AI is no longer a technology initiative. It is now a direct test of executive leadership.
Moving AI from pilots to production is harder than most executives expected—and the issue isn’t the technology. We did research to find the real barriers are the systems, data, and workflows the enterprise relies on.
If your pilots are stuck, you’re not alone. Nearly every enterprise is hitting the same wall. But your teams aren’t waiting on better models, they’re waiting on your direction.
The organizations pulling ahead aren’t doing so because they have better algorithms. They’re winning because their leaders demanded operational discipline: clean data, enforceable governance, and AI embedded where results can be measured. That discipline is the difference—and it’s the decision that will determine which leaders will win the AI race and which ones will get left behind.
95% |
of AI projects fail to deliver measurable ROI MIT Sloan (2025) |
80% |
of AI projects never reach production Gartner 2025 Hype Cycle (2025) |
74% |
of CEOs believe failing to deliver AI results puts their jobs at risk Dataiku / Harris Poll |
64% |
of executives report “AI fatigue” within their organization Deloitte 2025 Executive Pulse |
58% |
of employees say their company “talks more about AI than it delivers” PwC 2025 |
Our competitors seem to be getting real results from AI. Why can’t we?
Our teams are stuck in perpetual experimentation and I can’t tie that work to results.
AI isn’t broken. It’s our systems and processes. We are building AI on sand.
You have 12 months get AI working or your time is up…
for both companies and the executives running them.
Gartner forecasts AI surging to
$2T market in 2026
Gartner Forecast Alert: AI in IT Spending, 2Q25
88% |
“We’re doing AI” is not a strategyof organizations say they use AI in at least one business function McKinsey & Company State of AI 2025 Survey |
24% |
“Go figure it out” isn’t leadershipof companies have a documented AI strategy in EisnerAmper’s “Artificial Intelligence in the Workplace” Report |
66% |
“We’re exploring” will not workof board members admit they don’t fully understand their org’s AI operating risks Deloitte Board Readiness Survey |
Companies that have made the shift with AI
CrowdStrike’s AI initiatives delivered a 100% improvement in the Inquiry to Open Opportunity conversion rate through a sophisticated, region-specific behavior scoring system. They also achieved over 98% accuracy in persona assignments using an AI-based LLM for segmentation, which eliminated manual maintenance and enabled precise, global targeting.
The new process makes sales and marketing more efficient and accurate by successfully classifying over 90% of all leads and 94% of contacts. The contribution from AI resolves complex job title exceptions that older methods miss, ensuring that teams consistently prioritize the most valuable, high-impact opportunities.
Good news
There is a clear path for teams to take AI from pilot to production.
Bad news
The teams can’t create the conditions to take the path, only executives can.
The executive imperative
Own the three forces that determine whether AI reaches production. Your teams will not be able to scale AI without it.
The path to scaling your AI initiatives into production
If leaders don’t fix the environment, their teams can’t guarantee the outcomes. AI won’t reach production, not because your people aren’t capable, but because the conditions required for success don’t exist yet.
Your teams already know what to build. Your vendors already know what to deliver. Your competitors already know the opportunity.
The advantage now shifts to the executives who create the environment where AI can actually scale.
What follows outlines what must be true for AI to move from pilots to production.
| Executive responsibilities | Operational pillars |
| Governance & Security | Prompt management, hybrid integration |
| Accountability | KPI & ROI measurement, Hallucination management |
| Data directive | Model orchestration, Context orchestration |
1
Governance & security
If AI creates financial,
reputational, and security
risk for your company, it’s
not worth doing.
2
Accountability
Someone must own the
results and ensure the
truth is actively being
managed
3
Data directive
The companies that treat
data as infrastructure are
the ones turning AI from
promise into profit
With executive support, your teams can do the operational work.
control cost
of your tech stack
improve trust
GOVERNANCE & SECURITY
ACCOUNTABILITY
Data directive
Context
Orchestration
Prompt
Management
Model
Orchestration
Hallucination
Management
Hybrid
Integrations
KPI & ROI
Measurement
data it needs
as system configurations
portfolio of AI tools
This unlocks your AI capabilities as a part of production
When data doesn’t make sense, neither does AI.
The vision being sold by AI vendors and visionaries is that every executive will have a co-pilot on their desktop and AI will serve up the answers instantaneously…..
AI is promising your executives a wormhole directly to your company’s raw data. Maybe this is a good thing, because AI may finally give the executives an unadulterated view of how bad most companies’ data quality is. Maybe this will finally make the executives care about and invest in their data quality and infrastructure.
-Ed King, CEO Openprise
