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

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6 months ago

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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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How to Train Generative AI Using Your Company’s Data

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3 months ago

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July 6, 2023

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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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13 Principles for Using AI Responsibly

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3 months ago

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June 30, 2023

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The competitive nature of AI development poses a dilemma for organizations, as prioritizing speed may lead to neglecting ethical guidelines, bias detection, and safety measures. Known and emerging concerns associated with AI in the workplace include the spread of misinformation, copyright and intellectual property concerns, cybersecurity, data privacy, as well as navigating rapid and ambiguous regulations. To mitigate these risks, we propose thirteen principles for responsible AI at work.

Love it or loath it, the rapid expansion of AI will not slow down anytime soon. But AI blunders can quickly damage a brand’s reputation — just ask Microsoft’s first chatbot, Tay. In the tech race, all leaders fear being left behind if they slow down while others don’t. It’s a high-stakes situation where cooperation seems risky, and defection tempting. This “prisoner’s dilemma” (as it’s called in game theory) poses risks to responsible AI practices. Leaders, prioritizing speed to market, are driving the current AI arms race in which major corporate players are rushing products and potentially short-changing critical considerations like ethical guidelines, bias detection, and safety measures. For instance, major tech corporations are laying off their AI ethics teams precisely at a time when responsible actions are needed most.

It’s also important to recognize that the AI arms race extends beyond the developers of large language models (LLMs) such as OpenAI, Google, and Meta. It encompasses many companies utilizing LLMs to support their own custom applications. In the world of professional services, for example, PwC announced it is deploying AI chatbots for 4,000 of their lawyers, distributed across 100 countries. These AI-powered assistants will “help lawyers with contract analysis, regulatory compliance work, due diligence, and other legal advisory and consulting services.” PwC’s management is also considering expanding these AI chatbots into their tax practice. In total, the consulting giant plans to pour $1 billion into “generative AI” — a powerful new tool capable of delivering game-changing boosts to performance.

In a similar vein, KPMG launched its own AI-powered assistant, dubbed KymChat, which will help employees rapidly find internal experts across the entire organization, wrap them around incoming opportunities, and automatically generate proposals based on the match between project requirements and available talent. Their AI assistant “will better enable cross-team collaboration and help those new to the firm with a more seamless and efficient people-navigation experience.”

Slack is also incorporating generative AI into the development of Slack GPT, an AI assistant designed to help employees work smarter not harder. The platform incorporates a range of AI capabilities, such as conversation summaries and writing assistance, to enhance user productivity.

These examples are just the tip of the iceberg. Soon hundreds of millions of Microsoft 365 users will have access to Business Chat, an agent that joins the user in their work, striving to make sense of their Microsoft 365 data. Employees can prompt the assistant to do everything from developing status report summaries based on meeting transcripts and email communication to identifying flaws in strategy and coming up with solutions.

This rapid deployment of AI agents is why Arvind Krishna, CEO of IBM, recently wrote that, “[p]eople working together with trusted A.I. will have a transformative effect on our economy and society … It’s time we embrace that partnership — and prepare our workforces for everything A.I. has to offer.” Simply put, organizations are experiencing exponential growth in the installation of AI-powered tools and firms that don’t adapt risk getting left behind.

AI Risks at Work

Unfortunately, remaining competitive also introduces significant risk for both employees and employers. For example, a 2022 UNESCO publication on “the effects of AI on the working lives of women” reports that AI in the recruitment process, for example, is excluding women from upward moves. One study the report cites that included 21 experiments consisting of over 60,000 targeted job advertisements found that “setting the user’s gender to ‘Female’ resulted in fewer instances of ads related to high-paying jobs than for users selecting ‘Male’ as their gender.” And even though this AI bias in recruitment and hiring is well-known, it’s not going away anytime soon. As the UNESCO report goes on to say, “A 2021 study showed evidence of job advertisements skewed by gender on Facebook even when the advertisers wanted a gender-balanced audience.” It’s often a matter of biased data which will continue to infect AI tools and threaten key workforce factors such as diversity, equity, and inclusion.

