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Generative AI Will Change Your Business. Here’s How to Adapt.



It’s coming. Generative AI will change the nature of how we interact with all software, and given how many brands have significant software components in how they interact with customers, generative AI will drive and distinguish how more brands compete.

In our last HBR piece, “Customer Experience in the Age of AI,” we discussed how the use of one’s customer information is already differentiating branded experiences. Now with generative AI, personalization will go even further, tailoring all aspects of digital interaction to how the customer wants it to flow, not how product designers envision cramming in more menus and features. And then as the software follows the customer, it will go to places that range beyond the tight boundaries of a brand’s product. It will need to offer solutions to things the customer wants to do. Solve the full package of what someone needs, and help them through their full journey to get there, even if it means linking to outside partners, rethinking the definition of one’s offerings, and developing the underlying data and tech architecture to connect everything involved in the solution.

Generative AI can “generate” text, speech, images, music, video, and especially code. When that capability is joined with a feed of someone’s own information, used to tailor the when, what, and how of an interaction, then the ease by which someone can get things done, and the broadening accessibility of software, goes up dramatically. The simple input question box that stands at the center of Google and now, of most generative AI systems, such as in ChatGPT and DALL-E 2, will power more systems. Say goodbye to drop down menus in software, and the inherently guided restrictions they place on how you use them. Instead, you’ll just see: “What do you want to do today?” And when you tell it what you want to do, it will likely offer some suggestions, drawing upon its knowledge of what you did last time, what triggers the systems knows about your current context, and what you’ve already stored in the system as your core goals, such as “save for a trip,” “remodel our kitchen,” “manage meal plans for my family of five with special dietary needs,” etc.

Without the boundaries of a conventional software interface, consumers will just want to get done what they need, not caring whether the brand behind the software has limitations. The change in how we interact, and what we expect, will be dramatic, and dramatically more democratizing.

So much of the hype on generative AI has focused on its ability to generate text, images, and sounds, but it also can create code to automate actions, and to facilitate pulling in external and internal data. By generating code in response to a command, it facilitates the short cut for a user that takes them from a command to an action that simply just gets done. No more working through all of the menus in the software. Even questions into and analyses of the data stored in an application will be easily done just by asking: “Who are the contacts I have not called in the last 90 days?” or “When is the next time I am scheduled to be in NYC with an opening for dinner?” To answer these questions now, we have to go into an application and gather data (possibly manually) from outside of the application itself. Now, the query can be recognized, code created, possibilities ranked, and the best answer generated. In milliseconds.

This drastically simplifies how we interact with what we think of as today’s applications. It also enables more brands to build applications as part of their value proposition. “Given the weather, traffic, and who I am with, give me a tourist itinerary for the afternoon, with an ongoing guide, and the ability to just buy any tickets in advance to skip any lines.” “Here’s my budget, here’s five pictures of my current bathroom, here’s what I want from it, now give me a renovation design, a complete plan for doing it, and the ability to put it out for bid.” Who will create these capabilities? Powerful tech companies? Brands who already have relationships in their relevant categories? New, focused disruptors? The game is just starting, but the needed capabilities and business philosophies are already taking shape.

A Broader Journey with Broader Boundaries

In a world where generative AI, and all of the other evolving AI systems proliferate, building one’s own offering requires focusing on the broadest possible view of one’s pool of data, of the journeys you can enable, and the risks they raise:

Bring data together.

Solving for a customer’s complete need will require pulling from information across your company, and likely beyond your boundaries. One of the biggest challenges for most applications, and actually for most IT departments, is bringing data together from disparate systems. Many AI systems can write the code needed to understand the schemas of two different databases, and integrate them into one repository, which can save several steps in standardizing data schema. AI teams still need to dedicate time for data cleansing and data governance (arguably even more so), for example, aligning on the right definitions of key data features. However, with AI capabilities in hand, the next steps in the process to bring all the data together become easier.

Narrative AI, for example, offers a marketplace for buying and selling data, along with data collaboration software that allows companies to import data from anywhere into their own repositories, aligned to their schema, with merely a click. Data from across a company, from partners, or from sellers of data, can be integrated and then used for modeling in a flash.

