10 Urgent AI Takeaways for Leaders

“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.”  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.

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.  MIT SMR gathered 10 of its 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.  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.”
2. Make smart AI tech-debt trade-offs. The 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. 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.”
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.”
4. AI success requires building a data-driven culture.  For many leaders, the challenge is not buying advanced analytics tools or building accurate technical solutions. The real hurdle is subtle yet much more important: fostering an environment within an organization where individuals instinctively turn to data anytime they must make a decision. This is the real meaning of being data driven or creating a data culture.”
5. Philosophy could eat your AI strategy.  “Philosophy is eating AI: As a discipline, data set, and sensibility, philosophy increasingly determines how digital technologies reason, predict, create, generate, and innovate. The critical enterprise challenge is whether leaders will possess the self-awareness and rigor to use philosophy as a resource for creating value with AI or default to tacit, unarticulated philosophical principles for their AI deployments. Either way — for better and worse — philosophy eats AI. For strategy-conscious executives, that metaphor needs to be top of mind.”
6. GenAI can turbocharge how organizations learn.  “Combined with traditional AI, generative AI expands the scope of potential improvement in many processes and decisions and the ease with which this new knowledge can be applied. This, in turn, creates the potential for a positive compounding effect on organizational learning, with human and machine agents working in concert to create new competitive advantages.”
7. GenAI versus analytical AI: Pick your projects wisely.  “Leaders should recognize that generative AI and analytical AI [tools] are complementary rather than interchangeable. GenAI focuses on efficiency and automation, like using AI-powered chatbots to increase call center productivity, whereas analytical AI enhances strategic decision-making, like determining the best time or offer for each customer contacted by the call center.
8. Bring your own AI (BYOAI) can’t be banned.  It  can only push employees to find unofficial workarounds, potentially bypassing established governance frameworks. This can ultimately amplify the very risks leaders aim to mitigate.
9. Pay more attention to GenAI app evaluation.  Underinvest in evals can result into uneven progress and, ultimately, canceled GenAI projects or flawed applications that fail to achieve the business goal.
10. What-if questions call for a new machine learning tool.  “Causal ML — an emerging area of machine learning — can help to answer … what-if questions through causal inference. Similar to how marketers use A/B tests to infer which of two ads is likely to generate more sales, causal ML can inform what might happen if managers were to take a particular action.