Discriminatory employment practices may be only one of a cocktail of legal risks that generative AI exposes organizations to. For example, OpenAI is facing its first defamation lawsuit as a result of allegations that ChatGPT produced harmful misinformation. Specifically, the system produced a summary of a real court case which included fabricated accusations of embezzlement against a radio host in Georgia. This highlights the negative impact on organizations for creating and sharing AI generated information. It underscores concerns about LLMs fabricating false and libelous content, resulting in reputational damage, loss of credibility, diminished customer trust, and serious legal repercussions.

In addition to concerns related to libel, there are risks associated with copyright and intellectual property infringements. Several high-profile legal cases have emerged where the developers of generative AI tools have been sued for the alleged improper use of licensed content. The presence of copyright and intellectual property infringements, coupled with the legal implications of such violations, poses significant risks for organizations utilizing generative AI products. Organizations can improperly use licensed content through generative AI by unknowingly engaging in activities such as plagiarism, unauthorized adaptations, commercial use without licensing, and misusing Creative Commons or open-source content, exposing themselves to potential legal consequences.

The large-scale deployment of AI also magnifies the risks of cyberattacks. The fear amongst cybersecurity experts is that generative AI could be used to identify and exploit vulnerabilities within business information systems, given the ability of LLMs to automate coding and bug detection, which could be used by malicious actors to break through security barriers. There’s also the fear of employees accidentally sharing sensitive data with third-party AI providers. A notable instance involves Samsung staff unintentionally leaking trade secrets through ChatGPT while using the LLM to review source code. Due to their failure to opt out of data sharing, confidential information was inadvertently provided to OpenAI. And even though Samsung and others are taking steps to restrict the use of third-party AI tools on company-owned devices, there’s still the concern that employees can leak information through the use of such systems on personal devices.

On top of these risks, businesses will soon have to navigate nascent, varied, and somewhat murky regulations. Anyone hiring in New York City, for instance, will have to ensure their AI-powered recruitment and hiring tech doesn’t violate the City’s “automated employment decision tool” law. To comply with the new law, employers will need to take various steps such as conducting third-party bias audits of their hiring tools and publicly disclosing the findings. AI regulation is also scaling up nationally with the Biden-Harris administration’s “Blueprint for an AI Bill of Rights” and internationally with the EU’s AI Act, which will mark a new era of regulation for employers.

This growing nebulous of evolving regulations and pitfalls is why thought leaders such as Gartner are strongly suggesting that businesses “proceed but don’t over pivot” and that they “create a task force reporting to the CIO and CEO” to plan a roadmap for a safe AI transformation that mitigates various legal, reputational, and workforce risks. Leaders dealing with this AI dilemma have important decision to make. On the one hand, there is a pressing competitive pressure to fully embrace AI. However, on the other hand, a growing concern is arising as the implementation of irresponsible AI can result in severe penalties, substantial damage to reputation, and significant operational setbacks. The concern is that in their quest to stay ahead, leaders may unknowingly introduce potential time bombs into their organization, which are poised to cause major problems once AI solutions are deployed and regulations take effect.

For example, the National Eating Disorder Association (NEDA) recently announced it was letting go of its hotline staff and replacing them with their new chatbot, Tessa. However, just days before making the transition, NEDA discovered that their system was promoting harmful advice such as encouraging people with eating disorders to restrict their calories and to lose one to two pounds per week. The World Bank spent $1 billion to develop and deploy an algorithmic system, called Takaful, to distribute financial assistance that Human Rights Watch now says ironically creates inequity. And two lawyers from New York are facing possible disciplinary action after using ChatGPT to draft a court filing that was found to have several references to previous cases that did not exist. These instances highlight the need for well-trained and well-supported employees at the center of this digital transformation. While AI can serve as a valuable assistant, it should not assume the leading position.

