccurate analytics, negative biases, and trust-eroding errors sent directly to your employees or customers. 

“[This] is a trust problem,” said Claire Cheng, senior director, data science and engineering, at Salesforce. “We want AI to help businesses rather than make the wrong suggestions, recommendations, or actions to negatively impact businesses.”

It’s complicated

Some in the industry see hallucinations more positively. Sam Altman, CEO of ChatGPT creator OpenAI, told Salesforce CEO Marc Benioff that the ability to even produce hallucinations shows how AI can innovate. 

“The fact that these AI systems can come up with new ideas, can be creative, that’s a lot of the power,” Altman said. “You want them to be creative when you want, and factual when you want, but if you do the naive thing and say, ‘Never say anything you’re not 100% sure about’ — you can get a model to do that, but it won’t have the magic people like so much.”

For now, it appears we can’t completely solve the problem of generative AI hallucinations without eradicating its “magic.” (In fact, some AI tech leaders predict hallucinations will never really go away.) So what’s a well-meaning business to do? If you’re adding LLMs into your daily work, here are four ways you can mitigate generative AI hallucinations.

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1. Use a trusted LLM to help reduce generative AI hallucinations

For starters, make every effort to ensure your generative AI platforms are built on a trusted LLM. In other words, your LLM needs to provide an environment for data that’s as free of bias and toxicity as possible. 

A generic LLM such as ChatGPT can be useful for less-sensitive tasks such as creating article ideas or drafting a generic email, but any information you put into these systems isn’t necessarily protected. 

“Many people are starting to look into domain-specific models instead of using generic large language models,” Cheng said. “You want to look at the trusted source of truth rather than trust the model to give you the response. Do not expect the LLM to be your source of truth because it’s not your knowledge base.”

When you pull information from your own knowledge base, you’ll have relevant answers and information at your fingertips more efficiently. There will also be less risk the AI system will make guesses when it doesn’t know an answer. 

“Business leaders really need to think, ‘What are the sources of truth in my organisation?’” said Khoa Le, vice president of Service Cloud Einstein and bots at Salesforce. “They might be information about customers or products. They might be knowledge bases that live in Salesforce or elsewhere. Knowing where and having good hygiene around keeping these sources of truth up to date will be super critical.”

2. Write more-specific AI prompts

Great generative AI outputs also start with great prompts. And you can learn to write better prompts by following some easy tips. Those include avoiding close-ended questions that produce yes or no answers, which limit the AI’s ability to provide more detailed information. Also, ask follow-up questions to prompt the LLM to get more specific or provide more detailed answers. 

You’ll also want to use as many details as possible to prompt your tool to give you the best response. As a guide, take a look at the below prompt, before and after adding specifics. 

• Before: Write a marketing campaign for sneakers.
• After: Write a marketing campaign for a new online sneaker store called Shoe Dazzle selling to Midwestern women between the ages of 30 and 45. Specify that the shoes are comfortable and colorful. The shoes are priced between $75 and $95 and can be used for various activities such as power walking, working out in a gym, and training for a marathon.

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3. Tell the LLM to be honest

Another game-changing prompt tip is to literally direct the large language model to be honest. 

“If you’re asking a virtual agent a question, in your prompt you can say, ‘If you do not know the answer, just say you do not know,’” Cheng said. 

For example, say you want to create a report that compares sales data from five large pharmaceutical companies. This information likely will come from public annual reports, but it’s  possible the LLM won’t be able to access the most current data. At the end of your prompt, add, “Do not answer if you can’t find the 2023 data” so the LLM  knows not to make up something if that data isn’t available.

You can also make the AI “show its work” or explain how it came to the answer that it did through techniques like chain of thought or tree of thought prompting. Research has shown that these techniques not only help with transparency and trust, but they also increase the AI’s ability to generate the correct response.

4. Lessen the impact on customers

Le offers some things to consider to protect your customers’ data and business dealings. 

