Over the past year, I’ve had the opportunity to spend time with CIOs and CDAOs across industries and geographies, from Gartner C-level communities to strategic customer partnerships to executive roundtables. Despite differences in maturity, size, and industry, the themes are remarkably consistent.
- Organizations feel pressure to move faster with AI
- They are challenged with scaling AI and analytics across the enterprise while maintaining trust and governance alongside innovation
- Many are struggling to realize the promise of an AI reality with meaningful business impact
The real challenge behind AI at scale
Most organizations are not facing a lack ambition or access to technology. They are struggling because AI exposes long-standing gaps in how data, analytics, and decision-making operate inside the business.
Simply centralizing data into a platform to feed AI is not adequate on its own to create effective AI solutions. Neither are point AI tools or standalone copilots. Successful AI systems require quality data that’s grounded with appropriate business context and business logic, and those foundations are often overlooked during development.
What I hear most often from business leaders is a wary sense of urgency:
- AI promises speed, but IT and financial leaders fear loss of control or understanding.
- AI promises scale, but analysts, already overwhelmed, struggle to reimagine their work, or, worse, reject AI for fear of replacing their jobs.
- AI promises insight, but business teams have difficulty interpreting AI’s results and can’t see or trust how results are produced.
This is why many AI initiatives stall after early pilots. The models may work, but the organizational and operating foundations do not.
Why business-led AI matters
One of the clearest signals coming from Gartner CDAO and CIO communities is this: AI cannot be owned by IT alone.
IT plays a critical role in security, architecture, and governance, but AI only delivers value when it is shaped by the people closest to the business. The analysts, operators, and department leaders who understand the data, the definitions, the context, and the decisions that matter are critical for successful AI solutions.
Scaling AI means equipping everyday knowledge workers with the ability to prepare data, define logic, and operationalize insights while providing them with guardrails that lead to enterprise trust.
This is also where many organizations struggle. They either centralize too much, slowing innovation, or decentralize without a plan, which can lead to risks. The organizations who are realizing meaningful business impact from AI establish a governance framework and operating model that facilitates wide-scale innovation at the edge through their knowledge workers while monitoring and managing critical processes.
A recent Alteryx research report highlights a shift that is already underway. Business and IT leaders expect responsibility for AI workflows to increase by 11% within individual lines of business — moving away from centralized IT over the next three years.
The foundations of AI-native analytics
Across industries, the organizations seeing momentum share a few common characteristics:
They treat data readiness as a foundational AI capability
AI-ready data is not just clean data. It is data enriched with business context, consistent definitions, and transparent logic. When AI systems operate on governed, explainable foundations, trust accelerates instead of erodes.
They elevate the role of the analyst through a culture of innovation
Rather than replacing analysts, AI increases their importance. Analysts become the architects of the logic, rules, and signals that make meaning of AI systems and agents. When that logic is visible, reusable, and governed, organizations can scale insight without scaling risk.
They connect insight to action, consistently turning pilots into production
AI delivers value only when insights lead to outcomes. That requires the fusion of analytics, automation, and AI. No longer do recommendations have to be extrapolated from dashboards, but can instead come from automated, trigger-controlled, actions, easily understood and explained by the business.
This is what it means to move toward AI-native and agentic analytics — not just adding AI on top of existing processes but redesigning how data and decisions flow across the organization.
From principles to practice
These themes aren’t theoretical. We see them play out every day with customers who are moving beyond experimentation and into real operational scale.
One example is Copa Airlines.
Rather than treating analytics and AI as isolated initiatives, Copa focuses on empowering teams across the business with governed, repeatable analytics and automation. By standardizing workflows, embedding governance, and making analytics accessible across departments, they are able to scale confidently, without sacrificing trust or control.
Their experience reflects what many CIOs and CDAOs are discovering right now: the path to AI at scale runs through people, processes, and platforms together.
If these challenges sound familiar, I invite you to join us at the Gartner Data & Analytics Summit in Orlando.
Join our customer session with Copa Airlines to hear from Julio Toro Silva, Chief Technology and Information Officer at Copa Airlines. He’ll share how the company is empowering teams with analytics automation to improve efficiency and decision-making across the business.
Learn more about the session with Copa Airlines on Tuesday, March 10 in the Osceola Room at Gaylord Palms Resort & Convention Center.
I look forward to continuing the conversation in Orlando!