Combining one’s own proprietary data with public data, data from other available AI tools, and from many external parties can serve to dramatically improve the AI’s ability to understand one’s context, predict what is being asked, and have a broader pool from which to execute a command.

The old rule around “garbage in, garbage out” still applies, however. Especially when it comes to integrating third-party data, it is important to cross-check the accuracy with internal data before integrating it into the underlying data set. For example, one fashion brand recently found that gender data purchased from a third-party source didn’t match its internal data 50% of the time, so the source and reliability really matters.

The “rules layer” becomes even more critical.

Without obvious restrictions on what a customer can ask for in an input box, the AI needs to have guidelines that ensure it responds appropriately to things beyond its means or that are inappropriate. This amplifies the need for a sharp focus on the rules layer, where the experience designers, marketers and business decision makers set the target parameters for the AI to optimize.

For example, for an airline brand that leveraged AI to decide on the “next best conversation” to engage in with customers, we set rules around what products could be marketed to which customers, what copy could be used in which jurisdictions, and rules around anti-repetition to ensure customers didn’t get bombarded with irrelevant messages.

These constraints become even more critical in the era of generative AI. As pioneers of these solutions are finding, customers will be quick to point out when the machine “breaks” and produces non-sensical solutions. The best approaches will therefore start small, will be tailored to specific solutions where the rules can be tightly defined and human decision makers will be able to design rules for edge cases.

Deliver the end to end journey, and the specific use cases involved.

Customers will just ask for what they need, and will seek the simplest and/or most cost-effective way to get it done. What is the true end goal of the customer? How far can you get? With the ability to move information more easily across parties, you can build partnerships for data and for execution of the actions to help a customer through their journey, therefore, your ecosystem of business relationships will differentiate your brand.

In his impressive demo of how Hubspot is incorporating generative AI into “ChatSpot,” Dharmesh Shah, CTO and founder of Hubspot, lays out how they are mingling the capabilities of HubSpot with OpenAI, and with other tools. Not only does he show Hubspot’s interface reduced to just a single text entry prompt, but he also shows new capabilities that extend well beyond Hubspot’s current borders. A salesperson seeking to send an email to a business leader at a target company can use ChatSpot to perform research on the company, on the target business leader, and then draft an email that incorporates both information from the research and from what it knows about the salesperson themselves. The resulting email draft can then be edited, sent, and tracked by HubSpot’s system, and the target business leader automatically entered into a contact database with all associated information.

The power of connected information, automatic code creation, and generated output is leading many other companies to extend their borders, not as conventional “vertical,” or “horizontal” expansion, but as “journey expansion.” When you can offer “services” based on a simple user command, those commands will reflect the customer’s true goal and the total solution they seek, not just a small component that you may have been dealing with before.

Differentiate via your ecosystem.

Solving for those broader needs inevitably will pull you into new kinds of partner relationships. As you build out your end-to-end journey capabilities, how you construct those business relationships will be critical new bases for strategy. How trustworthy, how well permissioned, how timely, how comprehensive, how biased is their data. How will they use data your brand sends out? What is the basis of your relationship, quality control, and data integration? Pre-negotiated privileged partnerships? A simple vendor relationship? How are you charging for the broader service and how will the parties involved get their cut?

Similar to how search brands like Google, ecommerce marketplaces like Amazon, and recommendation engines such as Trip Advisor become gateways for sellers, more brands can become front-end navigators for a customer journey if they can offer quality partners, experience personalization, and simplicity. CVS could become a full health network coordinator that health providers, health tech, wellness services, pharma, and other support services will plug into. When its app can let you simply ask: “How can you help me lose 30 pounds,” or “How can you help me deal with my increasing arthritis,” the end-to-end program they can generate and then completely manage, through prompts to you and information passed around their network, will be a critical differentiator in how they, as a brand, build loyalty, capture your data, and use that to keep increasing service quality.

Prioritize safety, fairness, privacy, security, and transparency.