Principles for Responsible AI at Work

To help decision-makers avoid negative outcomes while also remaining competitive in the age of AI, we’ve devised several principles for a sustainable AI-powered workforce. The principles are a blend of ethical frameworks from institutions like the National Science Foundation as well as legal requirements related to employee monitoring and data privacy such as the Electronic Communications Privacy Act and the California Privacy Rights Act. The steps for ensuring responsible AI at work include:

  • Informed Consent. Obtain voluntary and informed agreement from employees to participate in any AI-powered intervention after the employees are provided with all the relevant information about the initiative. This includes the program’s purpose, procedures, and potential risks and benefits.
  • Aligned Interests. The goals, risks, and benefits for both the employer and employee are clearly articulated and aligned.
  • Opt In & Easy Exits. Employees must opt into AI-powered programs without feeling forced or coerced, and they can easily withdraw from the program at any time without any negative consequences and without explanation.
  • Conversational Transparency. When AI-based conversational agents are used, the agent should formally reveal any persuasive objectives the system aims to achieve through the dialogue with the employee.
  • Debiased and Explainable AI. Explicitly outline the steps taken to remove, minimize, and mitigate bias in AI-powered employee interventions—especially for disadvantaged and vulnerable groups—and provide transparent explanations into how AI systems arrive at their decisions and actions.
  • AI Training and Development. Provide continuous employee training and development to ensure the safe and responsible use of AI-powered tools.
  • Health and Well-Being. Identify types of AI-induced stress, discomfort, or harm and articulate steps to minimize risks (e.g., how will the employer minimize stress caused by constant AI-powered monitoring of employee behavior).
  • Data Collection. Identify what data will be collected, if data collection involves any invasive or intrusive procedures (e.g., the use of webcams in work-from-home situations), and what steps will be taken to minimize risk.
  • Data. Disclose any intention to share personal data, with whom, and why.
  • Privacy and Security. Articulate protocols for maintaining privacy, storing employee data securely, and what steps will be taken in the event of a privacy breach.
  • Third Party Disclosure. Disclose all third parties used to provide and maintain AI assets, what the third party’s role is, and how the third party will ensure employee privacy.
  • Communication. Inform employees about changes in data collection, data management, or data sharing as well as any changes in AI assets or third-party relationships.
  • Laws and Regulations. Express ongoing commitment to comply with all laws and regulations related to employee data and the use of AI.

We encourage leaders to urgently adopt and develop this checklist in their organizations. By applying such principles, leaders can ensure rapid and responsible AI deployment.

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AI

3 Steps to Prepare Your Culture for AI

Published

3 months ago

on

June 28, 2023

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The platform shift to AI is well underway. And while it holds the promise of transforming work and giving organizations a competitive advantage, realizing those benefits isn’t possible without a culture that embraces curiosity, failure, and learning. Leaders are uniquely positioned to foster this culture within their organizations today in order to set their teams up for success in the future. When paired with the capabilities of AI, this kind of culture will unlock a better future of work for everyone.

As business leaders, today we find ourselves in a place that’s all too familiar: the unfamiliar. Just as we steered our teams through the shift to remote and flexible work, we’re now on the verge of another seismic shift: AI. And like the shift to flexible work, priming an organization to embrace AI will hinge first and foremost on culture.

The pace and volume of work has increased exponentially, and we’re all struggling under the weight of it. Leaders and employees are eager for AI to lift the burden. That’s the key takeaway from our 2023 Work Trend Index, which surveyed 31,000 people across 31 countries and analyzed trillions of aggregated productivity signals in Microsoft 365, along with labor market trends on LinkedIn.

Nearly two-thirds of employees surveyed told us they don’t have enough time or energy to do their job. The cause of this drain is something we identified in the report as digital debt: the influx of data, emails, and chats has outpaced our ability to keep up. Employees today spend nearly 60% of their time communicating, leaving only 40% of their time for creating and innovating. In a world where creativity is the new productivity, digital debt isn’t just an inconvenience — it’s a liability.

AI promises to address that liability by allowing employees to focus on the most meaningful work. Increasing productivity, streamlining repetitive tasks, and increasing employee well-being are the top three things leaders want from AI, according to our research. Notably, amid fears that AI will replace jobs, reducing headcount was last on the list.

Becoming an AI-powered organization will require us to work in entirely new ways. As leaders, there are three steps we can take today to get our cultures ready for an AI-powered future:

Choose curiosity over fear

AI marks a new interaction model between humans and computers. Until now, the way we’ve interacted with computers has been similar to how we interact with a calculator: We ask a question or give directions, and the computer provides an answer. But with AI, the computer will be more like a copilot. We’ll need to develop a new kind of chemistry together, learning when and how to ask questions and about the importance of fact-checking responses.

