Here is an excerpt from an article written by Laurianne McLaughlin for MIT Sloan Management Review. To read the complete article, check out others, sign up for email alerts, and obtain subscription information, please click here.
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Which AI strategies really work? Get advice on today’s most pressing AI challenges, from MIT SMR experts.
“Despite two years of broad managerial attention and extensive experimentation, we are not seeing the large-scale GenAI-powered business transformations that many people initially envisioned.”
Does this statement by MIT SMR authors Melissa Webster and George Westerman reflect your experience? No wonder. After a wild two-year ride of hype, disruption, and experiments for many leaders, you (and your colleagues) may still be waiting for the big business payoff. You may not have redesigned that critical process, cut time to market, or radically improved customer satisfaction quite yet.
As Westerman, a senior lecturer at the MIT Sloan School of Management, noted in one of our most popular articles on artificial intelligence this year, you may need to practice more patience. The most successful companies are now in the midst of smaller transformations using AI tools — paving the way for that big transformation.
It’s difficult to articulate how hard it is for leaders to shape AI strategy in 2025. After all, this work involves tackling everything from risk management to AI ethics, with some daunting data management and culture challenges thrown in. At the same time, AI and generative AI tools keep evolving. What GenAI tool Claude can’t do this spring, it may well do by summer.
At MIT SMR, we strive to publish fresh, evidence-based advice that leaders can apply at their own organizations. Leaders keenly want guidance on AI strategy: We see this in our article readership data. Here, we’ve gathered 10 of our most popular, valuable AI articles of recent months to share timely lessons on 10 pressing AI issues.
1. Reap GenAI value: Start with “small t” transformations.
“Less than two years ago, generative AI made headlines with its amazing new capabilities: It could engage in conversations; interpret massive amounts of text, audio, or imagery; and even create new documents and artwork. After the fastest technology adoption in history — with over 100 million users in the first two months — businesses in every industry began experimenting with it. Yet, despite two years of broad managerial attention and extensive experimentation, we are not seeing the large-scale GenAI-powered business transformations that many people initially envisioned.
“What happened? Has the technology failed to live up to its promise? Were experts wrong in calling for giant transformations? Have companies been too cautious? The answer to each of those questions is both yes and no. Generative AI is already being used in transformative ways in many companies, just not yet as the driver of a wholesale redesign of major business functions. Business leaders are finding ways to derive real value from large language models (LLMs) without complete replacements of existing business processes. They’re pursuing ‘small t’ transformation, even as they build the foundation for larger transformations to come.”
Read the full article, “Generate Value From GenAI With ‘Small t’ Transformations,” by Melissa Webster and George Westerman.
2. Make smart AI tech-debt trade-offs.
“To understand how today’s business leaders are reinventing their organizations, including the role tech debt plays, Accenture studied 1,500 global companies in 10 countries covering 19 industries and conducted scores of in-depth discussions with C-level leaders. The research found that companies that are well positioned for change have a reinvention-ready ‘digital core’ — a set of key components such as cloud infrastructure, data, and AI that can be easily updated. They also typically set aside around 15% of their IT budgets for tech debt remediation.
Addressing tech debt is not about eliminating it but managing it.”
“What the research made clear is that today, addressing tech debt is not about eliminating it but managing it. The key lies in knowing what the debt is, what to fix, what to keep, and how to recognize the tech debt that is boosting your company’s innovation capacity.”
Read the full article, “How to Manage Tech Debt in the AI Era,” by Koenraad Schelfaut and Prashant P. Shukla.
3. Unstructured data matters again.
“The great majority of the data that GenAI works with is relatively unstructured, in forms such as text, images, video, and the like. A leader at one large insurance organization recently shared … that 97% of the company’s data was unstructured. Many companies are interested in using GenAI to help manage and provide access to their own data and documents, typically using an approach called retrieval-augmented generation, or RAG. But some companies haven’t worked on their unstructured data much since the days of knowledge management 20 or more years ago. They’ve been focused on structured data — typically rows and columns of numbers from transactional systems.”
Read the full article, “Five Trends in AI and Data Science for 2025,” by Thomas H. Davenport and Randy Bean.
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Here is a direct link to the complete article.
Laurianne McLaughlin is senior editor, digital, at MIT Sloan Management Review.