The way you manage data becomes part of your brand, and the outcomes for your customers will have edge cases and bias risks that you should seek out and mitigate. We are all reading stories of how people are pushing Generative AI systems, such as ChatGPT, to extremes, and getting back what the application’s developers call “hallucinations,” or bizarre responses. We are also seeing responses that come back as solid assertions of wrong facts. Or responses that are derived from biased bases of data that can lead to dangerous outcomes for some populations. Companies are also getting “outed” for sharing private customer information with other parties, without customer permissions, clearly not for the benefit of their customers per se.

The risks — from the core data, to the management of data, to the nature of the output of the generative AI — will simply keep multiplying. Some companies, such as American Express, have created new positions for chief customer protection officers, whose role is to stay ahead of potential risk scenarios, but more importantly, to build safeguards into how product managers are developing and managing the systems. Risk committees on corporate boards are already bringing in new experts and expanding their purviews, but more action has to happen pre-emptively. Testing data pools for bias, understanding where data came from and its copyright/accuracy/privacy risks, managing explicit customer permissions, limiting where information can go, and constantly testing the application for edge cases where customers could push it to extremes, are all critical processes to build into one’s core product management discipline, and into the questions that top management routinely has to ask. Boards will expect to see dashboards on these kinds of activities, and other external watchdogs, including lawyers representing legal challenges, will demand them as well.

Is it worth it? The risks will constantly multiply, and the costs of creating structures to manage those risks will be real. We’ve only begun to figure out how to manage bias, accuracy, copyright, privacy, and manipulated ranking risks at scale. The opacity of the systems often makes it impossible to explain how an outcome happened if some kind of audit is necessary.

But nonetheless, the capabilities of generative AI are not only available, they are the fastest growing class of applications ever. The accuracy will improve as the pool of tapped data increases, and as parallel AI systems as well as “humans in the loop” work to find and remedy those nasty “hallucinations.”.

The potential for simplicity, personalization, and democratization of access to new and existing applications will pull in not only hundreds of start-ups, but will also tempt many established brands into creating new AI-forward offerings. If they can do more than just amuse, and actually take a customer through more of the requirements of their journey than ever before, and do so in a way that inspires trust, brands could open up new sources of revenue from the services they can enable beyond their currently narrow borders. For the right use cases, speed and personalization could possibly be worth a price premium. But more likely, the automation abilities of AI will pull costs out of the overall system and put pressure on all participants to manage efficiently, and compete accordingly.

We are now opening up a new dialogue between brands and their customers. Literally. Not like the esoteric descriptions of what happened in the earlier days of digital interaction. Now we are talking back and forth. Getting things done. Together. Simply. In a trustworthy fashion. Just how the customer wants it. The race is on to see which brands can deliver.


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Revolutionizing Marketing: The Power of AI in the Digital Age



Embracing AI-Powered Marketing: Transforming Brands in the Digital Marketplace

In the crowded digital marketplace, standing out is challenging. Enter AI-powered marketing, a revolutionary upgrade transforming brands into digital powerhouses.

Hyper-Personalized Campaigns: Beyond Basic Personalization

Gone are the days of generic marketing. Today’s gold standard is AI-driven hyper-personalization. This approach uses customer data analysis to create deeply resonant, individualized marketing campaigns. With AI’s ability to segment audiences based on intricate criteria, including purchasing history and browsing behavior, your messages can hit the mark every time.

Enhanced Customer Journey Mapping

AI’s capabilities extend to mapping the entire customer journey. By predicting needs and preferences at each stage, AI aids in crafting narratives that guide customers from discovery to purchase, integrating your brand into their personal stories.

SEO Wizardry: Mastering Search Engine Dynamics

With ever-changing algorithms, SEO is a complex puzzle. AI serves as a sophisticated navigator, deciphering these changes through machine learning. It aids in keyword optimization, understanding search intent, and aligning content with search trends.

Predictive SEO

AI tools offer predictive SEO, anticipating search engine and user behavior changes. This proactive stance ensures your brand’s prominent visibility in search results, capturing the right audience at the right time.

Social Media Mastery: Crafting a Digital Narrative

AI transforms social media strategies from uncertain to precise. By analyzing vast social data, AI provides insights into resonating content.

Content Optimization

AI analyzes performance data to recommend effective content types. This data-driven approach refines your social media content strategy.

Engagement Analysis

AI examines user interaction nuances, understanding engagement patterns. It helps tailor interactions for maximum impact, including adjusting posting schedules and messaging for increased relevance.

