AI adoption is accelerating, but where is it driving growth and impact in 2026? And how are organizations across industries approaching adoption at scale? Those questions framed the conversation and set the tone for an evening focused on practical, real-world impact.
The M.S. in Technology Management (TMGT) program recently welcomed students, alumni, faculty, and industry leaders to the event, 2026 Technology Management Trends: How AI and Emerging Technologies Are Shaping Business Transformation. The event was designed to translate leading-edge ideas into practical takeaways grounded in how organizations are implementing AI today.
The event was held in partnership with Deloitte, reflecting the TMGT program’s commitment to industry collaboration and applied learning. In opening remarks, Senior Associate Dean of Academic Affairs Erik Nelson emphasized how partnerships like this help prepare technology leaders to navigate complexity, drive innovation, and translate emerging technologies into meaningful organizational impact—while also expanding mentorship, internship, and employment pathways for students.
Moderated by Shahryar Shaghaghi, TMGT program director and professor of professional practice, the panel featured Deloitte leaders across strategy, supply chain and operations, industrial and private equity advisory, M&A technology, and enterprise data/AI leadership, including Lindsey Berckman, Ashish Midha, Joydeep Mukherjee, Juan Tello, and Michael Wilson. Together, they explored themes from Deloitte's Tech Trends 2026 and what those trends mean for industry-wide transformation.
A major thread throughout the discussion was that the hardest work is often not the model itself. The panel repeatedly returned to constraints such as legacy modernization and technical debt, and how those foundational realities can limit the value of AI if not addressed intentionally. From an enterprise execution perspective, Juan Tello emphasized that scaling depends less on isolated pilots and more on fundamentals: data readiness, integration, and the operationalization of AI across functions.
The discussion also explored how “AI goes physical” is no longer theoretical, extending into warehouses, robotics, and industrial environments, where transformation must occur alongside existing assets and long replacement cycles. Lindsey Berckman brought an operations lens to this shift, noting that success in physical environments depends on reliability, integration with existing workflows, and practical constraints that don’t exist in purely digital rollouts. The takeaway was clear: scaling AI in operations is as much about execution and change management as it is about technology.
Panelists also underscored that business transformation is ultimately a people, process, and technology challenge. It requires modernizing foundations, building trust through governance, and developing teams that can work effectively with intelligent systems. Michael Wilson reinforced the importance of maintaining a value-driven approach at the center of AI adoption. Rather than treating pilots and proofs of concept as the finish line, he pointed to the need for clear measurement, prioritization, and accountability, so AI investments tie directly to business performance, not just activity.
Importantly, the panel focused on what is changing in the workplace now. Ashish Midha spoke about how AI is compressing timelines for analysis and synthesis, reshaping expectations for early-career roles and day-to-day decision-making. As routine outputs become faster to generate, the skills rising in importance are problem framing, business judgment, and the ability to interpret and validate AI outputs in context. The shift is not about replacing roles but about raising the bar on fluency and responsibility.
From a platform and enterprise change perspective, Joydeep Mukherjee explained how AI strategy is increasingly shaping how organizations evaluate platforms, integrate capabilities, and unlock value. The discussion highlighted that buying or building AI is only the first step; value is realized through integration, alignment of operating models, and execution after implementation. In that sense, AI is not just a technology decision; it is becoming central to how organizations approach transformation and long-term competitive advantage.
A clear takeaway from the panel was that AI is moving fast, and the basics still matter. As adoption matures, the questions are shifting from “What can be built?” to “What can be scaled, governed, and measured?” Technical debt and legacy infrastructure still set the ceiling for scale, while data readiness and integration determine whether solutions become repeatable across the enterprise. As adoption scales, it is essential to build fluency with the technology while also doubling down on the human skills that make it useful: problem framing, judgment, communication, and ethical leadership—the ability to guide teams, organizations, and decisions in environments where humans and intelligent systems increasingly collaborate. These capabilities will be critical to ensuring AI delivers measurable business impact.
Watch the recording of the event here.
About the Program
The Master of Science in Technology Management at Columbia University prepares graduates to lead digital transformation, and align technology and business strategy with an ethical lens. Through experiential learning, industry partnerships, and Columbia-supported research, students gain fluency in digital platforms and emerging technologies, and learn to design human-centered solutions that drive innovation and sustainable impact.
The program is available for part-time or full-time enrollment online or on campus in NYC. Learn more about the program here.