Fear is a natural reaction to change, so it’s understandable for employees to feel some uncertainty about what AI will mean for their work. Our research found that while 49% of employees are concerned AI will replace their jobs, the promise of AI outweighs the threat: 70% of employees are more than willing to delegate to AI to lighten their workloads.

We’re rarely served by operating from a place of fear. By fostering a culture of curiosity, we can empower our people to understand how AI works, including its capabilities and its shortcomings. This understanding starts with firsthand experience. Encourage employees to put curiosity into action by experimenting (safely and securely) with new AI tools, such as AI-powered search, intelligent writing assistance, or smart calendaring, to name just a few. Since every role and function will have different ways to use and benefit from AI, challenge them to rethink how AI could improve or transform processes as they get familiar with the tools. From there, employees can begin to unlock new ways of working.

Embrace failure

AI will change nearly every job, and nearly every work pattern can benefit from some degree of AI augmentation or automation. As leaders, now is the time to encourage our teams to bring creativity to reimagining work, adopting a test-and-learn strategy to find ways AI can best help meet the needs of the business.

AI won’t get it right every time, but even when it’s wrong, it’s usefully wrong. It moves you at least one step forward from a blank slate, so you can jump right into the critical thinking work of reviewing, editing, or augmenting. It will take time to learn these new patterns of work and identify which processes need to change and how. But if we create a culture where experimentation and learning are viewed as a prerequisite to progress, we’ll get there much faster.

As leaders, we have a responsibility to create the right environment for failure so that our people are empowered to experiment to uncover how AI can fit into their workflows. In my experience, that includes celebrating wins as well as sharing lessons learned in order to help keep each other from wasting time learning the same lesson twice. Both formally and informally, carve out space for people to share knowledge — for example, by crowdsourcing a prompt guidebook within your department or making AI tips a standing agenda item in your monthly all-staff meetings. Operating with agility will be a foundational tenet of AI-powered organizations.

Become a learn-it-all

I often hear concerns that AI will be a crutch, offering shortcuts and workarounds that ultimately diminish innovation and engagement. In my mind, the potential for AI is so much bigger than that, and it will become a competitive advantage for those who use it thoughtfully. Those will become your most engaged and innovative employees.

The value you get from AI is only as good as what you put in. Simple questions will result in simple answers. But sophisticated, thought-provoking questions will result in more complex analysis and bigger ideas. The value will shift from employees who have all the right answers to employees who know how to ask the right questions. Organizations of the future will place a premium on analytical thinkers and problem-solvers who can effectively reason over AI-generated content.

At Microsoft, we believe a learn-it-all mentality will get us much farther than a know-it-all one. And while the learning curve of using AI can be daunting, it’s a muscle that has to be built over time — and that we should start strengthening today. When I talk to leaders about how to achieve this across their companies and teams, I tell them three things:

  • Establish guardrails to help people experiment safely and responsibly. Which tools do you encourage employees to use, and what data is — and isn’t — appropriate to input. What guidelines do they need to follow around fact-checking, reviewing, and editing?
  • Learning to work with AI will need to be a continuous process, not a one-time training. Infuse learning opportunities into your rhythm of business and keep employees up to date with the latest resources. For example, one team might block off Friday afternoons for learning, while another has monthly “office hours” for AI Q&A and troubleshooting. And think beyond traditional courses or resources. How can peer-to-peer knowledge sharing, such as lunch and learns or a digital hotline, play a role so people can learn from each other?
  • Embrace the need for change management. Being intentional and programmatic will be crucial for successfully adopting AI. Identify goals and metrics for success, and select AI champions or pilot program leads to help bring the vision to life. Different functions and disciplines will have different needs and challenges when it comes to AI, but one shared need will be for structure and support as we all transition to a new way of working.

The platform shift to AI is well underway. And while it holds the promise of transforming work and giving organizations a competitive advantage, realizing those benefits isn’t possible without a culture that embraces curiosity, failure, and learning. As leaders, we’re uniquely positioned to foster this culture within our organizations today in order to set our teams up for success in the future. When paired with the capabilities of AI, this kind of culture will unlock a better future of work for everyone.

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