Conclusion: Navigating the AI-Driven Marketing Landscape

AI-powered marketing is essential for thriving in the digital age, offering precision and personalization beyond traditional methods. For small businesses, it’s a chance to leverage AI for impactful, data-driven strategies.

As we embrace the AI revolution, the future of marketing is not just bright but intelligently radiant. With AI as your digital ally, your brand is equipped for a successful journey, making every marketing effort and customer interaction count.

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AI: Your Small Business Ally in a Digital Age



In the ever-evolving landscape of modern commerce, small business owners find themselves at a crossroads of opportunity and obsolescence. Enter Artificial Intelligence (AI) – once the exclusive domain of tech behemoths, it now stands as the great equalizer, offering small businesses a competitive edge previously unthinkable. The emergence of AI as a wingman for small businesses is not just a fleeting trend but a fundamental shift in how entrepreneurs can leverage technology to revolutionize their operations.

The 24/7 Customer Service Hero: Chatbots

In the digital storefront, customer service is the heartbeat of business survival and success. Chatbots emerge as the indefatigable heroes of this domain. Envision a customer service agent that never clocks out an entity that requires no sleep or sustenance yet delivers consistently and instantaneously. These AI-driven chat interfaces embody the essence of your brand’s voice, capable of handling a barrage of customer queries with a speed that outpaces the swiftest of typists. They are the embodiment of efficiency – ensuring that customer satisfaction is not just met but exceeded around the clock.

Unearthing Market Treasures: Data Dive

AI’s prowess in pattern recognition has catapulted data analytics into a realm once considered the stuff of science fiction. Small business owners armed with AI tools can sift through vast swathes of data to extract actionable insights. These algorithms act as modern-day oracles, predicting market trends, discerning customer behaviors, and offering sales forecasts with remarkable accuracy. Equipped with: this knowledge, small businesses, can navigate the market with the foresight and precision of an experienced captain steering through foggy seas.

Personalization at Scale: Customize Like a Boss

The age-old business mantra of the customer is king is given new potency with AI’s personalization capabilities. Tailoring the customer experience is no longer a luxury but a necessity. AI enables small businesses to offer bespoke experiences to consumers, making them feel like the sole focus of their attention. It’s personalization executed with such finesse that customers are left marveling at the thoughtfulness and individual attention, fostering loyalty and establishing deep-rooted brand connections.

Offloading the Mundane: Task Slayers

Repetitive tasks are the bane of creativity and innovation. AI steps in as the ultimate task slayer, automating routine chores that once consumed disproportionate amounts of time. From scheduling appointments to managing inventory, AI liberates entrepreneurs from the drudgery of administrative duties, freeing them to refocus on the creative and strategic endeavors that propel business growth.

Mastering Social Media: Social Savants

Social media – the pulsing vein of modern marketing – demands astuteness and agility. AI emerges as the savant of social media, capable of demystifying platform algorithms to optimize content delivery. It knows the optimal times to post, the types of content that resonate with audiences, and the strategies that convert passive scrollers into engaged customers. By automating your social media presence, AI transforms your brand into an online sensation, cultivating a digital community of brand ambassadors.

The Verdict: Embracing AI

For a small business owner, AI is not about an overnight overhaul but strategic integration. The goal is to start small, allowing AI to shoulder incremental aspects of your business, learning and scaling as you witness tangible benefits. The transition to AI-enablement does not necessitate a background in technology; it requires a willingness to embrace change and a vision for the future.

In summary, as the digital revolution marches forward, AI stands ready to partner with small businesses, providing them with tools once deemed the province of giants. This partnership promises to elevate the small business landscape, ushering in an era of democratized technology where every entrepreneur can harness the power of AI to write their own David vs. Goliath success story. AI, the once-distant dream, is now the most loyal wingman a small business can enlist in its quest for growth and innovation.

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How to Train Generative AI Using Your Company’s Data



Many companies are experimenting with ChatGPT and other large language or image models. They have generally found them to be astounding in terms of their ability to express complex ideas in articulate language. However, most users realize that these systems are primarily trained on internet-based information and can’t respond to prompts or questions regarding proprietary content or knowledge.

