Connect with us


AI Can Help You Ask Better Questions — and Solve Bigger Problems



Just a few years ago, businesses wrestled with artificial intelligence mainly in the abstract — a “future of work” problem they’d have to contend with down the line. Now? More than half the companies around the world are actively adopting AI. Although investments are particularly high in industries such as health care, data management and processing, cloud computing, and fintech, all types of organizations and functions have incorporated AI technology into their operations. And generative AI tools such as ChatGPT are forcing leaders to ask where and how AI can help their businesses.

Even so, most companies still view AI rather narrowly, as a tool that alleviates the costs and inefficiencies of repetitive human labor by automating mundane physical tasks (like moving merchandise in warehouses) and increasing organizations’ capacity to produce, process, and analyze piles and piles of data. But the technology can do much more than that.

Paired with “soft” inquiry-related skills such as critical thinking, innovation, active learning, complex problem solving, creativity, originality, and initiative, this technology can further our understanding of an increasingly complex world, allowing us to engage in more abstract questioning and shifting our focus from identification to ideation. In our research and workshops with executives, we’re finding that companies have much to gain by treating AI as a aknowledge-work collaborator in diverse areas such as product design, process efficiency, and prompt engineering. Partnering with the technology in this way can help people ask smarter questions, which in turn makes them better problem solvers and breakthrough innovators. We are also seeing the initial impacts of more context-aware AI systems (like ChatGPT), and as they continue to improve, the skill of asking questions (or creating prompts) will only become more valuable in the discovery process.

Although experts have recognized the need for software engineers to ask smart questions upstream, when developing automated tools (to bake in fewer biases and assumptions), little has been said about the flipside of the relationship between AI and inquiry: the technology’s potential to help people become more inquisitive, creative problem-solvers on the job. We aimed to correct this oversight through design-thinking sessions and extensive follow-up conversations with tech-driven business leaders from a diverse array of countries and industries. We also surveyed roughly 200 leaders, from more than 30 countries who participated in our executive education programs at MIT —to learn how artificial intelligence has affected questioning patterns and innovation behaviors and outcomes in their organizations. (For this research, we’ve defined “artificial intelligence” broadly to include machine learning, deep learning, robotics, and the recent explosion of generative AI.)

We have found two distinct, yet related, paths that leaders follow to strengthen their (and their teams’) inquiry muscles as they tap the power of AI in their question-asking work.

On the first path, they can use the technology to change the cadence and patterns of their questions: AI increases question velocity, question variety, and question novelty. Results from our ongoing research show that AI can significantly increase all three.

On the second path, AI can help transform the conditions and settings where people work so that questions that spark change — what we call “catalytic” questions — can emerge. This pushes leaders out of their comfort zones and into the position of being intellectually wrong, emotionally uncomfortable, and behaviorally quiet and more reflective, all of which, it turns out, promotes innovative thinking and action.

Let’s look at how each path can lead to breakthrough ideas.

Increasing velocity, variety, and novelty.

Partnering with AI to ramp up the velocity, variety, and novelty of questions requires companies to train algorithms to answer the basic, easy (yes/no) questions independently and to reveal deeply buried patterns in the data. When this foundation is laid, humans can start exploring the power of more context-dependent and nuanced questions that AI technologies are not yet capable of answering alone.

Question velocity

Algorithms can provide immediate answers to questions that leaders pose, in turn allowing them to ask more — and more frequent — questions. In our research, we found that 79% of respondents asked more questions, 18% asked the same amount, and 3% asked fewer.

At the cybersecurity firm Cybereason, researchers rely on AI and machine learning to immediately answer the basic questions about what happened in an apparent breach so the team can more quickly turn its attention to formulating deeper questions about why it happened. In the past, CEO Lior Div said, findings were more black-and-white: “It’s an attack. It’s not an attack. It’s good or it’s bad.” But the speed with which AI filled in those blanks opened up a whole new line of questions around intent — and what hackers are really after in a given situation.

Of course, there are risks to using AI to generate rapid-fire questions. For one, people may keep asking more and more questions without working their way toward an actionable path, making it important to recognize when the process stops being productive. For another, more questions don’t necessarily amount to better questions, which means you’ll still need to exercise human judgment in deciding how to proceed.

Question variety

AI helps uncover patterns and correlations in large volumes of data — connections that humans can easily miss without the technology. Knowing they have this tool at their disposal frees up leaders to ask farther-ranging questions and explore new ideas that they may not have otherwise considered. In our research, we found that engagement with AI led respondents to ask different questions than they otherwise would have 94% of the time.

Consider this example: Kli Pappas, the director of predictive analytics at Colgate-Palmolive, told us that his team tapped AI to understand how charcoal became a wildly popular ingredient in consumer products so they could “find the next charcoal.” Their algorithm generated and answered thousands of questions based on their initial search for data, sketching out a decades-long trajectory from charcoal scrubs in South Korea 20 years ago to charcoal appearing in face washes in the U.S. and then in all kinds of products around the world. The AI-generated data led the team to ask hundreds of less-obvious questions to spark creative thinking about future trends that may be lurking in unexpected places. “We look backwards across categories and try to see how do trends move between categories from hair care, to skincare, to oral care,” Pappas said. “Just doing that puts you a decade or more ahead of the curve.”

