In the face of an uncertain business landscape and rising pressure to prove the business value of AI, a new phase of enterprise AI adoption is emerging: A shift in focus from experimentation to operational accountability.
That transition was a central theme during a CIO.com dinner roundtable held alongside CIO-100 Leadership Live Atlanta. The next wave of AI deployment will be defined less by technological capability and more by governance discipline, data accountability, and measurable business results, according to tech execs attending the event.
The discussion, co-hosted with PwC, brought together technology leaders from industries that included auto tech, higher education, professional services, nonprofit organizations, and professional sports. Across sectors, participants described a similar shift in executive thinking: AI initiatives that began as exploratory pilots are now being evaluated against operational outcomes, cost savings, and improved productivity.
Jeff Baker, PwC Technology Managed Services Leader, hosted the discussion with CIO.com contributing editor Lane Cooper. Baker opened the dialog by observing that organizations are entering a new period. “The technology continues to evolve very quickly,” Baker said. “But organizations still have to connect these capabilities to measurable outcomes and ensure that they are implemented in a way that is sustainable.”
Participants agreed that the early surge of generative AI (genAI) experimentation is giving way to a more disciplined phase. During the past two years many companies launched proof-of-concept (PoC) initiatives to explore how AI tools might improve workflows or automate tasks. The current challenge is determining which of those initiatives can be scaled across enterprise operations.
PwC’s research, The PwC 2026 AI Predictions Report, reported that many organizations are still navigating that transition. In surveys of business leaders, the firm has found that a large share of companies experimenting with AI remain stuck in pilot or exploratory phases, while more than half of CEOs report that their organizations have yet to realize significant financial returns from early AI investments in PwC’s 29th Global CEO Survey released at Davos.
From AI experimentation to operational accountability
One key lesson from early experimentation is that AI projects should always begin with clearly defined business problems. Participants cited examples such as accelerating enterprise software modernization; improving the accuracy and timeliness of operational data; and automating labor-intensive tasks in analytics, coding, and reporting workflows. Others described efforts to strengthen data governance, detect anomalies in complex operational environments, or streamline decision-making across distributed teams. In each case, participants said the objective was not to deploy AI for its own sake, but to address specific operational bottlenecks that affect productivity, cost control, and organizational agility.
To that end, many organizations are embedding AI capabilities into existing modernization initiatives such as enterprise resource planning (ERP) upgrades, analytics programs, and workflow automation efforts versus launching isolated AI experiments.
One executive described how AI-assisted development tools are accelerating large enterprise system modernization projects. As a result, tasks that once required extensive manual coding can now be completed significantly faster through AI-supported development workflows.
Other participants described using AI systems to assist with research, analytics, and operational decision support. In many cases, the value of these tools lies in incremental productivity gains that accumulate across large teams. PwC’s Baker pointed out that even modest improvements in workflow efficiency can produce meaningful results when applied across complex organizations.
The importance of data hygiene and governance
Meanwhile, the expansion of AI initiatives is exposing weaknesses in enterprise data management practices. Several participants said their organizations are discovering that AI adoption quickly reveals gaps in data quality, accessibility, and governance.
AI systems depend heavily on reliable data inputs. When data is fragmented or poorly governed, organizations struggle to produce consistent results. As a result, companies are placing new emphasis on data stewardship, classification, lineage tracking, and governance frameworks.
“Data used to sit in centralized repositories,” one executive said. “Now it exists across business units, platforms, and partners. For this reason, accountability must expand across the organization.”
Several participants noted that this shift is elevating the role of teams responsible for data management, data quality, and data architecture. Functions that were once viewed primarily as technical support activities are increasingly recognized as strategic enablers of AI and advanced analytics.
For many executives at the roundtable, the result is long overdue recognition for teams that have spent years building and maintaining enterprise data foundations.
“Data people have been talking about quality and governance for a long time,” noted one participant. “Now that AI depends on it, the rest of the organization is finally paying attention.”
Leadership and workforce readiness become strategic priorities
While technology and data issues dominated much of the discussion, participants repeatedly returned to the role of leadership in navigating the AI transition. Enterprise technology leaders increasingly act as translators between technical teams and business executives. CIOs must explain how AI initiatives affect operational performance, financial outcomes, and organizational risk.
Participants said the ability to communicate technological change in business terms is becoming a critical leadership capability.
“You have to explain why the change matters,” one executive said. “Leaders want to understand how it improves decision speed, reduces operational risk, or changes the economics of how work gets done. Technology leaders need to connect those outcomes directly to the company’s strategic priorities.”
That communication challenge is becoming more complex as AI initiatives often involve collaboration across departments such as finance, operations, human resources, and legal teams.
“Each of those groups brings different priorities, regulatory obligations, and definitions of risk, which means technology leaders must reconcile competing perspectives before AI initiatives can move forward,” said PwC’s Baker.
Artificial intelligence is not simply another technology cycle to manage. It is forcing organizations to rethink how decisions are made, how risk is governed, and how leaders communicate across the enterprise.
“Companies that succeed in this environment will treat AI as a leadership discipline that connects strategy, operations, and accountability to better navigate rapid technological change and global uncertainty,” he concluded.
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EDITORIAL NOTE: The discussion was held under the Chatham House Rule. The group agreed that, aside from the hosts, no one would be quoted so that insights and observations could be shared without fear or favor.
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Source: News