Leveraging a company’s propriety knowledge is critical to its ability to compete and innovate, especially in today’s volatile environment. Organizational Innovation is fueled through effective and agile creation, management, application, recombination, and deployment of knowledge assets and know-how. However, knowledge within organizations is typically generated and captured across various sources and forms, including individual minds, processes, policies, reports, operational transactions, discussion boards, and online chats and meetings. As such, a company’s comprehensive knowledge is often unaccounted for and difficult to organize and deploy where needed in an effective or efficient way.

Emerging technologies in the form of large language and image generative AI models offer new opportunities for knowledge management, thereby enhancing company performance, learning, and innovation capabilities. For example, in a study conducted in a Fortune 500 provider of business process software, a generative AI-based system for customer support led to increased productivity of customer support agents and improved retention, while leading to higher positive feedback on the part of customers. The system also expedited the learning and skill development of novice agents.

Like that company, a growing number of organizations are attempting to leverage the language processing skills and general reasoning abilities of large language models (LLMs) to capture and provide broad internal (or customer) access to their own intellectual capital. They are using it for such purposes as informing their customer-facing employees on company policy and product/service recommendations, solving customer service problems, or capturing employees’ knowledge before they depart the organization.

These objectives were also present during the heyday of the “knowledge management” movement in the 1990s and early 2000s, but most companies found the technology of the time inadequate for the task. Today, however, generative AI is rekindling the possibility of capturing and disseminating important knowledge throughout an organization and beyond its walls. As one manager using generative AI for this purpose put it, “I feel like a jetpack just came into my life.” Despite current advances, some of the same factors that made knowledge management difficult in the past are still present.

The Technology for Generative AI-Based Knowledge Management

The technology to incorporate an organization’s specific domain knowledge into an LLM is evolving rapidly. At the moment there are three primary approaches to incorporating proprietary content into a generative model.

Training an LLM from Scratch

One approach is to create and train one’s own domain-specific model from scratch. That’s not a common approach, since it requires a massive amount of high-quality data to train a large language model, and most companies simply don’t have it. It also requires access to considerable computing power and well-trained data science talent.

One company that has employed this approach is Bloomberg, which recently announced that it had created BloombergGPT for finance-specific content and a natural-language interface with its data terminal. Bloomberg has over 40 years’ worth of financial data, news, and documents, which it combined with a large volume of text from financial filings and internet data. In total, Bloomberg’s data scientists employed 700 tokens, or about 350 billion words, 50 billion parameters, and 1.3 million hours of graphics processing unit time. Few companies have those resources available.

Fine-Tuning an Existing LLM

A second approach is to “fine-tune” train an existing LLM to add specific domain content to a system that is already trained on general knowledge and language-based interaction. This approach involves adjusting some parameters of a base model, and typically requires substantially less data — usually only hundreds or thousands of documents, rather than millions or billions — and less computing time than creating a new model from scratch.

Google, for example, used fine-tune training on its Med-PaLM2 (second version) model for medical knowledge. The research project started with Google’s general PaLM2 LLM and retrained it on carefully curated medical knowledge from a variety of public medical datasets. The model was able to answer 85% of U.S. medical licensing exam questions — almost 20% better than the first version of the system. Despite this rapid progress, when tested on such criteria as scientific factuality, precision, medical consensus, reasoning, bias and harm, and evaluated by human experts from multiple countries, the development team felt that the system still needed substantial improvement before being adopted for clinical practice.

The fine-tuning approach has some constraints, however. Although requiring much less computing power and time than training an LLM, it can still be expensive to train, which was not a problem for Google but would be for many other companies. It requires considerable data science expertise; the scientific paper for the Google project, for example, had 31 co-authors. Some data scientists argue that it is best suited not to adding new content, but rather to adding new content formats and styles (such as chat or writing like William Shakespeare). Additionally, some LLM vendors (for example, OpenAI) do not allow fine-tuning on their latest LLMs, such as GPT-4.