• Be transparent. If you’re using a chatbot or virtual agent backed by generative AI, don’t pass the interface off as if customers are talking to a human. Instead, disclose the use of generative AI on your site. “It’s so important to be clear where this information comes from and what information you’re training it on,” Le said. ”Don’t try to trick the customer.”  
• Follow local laws and regulations. Some municipalities require you to allow end users to opt in to this technology; even if yours doesn’t, you may want to offer an opt-in. 
• Protect yourself from legal issues. Generative AI technology is new and changing rapidly. Work with your legal advisors to understand the latest issues and follow local regulations. 
• Make sure safeguards are in place. When selecting a model provider, make sure they have safeguards in place such as toxicity and bias detection, sensitive data masking, and prompt injection attack defenses like Salesforce’s Einstein Trust Layer.

Generative AI hallucinations are a concern, but not necessarily a deal breaker. Design and work with this new technology, but keep your eyes wide open about the potential for mistakes. When you’ve used your sources of truth and questioned the work, you can go into your business dealings with more confidence.

Get started with an LLM today

The Einstein 1 Platform gives you the tools you need to easily build your own LLM-powered applications. Work with your own model, customise an open-source model, or use an existing model through APIs. It’s all possible with Einstein 1.

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There’s a good reason: Generative AI represents a seismic shift in technology. It will mean big changes in your organisation, your workforce, business processes, skill requirements, and the tools you use. If you’re not already thinking about how generative AI will impact your business, it’s time to get on board.

“Generative AI will reshape how every team operates,” said Clara Shih, CEO of Salesforce AI. “Just like with the adoption of the Internet, generative AI represents a generational opportunity to raise the capabilities, skills, and potential of teams throughout the company.”

Here, Shih, along with Kathy Baxter, principal architect of responsible AI and tech, and Khoa Le, vice president of product management for Salesforce AI, discuss the importance of generative AI for business. They offer six key areas where generative AI can boost efficiency and productivity and give your company a competitive edge.

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Key takeaways:

• Generative AI will improve efficiency, enhance customer personalisation, and foster innovation.
• Companies that can safely and responsibly realise the full potential of generative AI will reshape their industries, win lifelong customers, and solidly establish themselves as market leaders. 

1. Increase content creation to improve efficiency and productivity

Generative AI can deliver first drafts in seconds. Your business will be able to produce copious high-quality content in less time. This increase in speed will transform sales communications, marketing campaigns, product documentation, and more. This will free you up to respond quickly to changing customer needs and give employees time to deepen customer relationships.

“To craft a perfect sales email, it might take even a top salesperson several hours or even days to collect all of the context about the customer, such as their marketing interactions, open service support issues, or what they have in their commerce basket,” said Shih. “Generative AI can do that in an instant for every seller in your organisation, as well as every service agent,  every marketing manager, every commerce manager, and every developer — not just the top few percent.”

2. Build better customer service to improve customer relationships

Companies are struggling to meet customers’ rising expectations for speedy solutions and increased personalisation. Instead of having customers lose patience waiting on hold while service reps dig for solutions, generative AI can quickly craft the exact response required by combining knowledge culled from multiple articles and sources. Then, it can automatically generate post-call summaries.

“The productivity boost we can get from these generative AI capabilities will allow us to create more capacity to focus on connecting and building relationships with customers,” Le said. “Think about being on the phone with a person who actually listens, hears you, understands you, and makes you feel truly seen.” 

“It’s incredibly powerful to be able to look within thousands of knowledge articles and be able to generate the best answer to a customer support question in a very short period of time,” Baxter added.

This opens up more time for you to actually talk to a customer, to get to know their issue, and address it quickly. Generative AI can fill in the white space faster and let service agents get back to having better conversations and being more personable. 

“This will completely revolutionise how businesses interact and build relationships with their customers,” Shih said. “Customer service with generative AI is not only more efficient, but also more rewarding for service reps and less frustrating for customers.”

3. Develop hyper-personalisation for better targeted customer experiences

By analysing customer data, preferences, and past interactions, generative AI will enable businesses to deliver custom content, recommendations, and communications to each individual. These tailor-made touch points will resonate more deeply with customers, helping create stronger, more personalised interactions.

