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Using AI to Adjust Your Marketing and Sales in a Volatile World



Much has been written over the years about how firms lack visibility into the returns from their marketing investments. In an analog world, the perennial reason offered for this problem was difficulty establishing a causal link between investments made in marketing activities and the market (or customer) response to those actions.

In the digital world, a common way to build causal links is by running a large number of relatively cheap experiments through which firms can connect marketing and sales actions to a customer response. Firms can track customer responses throughout the journey from search to click to purchase, and even to consumption. The result has been an exponential increase in the amount of data on that journey to which firms have access.

We wanted to know why some firms are much better and faster than others at adapting their use of customer data to respond to changing or uncertain marketing conditions. Especially during the initial months of the pandemic in 2020, and more recently in 2022, when recessionary forces began to affect the nature of customer demand, some firms were able to analyze the burgeoning customer journey data and pivot, adapting their marketing and sales efforts much faster than their competitors. We have observed a common thread across these fast-acting firms is their use of AI models to predict outcomes at various stages of the customer journey — for example, using AI to analyze historical consumer behavior data and predict the likelihood of a customer responding favorably to a marketing campaign.

What else do we see happening in these firms? First, while their competitors respond reactively to actions taken by customers, these firms are taking a proactive approach to managing their customer relationships. They’re using AI to predict which customers are likely to churn and what corrective action can be taken to prevent the customer from defecting, while their competitors react after the customers have already left. And when their predictions go off track because of external changes or market conditions, they use that feedback to quickly reorient and redirect their marketing and sales efforts. Using AI models to predict customer response translated, in effect, to designing and running a large number of experiments that helped these firms respond to market changes faster than firms not using those tools.

Prediction Models Are Changing how Strategy Works

Consider the example of a global trading firm engaged in the sourcing and distribution of commodity bulk chemicals. In early 2019 the firm began using AI-based prediction models to understand the flow of opportunities through the various stages of clients’ RFP-based buying processes. The firm learned that quality-related factors were primary determinants of getting short-listed by clients. They began using this information to selectively pursue client opportunities.

By May 2020, however, the company’s AI-model predictions were proving to be wrong. Further analysis revealed that delivery-related terms were now better predictors of being short-listed by clients, and the firm quickly and successfully switched its engagement model globally. Firm leaders who would previously have received information about supply-chain issues through macroeconomic data or a revenue shortfall at the end of a couple of quarters were able, using AI to predict intermediate outcomes in clients’ buying processes, to rapidly switch the marketing and sales approach to better align with shifts in the marketplace.

We found another example at a major real estate property developer in the UK. A January 2020 analysis of optimal incentives to tenants suggested that, given a low likelihood of corporate space remaining unrented for more than 30 days, it should be conservative in offering incentives to existing corporate tenants. The analysis further showed flexible workspaces to be less profitable than renting out corporate office space given competitive cost pressures. By late February 2020, in the very early stages of the pandemic, the developer’s updated AI model suggested increasing the flex workspace footprint by 30% and offering generous incentives to lock in existing tenants. These recommendations led the developer to begin changing its sales strategy by the middle of March, much faster than competitors still relying on the first quarter (ending March) output of their marketing and sales models. A month’s or even a week’s lead can make a significant difference in a competitive market.

In the preceding examples, each firm had to specify goals when setting up its AI models to predict outcomes. A goal might be to achieve a specific customer-acquisition level when given a specific marketing budget. Well-designed AI models are about enhancing business outcomes — not just accurate predictions. They balance the benefit of a correct prediction against the cost of an incorrect one and work within organizational constraints like marketing budgets. Being trained using historical data, AI models provide firms with a better, more sophisticated and nimble understanding of the links between their actions and the market or customer response.

Understanding the Role of Feedback Loops

Marketing and sales have traditionally lacked an approach to the classic “SENSE –>RESPONSE” feedback loop commonly exploited in the engineering world. Feedback loops enable systems to change input mix and system characteristics to enhance output. The lagged effect of marketing actions and the fact that customer response is, more often than not, the result of the cumulative effect of multiple actions taken by the firm make it hard to establish causality and establish a clear feedback loop. It is this lack of a feedback loop that limits firms’ ability to assess the ROI of their marketing and sales efforts. Absence of feedback loops further results in a disconnect between episodic strategy formulation (the realm of senior management) and the constant execution in the field that is typically managed at the frontline.

AI prediction models can pick up trends at a granular level, such as at the level of individual transactions. The field information provided by these models can be used to update and tweak marketing and sales strategy faster and more frequently, enabling firms to close the gap between strategy and execution.

Here’s an example: A 200-year-old North American manufacturing firm had significantly increased its marketing lead-generation activities but had yet to achieve a significant increase in sales. The firm was convinced it had a marketing problem. It used an AI model to analyze the data and found that the increased marketing spending had indeed generated high-quality leads, but not higher overall sales. Subsequent analyses revealed that the manufacturer’s limited sales resources were part of the problem. The sales team had cherry picked the best leads from the incremental marketing spend, but ignored a corresponding number of leads it would otherwise have followed up on.