Prompt-tuning an Existing LLM

Perhaps the most common approach to customizing the content of an LLM for non-cloud vendor companies is to tune it through prompts. With this approach, the original model is kept frozen, and is modified through prompts in the context window that contain domain-specific knowledge. After prompt tuning, the model can answer questions related to that knowledge. This approach is the most computationally efficient of the three, and it does not require a vast amount of data to be trained on a new content domain.

Morgan Stanley, for example, used prompt tuning to train OpenAI’s GPT-4 model using a carefully curated set of 100,000 documents with important investing, general business, and investment process knowledge. The goal was to provide the company’s financial advisors with accurate and easily accessible knowledge on key issues they encounter in their roles advising clients. The prompt-trained system is operated in a private cloud that is only accessible to Morgan Stanley employees.

While this is perhaps the easiest of the three approaches for an organization to adopt, it is not without technical challenges. When using unstructured data like text as input to an LLM, the data is likely to be too large with too many important attributes to enter it directly in the context window for the LLM. The alternative is to create vector embeddings — arrays of numeric values produced from the text by another pre-trained machine learning model (Morgan Stanley uses one from OpenAI called Ada). The vector embeddings are a more compact representation of this data which preserves contextual relationships in the text. When a user enters a prompt into the system, a similarity algorithm determines which vectors should be submitted to the GPT-4 model. Although several vendors are offering tools to make this process of prompt tuning easier, it is still complex enough that most companies adopting the approach would need to have substantial data science talent.

However, this approach does not need to be very time-consuming or expensive if the needed content is already present. The investment research company Morningstar, for example, used prompt tuning and vector embeddings for its Mo research tool built on generative AI. It incorporates more than 10,000 pieces of Morningstar research. After only a month or so of work on its system, Morningstar opened Mo usage to their financial advisors and independent investor customers. It even attached Mo to a digital avatar that could speak out its answers. This technical approach is not expensive; in its first month in use, Mo answered 25,000 questions at an average cost of $.002 per question for a total cost of $3,000.

Content Curation and Governance

As with traditional knowledge management in which documents were loaded into discussion databases like Microsoft Sharepoint, with generative AI, content needs to be high-quality before customizing LLMs in any fashion. In some cases, as with the Google Med-PaLM2 system, there are widely available databases of medical knowledge that have already been curated. Otherwise, a company needs to rely on human curation to ensure that knowledge content is accurate, timely, and not duplicated. Morgan Stanley, for example, has a group of 20 or so knowledge managers in the Philippines who are constantly scoring documents along multiple criteria; these determine the suitability for incorporation into the GPT-4 system. Most companies that do not have well-curated content will find it challenging to do so for just this purpose.

Morgan Stanley has also found that it is much easier to maintain high quality knowledge if content authors are aware of how to create effective documents. They are required to take two courses, one on the document management tool, and a second on how to write and tag these documents. This is a component of the company’s approach to content governance approach — a systematic method for capturing and managing important digital content.

At Morningstar, content creators are being taught what type of content works well with the Mo system and what does not. They submit their content into a content management system and it goes directly into the vector database that supplies the OpenAI model.

Quality Assurance and Evaluation

An important aspect of managing generative AI content is ensuring quality. Generative AI is widely known to “hallucinate” on occasion, confidently stating facts that are incorrect or nonexistent. Errors of this type can be problematic for businesses but could be deadly in healthcare applications. The good news is that companies who have tuned their LLMs on domain-specific information have found that hallucinations are less of a problem than out-of-the-box LLMs, at least if there are no extended dialogues or non-business prompts.

Companies adopting these approaches to generative AI knowledge management should develop an evaluation strategy. For example, for BloombergGPT, which is intended for answering financial and investing questions, the system was evaluated on public dataset financial tasks, named entity recognition, sentiment analysis ability, and a set of reasoning and general natural language processing tasks. The Google Med-PaLM2 system, eventually oriented to answering patient and physician medical questions, had a much more extensive evaluation strategy, reflecting the criticality of accuracy and safety in the medical domain.

Life or death isn’t an issue at Morgan Stanley, but producing highly accurate responses to financial and investing questions is important to the firm, its clients, and its regulators. The answers provided by the system were carefully evaluated by human reviewers before it was released to any users. Then it was piloted for several months by 300 financial advisors. As its primary approach to ongoing evaluation, Morgan Stanley has a set of 400 “golden questions” to which the correct answers are known. Every time any change is made to the system, employees test it with the golden questions to see if there has been any “regression,” or less accurate answers.

