The organizations succeeding with AI are not the ones that moved fastest. They are the ones that built in the right order.
Is your organization investing in AI but struggling to show it?
Are your teams running pilots that never reach production, or deploying tools that decision-makers don’t trust?
Are you starting to wonder whether the problem is your data, your sequencing, or something no vendor presentation has named yet?
You are not alone, and you are not behind.
According to BARC‘s 2026 research across hundreds of data and business leaders, only 23% of organizations qualify as true AI Leaders. That number has barely moved in three years. The gap is not closing. And the organizations stuck on the wrong side of it are not short on ambition or budget, they just skipped steps. So what do the 23% know that everyone else doesn’t?
This report synthesizes findings from the BARC Data & Analytics Retreat 2026, one of the most rigorous independent research programs in data and analytics, and translates them specifically for mid-market data teams and business leaders.
Not Fortune 500 playbooks, not advice designed for 40-person data engineering teams. This is for the team of 3 to 15 people trying to build something that actually works ; under pressure, with limited resources, and handed an AI strategy that was built for an organization three times their size.
ClicData works with mid-market data teams every day. That proximity gives us a vantage point the research alone cannot offer: we see which problems are real blockers, which ones resolve themselves, and where mid-market teams consistently punch above the research averages. The evidence here is BARC’s but the interpretation is ours.
Three chapters, three gaps, and fifteen actions you can take this quarter.
What You’ll Find Inside
- Why 70% of organizations can’t actually use the data they already have, and the two preparation activities that separate AI-mature organizations from everyone else
- The “Three Waves” framework that explains why most teams are buying Wave 3 solutions to Wave 1 problems, and how to honestly assess where you actually are
- What Cox Enterprises, MTN Ghana, and Unilever did before deploying agentic AI, and why the order of operations mattered more than the technology itself
- The governance exposure hiding inside undiscovered data, and why it compounds the moment your AI agents start acting autonomously
- Why features are no longer a competitive moat – for vendors or internal data teams – and what replaces them.
Why This Matters Now
The external environment (geopolitical volatility, supply chain disruption, trade policy shifts) is creating a pace of change that manual planning processes simply cannot absorb. The organizations that navigate it effectively are those that can update plans quickly, scenario-plan under uncertainty, and surface variance drivers in near real-time. AI makes that possible. But only if the data feeding those systems is reliable, current, and well-governed.
The urgency is real. The answer is not to skip the foundation, but to build it faster.
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Report based on BARC research studies by Merv Adrian, Kevin Petrie, Dr. Carsten Bange, and Kelley Lynn Kassa, and strategic analysis by Donald Farmer of TreeHive Strategy. Interpreted for mid-market organizations by the ClicData team.