“Every email, every customer service conversation, every marketing message will absolutely be much more personalised and relevant to the customer,” Le said. “That’s what generative AI is going to be able to deliver at scale. It’s ultimately about the dynamic, personalised experiences you’ll be able to deliver for your customers at every touch point.” 

4. Generate sales notes and emails and boost sales performance through data

For sales teams, generative AI can automatically generate call summaries and follow-up emails, freeing reps to focus on connecting with the customer. It will also help sales leaders measure the effectiveness of sales activities. 

For example, managers will be able to measure the performance of an email that was generated based on certain data. Did it actually cause opportunities to advance pipeline stages faster than before? Did it drive a higher average order price or a higher close rate? Generative AI can help refine sales processes and overall communications with customers.

“The holy grail of sales management has always been the question of how you can get the median sales rep performing like a top rep,” Shih said. “Now we have an opportunity to do so in a data-driven way that also feels authentic and personalised both to the rep and to the particular customer or prospect they’re reaching out to.”

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5. Expand creativity for marketing campaigns and product innovation

AI-generated design concepts will bolster creative teams and product designers with tools previously out of reach. This will enhance innovation and accelerate the time to market for new campaigns and products. Generative AI will strengthen the creative process, bringing ideas to life quickly. Human creativity will increase as team members evaluate and refine the results or quickly move on to newer concepts.

“Rather than AI taking over creative tasks, combining AI with human capabilities can actually amplify creativity,” Baxter said. “It’s all about how you apply the technology and create incentive structures to keep humans in the loop. It’s up to companies to make sure their employees are taking a critical eye to AI-generated content and identifying the places where it is additive and providing benefit.”

6. Automate repetitive coding to raise developers’ productivity

Generative AI’s ability to write code will allow developers to automate repetitive tasks. By using AI to create boilerplate code or apply common algorithms, teams can shorten timelines, ensure consistency, and minimise human error. At the same time, non-technical team members will be able to use no-code and low-code tools, so the ability to create applications won’t be limited to the engineering team.

“Business analysts can create their own AI-infused applications and integrate AI across different data sources,” Shih said. “This really speaks to the importance of being able to scale this out during this developer shortage.”

While it seems like there’s a lot ahead of you, getting set up with generative AI at its inception will help position your business to thrive more quickly. Follow these three steps to get up and running — and then keep building.

• Figure out where generative AI will fit in your business operation
• Understand the cost implications and ROI of generative AI, as well as the importance of investing in companies and tools that use trusted AI.
• Build a business use case that will prove its usefulness, and start planning how you will roll it out.
  

“This is absolutely such an interesting time,” said Le. “The tremendous change we’re experiencing with generative AI will allow people to spend far less time on dull, repetitive tasks, and more time on human things, like building relationships.”

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First, accelerated adoption of competing OTT (Over-The-Top) service offerings – such as voice calls and messaging through WhatsApp – have put downward pressure on consumer revenue. 

Second, increasing spends on generational technology advancements every few years – like 4G to 5G network upgrades and fibre rollouts to address increased data consumption demands – have put upward pressure on costs.

So it’s logical for CSPs to seek alternative strategies to maintain healthy margin levels and retain market foothold. Mergers and acquisitions (M&As) are a powerful strategy CSPs use to achieve this.

How M&As are reshaping the APAC communications market

APAC markets are a diverse mix of prepaid and postpaid, characterised by a blended mobile ARPU (Average Revenue Per User) – as low as USD $3 in Indonesia and India, and up to USD $30 in Australia. 

At the same time, the prepaid heavy markets have an extremely cost savvy subscriber base that churns easily from one provider to next. Consumers are lured by lower costs or value-added incentives such as free data roaming packages or unlimited local calls.

And most countries have high teledensities – as high as 145% in Singapore, 124.8% in Australia, and 113.9% in New Zealand – that leave limited headroom for net new customer acquisition.

These unique market dynamics have powered a surge in M&A activity that has reshaped the marketplace and created new market leaders throughout the region. 