Question novelty

AI also facilitates deeper insights by helping users arrive at novel, “category jumping” questions — the gold standard of innovative inquiry — that apply understanding from one area to a completely different space. Our research shows that AI led respondents to ask unique questions that changed the direction of their team, organization, or industry 75% of the time.

When you know a technology can sift through much more data, and connect more dots, than you could ever do alone, it gives you license to ask wilder questions — things you would never ask if you had to answer them on your own, because they are intractable for the human brain or somehow go against entrenched cognitive biases.

While category-jumping questions will not arise in every encounter with AI systems, being open to the possibilities and allowing for freedom of inquiry can pave the way for more instances. Here’s how Mir Imran, a medical innovator and founder of InCube Labs, described the upside when we spoke: “AI can take really obscure variables and make novel connections. When these hidden connections come together, it causes you to reframe your question and deliver disruptive innovations.” In other words, AI’s novel connections can spark your novel questions, which in turn can lead you to investigate solutions others haven’t dreamed of yet — like the robotic pills that Imran’s team recently created to replace external injections with internal ones.

Creating conditions for better questions.

AI can take leaders out of their usual mode of operation and force them to cede control over where their questions will take them. That’s a good thing. Increased question velocity, variety, and especially novelty give facilitate recognizing where you’re intellectually wrong, and becoming emotionally uncomfortable and behaviorally quiet — the very conditions that, we’ve found, tend to produce game-changing lines of inquiry. Jeff Wilke — former CEO of Amazon Consumer Worldwide, now a cofounder of Re:Build Manufacturing — has embraced these conditions not only in his day-to-day work as a tech executive but also throughout his career, continually revising his mental models while moving from role to role. When we spoke, he had this to say: “If you seek out things that you don’t know, and you have the courage to be wrong, to be ignorant, to have to ask more questions and maybe be embarrassed socially, then I think you build a more complete model, and that model serves you well over the course of your life.”

But there’s a hitch to teaming up with AI: Research suggests that it can be challenging for people to do so congenially because AI’s superhuman capabilities and unpredictable moves may prevent them from fully trusting and engaging with the technology. That tracks with what we’ve observed in organizations and learned from our conversations with leaders.

Distrust of the technology is hardly conducive to creative inquiry. So, look for ways to offset that, and don’t just leave it to AI to produce the conditions for breakthrough thinking and problem-solving. Consider how else you might create them. Where is there room in your problem-solving processes for synthesizing things that don’t seem related? How might you use those opportunities to throw people off balance so they’ll generate questions that reach beyond what they intellectually know to be right, what makes them emotionally comfortable, and what they are accustomed to saying and doing? At the same time, how can you create psychological safety for people in your organization to ask far-ranging questions and to use AI more effectively to learn from them, ultimately leading to asking better questions? When psychological safety is present, people can say, without repercussion, “I am wrong,” “I am uncomfortable,” and “I am still thinking”?

Rather than neatly resolve all those tensions, leaders and teams must learn to sit with the uncertainty that comes from asking questions that take them into new territory. While the process isn’t easy, the results are exciting, which is perhaps the most important benefit of collaborating with an AI system. Excitement provides momentum and motivation to push through a tough process, fueling further creativity.

Mitigating AI’s Weaknesses with Human Strengths

Artificial intelligence may be superhuman in some ways, but it also has considerable weaknesses. For starters, the technology is fundamentally backward-looking, trained on yesterday’s data — and the future might not look anything like the past. What’s more, inaccurate or otherwise flawed training data (for instance, data skewed by inherent biases) produces poor outcomes.

Leaders and their teams must manage such limitations if they are going to treat AI as a creative-thinking partner. How? By focusing on areas where the human brain and machines complement one another. Whereas AI increases the volume of data we can process and the degree of complexity we can manage, our brains work in a reductive manner; we generate ideas and then explain them to other people. Whereas machines lack imagination and moral judgment, we can tap those critical skills as AI helps us increase the velocity, variety, and novelty of the questions we’re asking to solve problems in our organizations. Such differences are the stuff of fruitful collaboration — and optimizing them can reduce the threat of AI to human labor.

With humans and AI working to their respective strengths, they can transform unknown unknowns into known unknowns, opening the door to breakthrough thinking: logical and conceptual leaps that neither could make without the other. Harnessing this potential will require leaders to look at artificial intelligence in a new light — one that is less about cost savings, efficiency, and automation and more about inspiration, imagination, and innovation. It will also require building a culture that supports, incentivizes, and rewards asking big questions — and not necessarily knowing the answers.


This post was originally published on this site

Continue Reading


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.

Continue Reading


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.

Continue Reading


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.


This post was originally published on this site

Continue Reading

Trending does not provide legal or accounting advice and is not associated with any government agency. Copyright © 2023 UA Services Corp - All Rights Reserved.