Strategic and Tactical Return on AI
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Most tactical generative AI projects don't seem to yield much value
Many observers of AI are questioning the level of value that it provides to organizations, particularly for generative AI. The notorious MIT NANDA study—often described as the “95% zero value” study—has an odd methodology, but its primary finding is that tactical, individual-level, “broad and shallow” implementations of generative AI don’t provide substantial value to the companies that employ them. Enterprise-level, “deep and narrow” generative AI projects that are consistent with a company’s business strategy, on the other hand, do often yield measurable value.
The most extreme version of strategic AI is found in a small but growing number of companies might be described as “all in on AI” or “AI first.” These companies are aggressively pursuing strategic returns on their AI investments. They are using the technology to enable new strategies, new business models, and dramatically new ways of performing their business processes. While they represent a low percentage of companies, they are providing trailblazing examples for the majority of companies that are more conservative.
Consider, for example, such companies as Ping An in China, DBS Bank in Singapore, and Sanofi in France. All are currently involved in, or have already been successful at, the adoption of AI at strategic levels in many parts of their businesses. Ping An has employed AI to enable and empower five rapidly-growing ecosystems in insurance, banking, healthcare, automobile services, and smart cities. DBS uses AI to create “joyful banking”—a pleasant, digital way to conduct banking transactions using a chatbot built with extensive refinements—as well as to reduce fraud and transform internal processes. At the pharmaceutical giant Sanofi, both analytical and generative AI are being harnessed to transform the process of drug development.
Contrast their approach with the goal of tactical returns on AI investments. In this context, returns from AI projects mean incremental gains on existing tasks or processes. Perhaps some customer calls are diverted to a chatbot or intelligent agent, or the company is able to use AI to extract information from documents that previously required human eyes and typing. In order to achieve substantial value with this approach, there would need to be widespread adoption of AI across the company. Even that broad approach will achieve incremental benefits for the overall company, rather than any specific function or unit.
Strategic use of AI takes advantage of all the technologies available. In the late 2010s and early 2020s, the focus was on traditional machine learning on structured data—sometimes called “analytical AI.” For example, Morgan Stanley Wealth Management began working in the early 2010s on a “next best action” AI system to recommend personalized investments to clients, which it deployed beginning in 2017. In late 2022 with the announcement of ChatGPT, many companies began to explore generative AI. However, Morgan Stanley began working with OpenAI in early 2021 to build the “AI@Morgan Stanley Assistant.” It made the company’s own knowledge widely accessible to advisors and their teams, and the application went into production deployment in 2023. Since then Morgan Stanley has created several other production use cases of generative AI. In 2025, many companies are beginning to address strategic use cases of agentic AI, which not only provides content to users but can take action on digital transactions. Morgan Stanley plans to roll out a AI agent for customer service in late 2025. The most strategically-focused companies with AI are now employing all of these AI capabilities.
The same distinctions between strategic and tactical use, however, apply to all types of AI technology. With analytical AI, RoAI Institute research presented in a 2021 MIT Sloan Management Review webinar noted that less than 15% of companies investing in AI are getting meaningful returns. Several other surveys found low levels of economic value from AI. A 2022 Deloitte survey in large, global organizations found that the most common objectives pursued with analytical AI were process improvement and better decision-making—worthy goals, but normally tactical.
More recently, the same pattern is taking place with generative AI. Most organizations are taking an experimental approach to the technology, with tactical and incremental productivity as the objective. At the beginning of 2024 only 6% had an organizational use case in production; a year later at the beginning of 2025, the percentage increased to 20-25%. Even when companies are experimenting, the experiments are typically casual and unstructured. They seldom change business processes, measure value, or address strategic priorities. A McKinsey survey in early 2025 found that only 1% of companies in developed economies described their generative AI implementations as “mature.” Another McKinsey survey later in 2025 found that only 21% of large organizations had redesigned some workflows to take advantage of AI, but that workflow change was the attribute of AI most closely correlated with economic value.
Whatever the specific form of AI, if your return on AI is non-existent or all tactical now, our research points to success factors and approaches organizations can take to maximizing returns using a more strategic and systematic approach.
Strategic vs. Tactical Return
Both strategic and tactical approaches to AI returns can be useful, and some firms are aiming for both. However, the processes for proposing and realizing them are very different. Achieving strategic returns, by definition, involves strategy and its implementation. The idea to use AI in a particular setting is part of the organization’s overall strategy for how it goes to market and succeeds with customers. Strategy is usually articulated by senior executives, and if AI is going to be integrated with strategizing, senior executives need to understand all types of AI and what can be done with them. If there is a strategy organization, its members also need to be conversant with AI and its application.
Strategic implementations of AI often cost more, take longer, and involve more people than tactical ones. They usually involve major change to business processes and business capabilities. Perhaps they are the basis of a new product line or a new relationship with customers. That requires careful preparation across multiple parts of a company. Front-line employees who will use the system need to be educated in the use of the system, and perhaps will need to develop other new skills. The rollout of the technology and the associated business changes needs to be carefully planned.
