Artificial intelligence is prompting organizations to rethink how work gets done. Significant experimentation is already underway across industries, changing how software is developed, how research and analysis are conducted, and how organizations make decisions and deliver value.

The larger leadership challenge is not simply adopting new tools. It is understanding how work, workflows, and business processes need to evolve in an AI-enabled environment. The question is no longer how organizations implement AI. It is how leaders redesign work to take advantage of what AI enables.

That question runs through AI for Leaders, a module inside Boston University’s online AI master’s portfolio where faculty bring leaders from industry and academia into live sessions with students who are themselves experienced professionals.

Learning from Practitioners Navigating the Change

These conversations are not simply an addition to the curriculum – they are an integral part of it. Industry leaders bring students into work that is still unfolding, sharing not only successes but also the uncertainty, tradeoffs and decisions that accompany AI transformation. The right approach often depends on an organization’s context, goals and constraints.

Consider how many large financial institutions began their AI journey — not with enterprise-wide transformation, but by introducing coding assistants for their own software developers. One founder whose company builds explainability software for financial institutions described a deliberate progression: start with a narrowly defined internal use case, establish governance and guardrails while the stakes are relatively low, and only then expand into applications that affect cost, customers or revenue. The lesson is not the technology itself but the sequence. Organizations often discover what effective governance looks like only by starting small.

Similar leadership challenges appear in different contexts. A session on digital marketing for mid-sized businesses and nonprofits described organizations that measured success through follower counts while failing to connect marketing activity to the business outcomes. The parallel to AI was immediate. Many organizations are still measuring what is easiest to measure rather than what creates meaningful value.

One observation stood out. The divide is often not between organizations that understand AI and those that do not. It exists within organizations themselves. Mid-level professionals are already experimenting with AI in their daily work, while senior leaders may still be determining how—or whether—to move forward. Closing that leadership gap may be one of the most important challenges organizations face.

Another discussion illustrated how dramatically work itself is changing. One industry leader recalled that the defining achievement of an engineer’s career has been completing a project involving roughly a quarter-million lines of code. Today that same engineer might write only a few thousand lines and AI would generate the rest. The job has not disappeared – it has fundamentally changed. Success increasingly depends on understanding, reviewing, validating and maintaining code that humans did not write, and often at a scale no organization has previously managed. New workflows, new roles and new skills are emerging faster than organizations can define them.

Students also bring their own questions into the conversation. One asked a Microsoft executive how to choose among competing models, and whether that judgment comes from experience or from a defined process. The answer was not to recommend a preferred model. Define what success looks like for the use case. Build an evaluation that reflects it. Test candidates against it. Expect to redo the exercise as the options change. That is what AI transformation asks of leaders: not a preferred tool, but a way of deciding.

The Data Behind the Gap

Research published by Russell Reynolds Associates shows how wide the distance between experimentation and transformation has become. In 2026, 35 percent of leaders reported that generative AI was fully implemented in their team’s day-to-day workflow, up from 19 percent a year earlier. Experimentation is now the norm.

The benefits, however, diverged in revealing ways. Sixty-seven percent of leaders reported higher team productivity, which is the gain individuals realize from better tools. Only 25 percent reported increased profitability and 22 percent reported new revenue – outcomes that require organizations to redesign work rather than accelerating existing processes.

The supporting structures tell the same story. Thirty-five percent said their organization had a clearly defined AI policy, and 27 percent believed stakeholders understood and referenced it. Asked which skills matter most in their organizations, 55 percent identified strategic thinking, against 17 percent for technical literacy and 11 percent for financial acumen. Leaders increasingly recognize that the challenge is organizational transformation — not simply technology adoption.

Transformation Requires More Than One Discipline

AI transformation requires perspectives across technology, business, operations, governance, ethics, and domain expertise at the same time.

This is why Boston University designed its portfolio of online AI master’s programs, to draw on expertise across business, computing, data science, engineering, education, and technology. AI leadership increasingly requires leaders who can connect technical capabilities with strategy, governance, ethics, transparency, bias mitigation, and organizational change.

The cohort itself becomes part of the educational experience. An engineer may identify a technical constraint a business leader has not considered. A governance specialist may raise a risk that changes how a use case is designed. A classmate from another sector may offer an approach that can be adapted. Students in the Online Master of Science in Enterprise AI, for example, come from organizations including AWS, Accenture, American Express, Boeing, Charles Schwab, Cummins, Intel, Intuit, Kroger, and Lockheed Martin.

Find the Leadership Challenge You Are Preparing For

These programs differ less by subject than by the decisions they prepare you to own.

AI is changing more than the tools organizations use – it is changing how work is designed, how decisions are made, and how value is created. Preparing leaders for that future requires more than technical expertise. It requires the ability to navigate organizational change, collaborate across disciplines, and make thoughtful decisions in an environment that continues to evolve. That is the experience these programs are designed to provide.

Explore Boston University’s interdisciplinary online AI programs →

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