Legal and Governance Issues

Legal and governance issues associated with LLM deployments are complex and evolving, leading to risk factors involving intellectual property, data privacy and security, bias and ethics, and false/inaccurate output. Currently, the legal status of LLM outputs is still unclear. Since LLMs don’t produce exact replicas of any of the text used to train the model, many legal observers feel that “fair use” provisions of copyright law will apply to them, although this hasn’t been tested in the courts (and not all countries have such provisions in their copyright laws). In any case, it is a good idea for any company making extensive use of generative AI for managing knowledge (or most other purposes for that matter) to have legal representatives involved in the creation and governance process for tuned LLMs. At Morningstar, for example, the company’s attorneys helped create a series of “pre-prompts” that tell the generative AI system what types of questions it should answer and those it should politely avoid.

User prompts into publicly-available LLMs are used to train future versions of the system, so some companies (Samsung, for example) have feared propagation of confidential and private information and banned LLM use by employees. However, most companies’ efforts to tune LLMs with domain-specific content are performed on private instances of the models that are not accessible to public users, so this should not be a problem. In addition, some generative AI systems such as ChatGPT allow users to turn off the collection of chat histories, which can address confidentiality issues even on public systems.

In order to address confidentiality and privacy concerns, some vendors are providing advanced and improved safety and security features for LLMs including erasing user prompts, restricting certain topics, and preventing source code and propriety data inputs into publicly accessible LLMs. Furthermore, vendors of enterprise software systems are incorporating a “Trust Layer” in their products and services. Salesforce, for example, incorporated its Einstein GPT feature into its AI Cloud suite to address the “AI Trust Gap” between companies who desire to quickly deploy LLM capabilities and the aforementioned risks that these systems pose in business environments.

Shaping User Behavior

Ease of use, broad public availability, and useful answers that span various knowledge domains have led to rapid and somewhat unguided and organic adoption of generative AI-based knowledge management by employees. For example, a recent survey indicated that more than a third of surveyed employees used generative AI in their jobs, but 68% of respondents didn’t inform their supervisors that they were using the tool. To realize opportunities and manage potential risks of generative AI applications to knowledge management, companies need to develop a culture of transparency and accountability that would make generative AI-based knowledge management systems successful.

In addition to implementation of policies and guidelines, users need to understand how to safely and effectively incorporate generative AI capabilities into their tasks to enhance performance and productivity. Generative AI capabilities, including awareness of context and history, generating new content by aggregating or combining knowledge from various sources, and data-driven predictions, can provide powerful support for knowledge work. Generative AI-based knowledge management systems can automate information-intensive search processes (legal case research, for example) as well as high-volume and low-complexity cognitive tasks such as answering routine customer emails. This approach increases efficiency of employees, freeing them to put more effort into the complex decision-making and problem-solving aspects of their jobs.

Some specific behaviors that might be desirable to inculcate — either though training or policies — include:

  • Knowledge of what types of content are available through the system;
  • How to create effective prompts;
  • What types of prompts and dialogues are allowed, and which ones are not;
  • How to request additional knowledge content to be added to the system;
  • How to use the system’s responses in dealing with customers and partners;
  • How to create new content in a useful and effective manner.

Both Morgan Stanley and Morningstar trained content creators in particular on how best to create and tag content, and what types of content are well-suited to generative AI usage.

“Everything Is Moving Very Fast”

One of the executives we interviewed said, “I can tell you what things are like today. But everything is moving very fast in this area.” New LLMs and new approaches to tuning their content are announced daily, as are new products from vendors with specific content or task foci. Any company that commits to embedding its own knowledge into a generative AI system should be prepared to revise its approach to the issue frequently over the next several years.

While there are many challenging issues involved in building and using generative AI systems trained on a company’s own knowledge content, we’re confident that the overall benefit to the company is worth the effort to address these challenges. The long-term vision of enabling any employee — and customers as well — to easily access important knowledge within and outside of a company to enhance productivity and innovation is a powerful draw. Generative AI appears to be the technology that is finally making it possible.


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