The company now understood it had a sales-capacity issue, not a marketing problem. The analysis enabled the manufacturer to appropriately balance sales and marketing expenses to generate stronger revenue. Without the benefit of the data analysis, the siloed nature of the marketing and sales organizations would have made it difficult and time-consuming to do such a cross-functional study or reallocate resources quickly.

This disconnect is further illustrated by the example of a consumer-electronics company that ceased doing business in Russia consequent to its invasion of Ukraine. The company knew what its revenue shortfall would be due to lost sales in Russia and associated markets, but faced the difficult question of how to optimally reallocate the marketing spend to other markets to try to offset the lost sales. An AI-optimized scenario planning exercise suggested the best way to reallocate the available marketing budget and quantified the expected net drop in sales and increase in marketing budget necessary to offset the loss by increasing sales in other regions. The analysis also revealed that it would be too expensive to increase marketing to fully offset the losses from Russia. But it still enabled the firm to optimally reduce sales losses by reallocating existing marketing promotion budgets to other regions.

Flipping the Segmentation Process

As a result of the feedback-loop focus, we see the use of AI models also changing the practice of segmentation. In theory, segmentation is defined as the process of identifying a group of customers who have a common set of needs (to develop a unique product/solution to serve that segment), that share common identifiable characteristics (to be able identify customers in the target segment), and that are likely to react in a similar manner to actions taken by the firm (to design the engagement strategy and exploit economies of scale). In practice, most firms in the analog world focus on the first two parts of the definition, i.e., common set of needs and common characteristics.  This approach therefore takes the form of an outside-in approach: “Let’s figure out what this group truly needs and then design the right product to serve these needs better than anyone else and, as a result, be able to extract a higher price.”

In AI-based prediction models, the practice of segmentation is focused on the third part of the definition of segmentation, i.e., the likelihood that all customers in a segment are likely to react similarly to marketing and sales actions taken by the firm.  For example, an AI-based prediction model might ask which customers are better served by the sales force in the field or the tele-sales team, or which customers are most likely to respond positively to a specific price promotion campaign. Firms can use an AI model’s predictions to align the appropriate marketing and sales resources to serve each demand opportunity.

Considering the unmatched targeting abilities of predictive models, it is easier to take organizational (or expected near-term organizational) capabilities as a given and find the customers most likely to match those capabilities. This is especially true in a rapidly changing environment where market conditions and customer behavior can change far faster than organizational capabilities can evolve.

Where Are We Headed Next with AI-based Prediction Models?

The availability of customer specific data and ability of AI and machine learning to provide better predictions is poised to force companies to create integrated customer-facing organizations that fuse traditional marketing and sales functions. Ideally, this will, help organizations deliver a superior customer experience that results in enhanced profitability.

Here’s one more example: An international manufacturer wanting to improve its marketing function using AI models initially focused on prioritizing sales opportunities. Analysis of its data, however, found that, dollar-for-dollar, efforts by the field sales force focused on retaining existing channel partners had a greater impact on revenue than a similar amount spent solely on marketing. In fact, optimizing spend across channel partner retention, marketing, and sales had a greater impact on overall business KPI for a given level of overall spend than would have been achieved had the focus remained exclusively on sales-opportunity prioritization. Truly automated approaches to AI can “let the data speak” to help identify entirely new avenues across traditional marketing and sales activities with the potential to impact business KPIs and optimally balance resourcing between those activities.

Digitally native firms may make quick progress on integration of AI models, but we are concerned that legacy firms that grew up in the analog world are going to run into two major stumbling blocks and fall behind their competitors. The first is the siloed nature of their sales, marketing, and support organizations, which will impede enterprise-wide integration of customer-facing functions. The second stumbling block is that the only entities that can break this stalemate — the CEO and board — are often ignorant of how AI-based prediction models can redefine the way firms engage with customers and market segments.

Boards, unless they have members with tech expertise, are unlikely to demand the organizational transformations needed to make this happen. Ample evidence of this is found in traditional, sales-led enterprise software firms, that have struggled to defend themselves from nimble digitally native competitors that take a holistic approach to serving customers and understanding the opportunities in their data.

Will machines take over marketing and sales functions? No. Marketing and sales will not be run entirely by machines. We still need humans to make non-obvious decisions. When it comes to updating strategy, a human will always be needed to ensure the validity of AI-generated recommendations before acting on them. Humans are needed to monitor outcomes on an ongoing basis in order to provide continuous feedback to the AI models.

Remember, despite all its strengths, AI tools are far from infallible. AI at its best is a tool that augments human capability, and could reshape how we make decisions in functions such as marketing and sales and maintain a competitive advantage.


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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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