Tactical returns from tactical AI projects, not surprisingly, generally involve easier implementation. Because they involve incremental change, they don’t need a lot of senior management deliberation. Changes to business processes are usually relatively minor. In many cases, AI is being used to perform a task that is already done by humans. If that leads to substantial reallocation of labor, human employees would need to be trained on their new roles. And in most cases there is still some need for integration of AI capabilities with existing information systems and architectures.
However, because tactical projects involve less commitment, they are more likely than strategic AI initiatives to start as proofs of concept or pilots and never get deployed into production. And as is perhaps obvious, no deployment means no value. Tactical projects also often employ the “so-so technologies” described by economists Daron Acemoglu and Pascual Restrepo. These are technologies that displace workers from jobs, or aspects of their jobs, without offering significant productivity benefits. One example they cite is inexpensive, off-the-shelf chatbots, which they argue displaces workers to some degree, but provides neither high-quality customer service nor significant gains in productivity.
Which to Pursue?
Strategic applications of AI are far more valuable to an organization than tactical ones if they work as planned. They can vault an organization into a new business model or strategy, and can yield high levels of benefit. For example, in a 2021 MIT Sloan Management Review/Boston Consulting Group survey, respondents who use AI primarily to explore new ways of creating value were 2.7 times more likely to improve their ability to compete with AI and capture opportunities from adjacent industries than those who use AI primarily to improve existing processes. In the 2022 Deloitte survey mentioned above, the most successful companies with AI were more than three times as likely as those with lower levels of value achieved to use AI to address new customer markets and segments, enable new products/programs/services, and support new business models.
Of course, achieving those returns is more difficult, as well as more difficult to measure. For an AI-enabled strategy to go right, many things have to fall into place, including the factor of competitor response. If they emulate the AI and business changes, the strategy could fall flat. And when a strategic use of AI does succeed, there will be many who want to claim the success. Attributing a specific amount of return to the AI itself is difficult with multiple aspects of business change being wrapped into the solution.
Tactical AI projects provide less value, but are easier to measure. The primary benefit they offer is productivity gains, particularly if they replace human employees. At a time when many companies struggle to find enough workers, tactical projects can also fill an important labor gap. When human workers are not eliminated but deployed onto other types of tasks (with reported savings in minutes or hours), measurement of value is more difficult. Elimination of outsourcing is another common source of value from tactical AI—particularly from automation projects.
Choosing strategic vs. tactical projects and returns is often dictated by the management and financial situation within the company that will implement AI. Are senior leaders aware of what AI can do and motivated to employ it aggressively? Is the strategic intent for AI clearly defined and communicated across the company? Does the company have the human and financial resources to undertake a large-scale AI project? Is there a potential strategic benefit from AI done well? Is there a deep experimentation mindset across the company? If these factors are in place, strategic projects will be a natural step to take. If they are missing, it is unlikely that any strategic project will be successful, and a company should “cut its teeth” on more tactical AI. At a minimum, however, companies should undertake tactical AI projects with an eye to how they might fit together to make possible a broader strategic objective.
Companies desiring strategic returns can start with significantly increasing the AI fluency of their top leaders including the board, CEO and management teams. Top leaders with a deep understanding of AI will position the company to ask the right questions about how AI can bring about a transformation in its strategy, business model, or operations.
Second, identify the levers required to achieve those desired outcomes from AI. These levers many include changes in business strategy, leadership, culture, AI talent, organization, and technology. It is helpful to baseline assets and capabilities and to pinpoint key gaps. Then, systematically identify and evaluate alternative strategic initiatives, and tactical projects that could eventually produce strategic value, around a coherent vision for generating return.
Third, companies should conduct a series of stakeholder sessions to align the top-down strategy with the bottom-up execution to create organizational buy-in. This organizational alignment will help to develop the right AI roadmap with components of action and corresponding investments. Finally, companies will need to monitor whether the desired strategic change is taking place, and whether capital resources are being effectively allocated. Remember that strategic returns are measured in business change, not incremental savings. These deep changes to an organization won’t happen overnight, but the investment and effort over time to achieve them can position an organization for a long and successful future.
This article was originally published on Dr. Tom Davenport’s Substack here.
Tom Davenport
Dr. Tom Davenport is a world-renowned thought leader and author, is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte's Chief Data and Analytics Officer Program.
An author and co-author of 25 books and more than 300 articles, Tom helps organizations to transform their management practices in digital business domains such as artificial intelligence, analytics, information and knowledge management, process management, and enterprise systems.
He's been named:
- A "Top Ten Voice in Tech" on LinkedIn in 2018
- The #1 voice on LinkedIn among the "Top Ten Voices in Education 2016"
- One of the top 50 business school professors in the world in 2012 by Fortune magazine
- One of the 100 most influential people in the technology industry in 2007 by Ziff-Davis
- The third most important business/technology analyst in the world in 2005 by Optimize magazine
- One of the top 25 consultants in the world in 2003
Laks helps organizations and leaders adopt AI to maximize value and impact.