True Corporation and Total Access Communication (dTac) in Thailand, for example, created a new company with an enterprise value exceeding USD $20 billion. 

That’s only one of several examples. Celcom and Digi in Malaysia formed the largest mobile services provider in the country. Indosat and Hutchinson in Indonesia created the country’s second largest service provider with more than 100 million subscribers. And the Telkomsel and IndiHome merger in Indonesia resulted in expected annualised savings of USD $330 million. 

In Australia, the merger between Vodafone Hutchison and TPG Telecom created an enterprise value of USD $4.9 billion for Vodafone. And in neighbouring New Zealand, 2degrees and Vocus joined forces to form the country’s third largest service provider with an annual turnover of more than USD $1 billion. 

The same can be seen in India. When Vodafone India and Idea Cellular merged a few years ago, the combined entity emerged as the market leader with nearly 400 million subscribers. 

M&As have enabled each of these players to establish a 50% or above market share in their markets of operation.  

Increasing customer stickiness and wallet share with M&As

In addition to an increase in market share, M&A activities typically enable CSPs to increase customer stickiness where subscribers use a mix of volatile prepaid services (such as mobile data) from one provider, and highly retentive postpaid services (like fibre broadband) from another. 

Tapping in on each party’s offerings generates cross-sell opportunities CSPs use to increase customer wallet share and retention.

On the spend side of the equation, M&A activities tend to free up capital by reducing or eliminating spend on overlapping infrastructure. 

CSPs can then choose to use such capital to develop and market innovative products in the information and communities technology (ICT) space (such enterprise apps, IoT, and data centres), or develop partnerships with other industry service providers (like digital banks and micro insurance companies).

The critical need for an integrated customer view

Achieving a successful M&A in the communications industry presents several challenges. For CSPs with legacy systems, realising the business benefits of an M&A requires the rationalisation and integration of business strategies, customer facing and internal functions, product offerings, business processes, and IT stacks. 

However, delivering high-quality customer service over the course of the rationalisation period – and beyond – is key to retaining customers across the merging companies. In scenarios where a customer is consuming products from both merging companies, having an integrated view of the customer becomes crucial to achieving this goal. 

Gavin Barfield, VP Solutions and CTO ASEAN at Salesforce, makes the point that M&As provide an opportunity to retire legacy technology and embrace modern technology stacks.

Solving the integration puzzle with a 360-degree customer view

Developing this integrated 360-degree customer view requires systems integration and normalisation of data across product offerings, sales transactions, inflight orders, customer’s assets, trouble ticket histories, and more.

Salesforce Customer 360 provides an integrated view of each customer, across multiple functions, products and systems. This view is what communications companies’ marketing, sales, contact centre and field  service teams require for day-to-day operations, and to maintain business-as-usual – or better. 

For example, marketing teams enabled with deep customer insights from Customer 360, can review customer segments, customer spend and preferences to develop attractive cross-sell and up-sell offers for the new acquired customer base. 

Sales and customer service teams can also review customer sentiment to inform meaningful conversations with customers from the merging organisations, and address customer concerns with the right insights at hand. 

Singtel in Singapore is one CSP that’s using such data insights to understand and prioritise its customers’ needs in a complex, hyper-connected and fast-changing world.

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Rationalising business processes with a connected CRM

Integrated CRM platforms also enable the rationalisation of business processes during M&A activity. When CRM platforms are served over a connected user interface, it enables seamless handovers across internal functions.

For example, when a sales representative requests pricing approvals for mobility and connectivity products, their manager uses the same connected interface to review and approve the pricing. Solution specialists use the same interface to review the overall solution construct for consistency and coherency. Sales Ops uses the same view to review quote accuracy, and can derive weekly forecast reports using a single data instance.

Such simplification allows identification of redundant or unnecessary business processes that are candidates for transformation during the rationalisation process.

Leveraging AI technologies to enable sales and service teams

Gavin Barfield is seeing more communications companies increasingly motivated to embrace generative artificial intelligence (AI) to lead innovation and stay ahead.

