Coordinating Global IT Resources Effectively

Most of its issues can be ironed out one way or another. Now, companies must begin to think about how agents can allow new ways of doing work.

Companies can likewise construct the internal capabilities to develop and evaluate agents involving generative, analytical, and deterministic AI. Successful agentic AI will need all of the tools in the AI tool kit. Randy's latest survey of data and AI leaders in big companies the 2026 AI & Data Management Executive Standard Survey, conducted by his instructional firm, Data & AI Management Exchange revealed some good news for information and AI management.

Almost all concurred that AI has led to a higher focus on information. Perhaps most remarkable is the more than 20% boost (to 70%) over last year's survey outcomes (and those of previous years) in the percentage of respondents who believe that the chief data officer (with or without analytics and AI consisted of) is an effective and recognized function in their companies.

In short, support for data, AI, and the management function to manage it are all at record highs in big business. The just challenging structural concern in this image is who ought to be handling AI and to whom they must report in the company. Not surprisingly, a growing percentage of business have named chief AI officers (or a comparable title); this year, it depends on 39%.

Just 30% report to a chief data officer (where our company believe the role must report); other companies have AI reporting to business leadership (27%), technology leadership (34%), or transformation management (9%). We think it's likely that the varied reporting relationships are contributing to the extensive problem of AI (especially generative AI) not delivering enough value.

Will Enterprise Infrastructure Support 2026 Tech Growth?

Progress is being made in value realization from AI, however it's most likely insufficient to validate the high expectations of the technology and the high appraisals for its vendors. Maybe if the AI bubble does deflate a bit, there will be less interest from multiple different leaders of companies in owning the technology.

Davenport and Randy Bean forecast which AI and data science trends will improve business in 2026. This column series looks at the most significant information and analytics difficulties facing contemporary business and dives deep into effective use cases that can assist other companies accelerate their AI progress. Thomas H. Davenport (@tdav) is the President's Distinguished Professor of Information Innovation and Management and professors director of the Metropoulos Institute for Technology and Entrepreneurship at Babson College, and a fellow of the MIT Effort on the Digital Economy.

Randy Bean (@randybeannvp) has been an adviser to Fortune 1000 organizations on information and AI leadership for over four decades. He is the author of Fail Quick, Learn Faster: Lessons in Data-Driven Leadership in an Age of Interruption, Big Data, and AI (Wiley, 2021).

Managing Distributed IT Resources Effectively

What does AI do for business? Digital transformation with AI can yield a variety of benefits for services, from cost savings to service delivery.

Other benefits companies reported achieving include: Enhancing insights and decision-making (53%) Lowering costs (40%) Enhancing client/customer relationships (38%) Improving products/services and cultivating development (20%) Increasing income (20%) Income growth mostly stays an aspiration, with 74% of companies wanting to grow earnings through their AI efforts in the future compared to simply 20% that are already doing so.

How is AI changing business functions? One-third (34%) of surveyed organizations are starting to use AI to deeply transformcreating new items and services or reinventing core processes or company models.

Maximizing AI ROI With Modern Frameworks

The staying third (37%) are utilizing AI at a more surface area level, with little or no change to existing processes. While each are catching performance and performance gains, only the first group are truly reimagining their businesses instead of optimizing what currently exists. Additionally, different kinds of AI innovations yield various expectations for effect.

The business we spoke with are currently deploying self-governing AI agents across diverse functions: A monetary services business is building agentic workflows to immediately catch conference actions from video conferences, draft interactions to advise individuals of their commitments, and track follow-through. An air provider is using AI agents to assist consumers complete the most typical deals, such as rebooking a flight or rerouting bags, maximizing time for human representatives to attend to more intricate matters.

In the general public sector, AI representatives are being utilized to cover labor force shortages, partnering with human workers to complete key procedures. Physical AI: Physical AI applications cover a large variety of industrial and commercial settings. Typical use cases for physical AI consist of: collective robotics (cobots) on assembly lines Examination drones with automatic reaction capabilities Robotic selecting arms Autonomous forklifts Adoption is especially advanced in production, logistics, and defense, where robotics, autonomous cars, and drones are currently reshaping operations.

Enterprises where senior leadership actively shapes AI governance attain significantly greater organization value than those entrusting the work to technical teams alone. Real governance makes oversight everyone's role, embedding it into efficiency rubrics so that as AI manages more tasks, people handle active oversight. Self-governing systems likewise heighten requirements for information and cybersecurity governance.

In regards to policy, effective governance integrates with existing threat and oversight structures, not parallel "shadow" functions. It concentrates on recognizing high-risk applications, implementing responsible style practices, and guaranteeing independent validation where proper. Leading companies proactively keep an eye on developing legal requirements and develop systems that can demonstrate security, fairness, and compliance.

Building High-Performing IT Teams

As AI abilities extend beyond software into gadgets, equipment, and edge locations, organizations need to assess if their technology structures are ready to support prospective physical AI implementations. Modernization should produce a "living" AI backbone: an organization-wide, real-time system that adapts dynamically to business and regulatory modification. Secret ideas covered in the report: Leaders are allowing modular, cloud-native platforms that safely connect, govern, and incorporate all information types.

Forward-thinking companies assemble functional, experiential, and external information flows and invest in evolving platforms that expect needs of emerging AI. AI change management: How do I prepare my labor force for AI?

The most effective organizations reimagine jobs to flawlessly integrate human strengths and AI capabilities, guaranteeing both aspects are utilized to their maximum potential. New rolesAI operations supervisors, human-AI interaction professionals, quality stewards, and otherssignal a deeper shift: AI is now a structural component of how work is organized. Advanced companies improve workflows that AI can execute end-to-end, while humans focus on judgment, exception handling, and strategic oversight.