That’s largely because AI technologies provide relevant and contextual information – during the sales stages and customer service engagement – that enables sales and service teams to meet their customers where they are. Customer 360 uses Salesforce Einstein AI technologies to  leverage the full power of this kind of data analytics.

Let’s take an example of a high-value subscriber who uses different prepaid SIM cards for voice and mobile gaming from two merging CSPs. AI could potentially suggest an up-sell to a 5G plan with more voice minutes and gigabytes for this subscriber, and the latest bluetooth earphones to enhance the subscriber’s gaming experience. 

Additionally, based on customer demographics and preferences across similar customers, AI could suggest an Instagram and WhatsApp add-on for a few extra dollars. Such examples not only generate increased wallet share, but also project the CSP as an intelligent organisation that understands and wows its customer throughout the customer lifecycle. 

From business transformation, process harmonisation and operational streamlining to increasing customer delight and wallet share, CRM platforms help CSPs across all stages of the M&A journey, and ensure long-term business success.

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Who owns customer centricity? Ultimately, everyone in your organisation does. That means collaboration must be your top priority.

In a previous article from this series, we talked about the need for customer-centric business processes, which lie at the heart of any customer-focused business. How do you bring these processes to life? You have to act as a unified team across silos so employees closest to the end customer are empowered to create enduring relationships.

Customer-centric companies have silos, too. They have clear designations of who’s doing what for efficiency. But they empower distinct departments to share, collaborate, and deliver integrated customer experiences — down to even the most junior employees.

Empower every employee to deliver the full force of the company

There is a good reason for silos: speed and efficiency. In the past, you could leave that statement and move on, confident in its accuracy. In today’s — and definitely tomorrow’s — digital world, rigid silos might be fast and efficient at repetition, but they are slow, expensive, and critical of change. To move your company from product-centric to customer-centric, you can keep silos but you have to advance them with new tools, new language, and new culture to ensure they become flexible, hyper-collaborative, and sensitive to customer needs.

What happens when customers have a poor experience with your brand? They speak to the first person they get from the company, and they don’t care what department the person is in. Unfortunately, and all too often, the customer ends up having to navigate the org chart to get what they want. Employees are simply not empowered and connected to serve the customer’s needs.

This requires us to abandon the top-down organisational structures we’ve inherited from the early 20th century, with rigid divisions between sales, service, marketing, and IT. And in a world where your customer is at the centre of your organisation, what the highest-paid person in an organisation wants may not be as important as what the customer wants.

It’s time for flexible, flatter, and empowered team structures.

Customer-centric companies think carefully about the customer experience and develop a customer-first culture to improve it. They overlay this culture with the tools and information everyone needs to make it easy to collaborate across teams and departments in the service of the customer.  These same companies cultivate information transparency and celebrate team behaviour.

Thousands of our customers are born digital and customer-first. For them, the challenge is one of formalising the culture and collaboration tools as they scale. For thousands of larger companies, the issues are slightly different. They see immediate results, especially around NPS and CSAT scores when they formalise multidisciplinary teams dedicated to a location. This case study on T-Mobile shows how bringing together cross-functional teams delivers improved revenue, NPS scores, and customer retention.

Make a plan with internal stakeholders to get started

To start to make this discipline a reality, review your goals and KPIs with peers or departmental leaders outside your team — even those that don’t normally work together. Explore how you can help each other, and find goals you can share. Celebrate successes as a broader team as you step away from partitioned, traditional responsibilities.

Review the following list. How can you move from the department-specific approaches on the left to the customer-first set on the right? Have you listened to all perspectives, especially from those who are on the front lines with customers every day? Who is responsible for designing the end-to-end customer experience and where are the internal bottlenecks?

To become a customer company, you must have the mindset to evolve your business model, culture, and organisation. One example is the Chief Technology Officer of Edelman Financial Engines, who evolved his department from functional product teams to “customer journey” teams.

Technology is only an enabling platform for change. The real transformation takes place when everyone within the organisation shares the commitment to place the customer at the centre of everything.

For more information on building your team around your customer, check out the customer 360 playbook.

This post originally appeared on the U.S.-version of the Salesforce blog.

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