Insights &
Intelligence
Patterns from enterprise Data & AI work — the signals, decisions, and operating shifts that matter for leaders building tomorrow's data-intelligent organizations.
Most AI programs aren't failing on technology.
They're failing on data.
Across our engagements this quarter, a consistent pattern has emerged: organizations with genuine AI ambition are hitting a ceiling — not because of model quality, vendor selection, or budget — but because the data underneath the AI is ungoverned, inconsistent, or siloed. The technology is ready. The data foundation often isn't.
"The AI programs that are actually in production share one thing: someone made the unglamorous investment in data readiness 12–18 months before the AI project started."
The implication is uncomfortable for organizations that want to move fast: you cannot shortcut the data layer. Predictive models built on inconsistent data produce inconsistent predictions. The organizations seeing the fastest AI ROI aren't the ones who moved first on AI — they're the ones who moved first on data governance, unified semantic layers, and accessible, trusted data products.
There is also a broader shift underway: net-new agents, custom workflows, and intelligence layers are being built on top of existing systems. Salesforce, Snowflake, and established data infrastructure are becoming the foundation for AI-native applications, not the obstacle to them.
The hardest part of AI adoption
isn't the technology.
The conversations I keep having with CIOs and CDOs aren't about which model to use or which platform to bet on. They're about something more fundamental: how do you build organizational confidence in AI outputs when the underlying data has never been fully trusted?
Most organizations have years of accumulated data debt — definitions that differ between systems, metrics that don't reconcile between teams, pipelines built for reporting rather than action. When you layer AI on top of that, you don't get intelligent outputs. You get confident-sounding outputs the business can't trust — which is arguably worse than no AI at all.
"When a VP of Sales and a CFO pull the same revenue metric and see different numbers, the AI program was already in trouble — even before the first model was trained."
What we've seen work: a unified semantic layer, a governed data model every team queries from, a single version of the truth that earns trust through consistency over time. It's unglamorous work — but it is the thing that makes everything else possible.
If you're navigating this tension between pressure to move fast on AI and recognition that the data foundation isn't ready, I'd genuinely like to have that conversation. Reply directly — I read every one.
Three things we heard
at every serious table
ViVE 2026 was the first major conference where the enterprise AI conversation had visibly matured. Less "what is AI?" and more "how do we scale what's working?" Four days, hundreds of conversations with CIOs, CMIOs, and operational leaders. These three themes were consistent across every substantive exchange.
The organizations winning on AI stopped delivering data on demand and started building governed, reusable data products — datasets with owners, SLAs, defined consumers, and version control. Faster AI deployment, less rework, a data team that compounds in value.
The organizations pulling ahead aren't necessarily the ones with the best AI models. They're the ones that can make high-quality decisions faster — from a trusted, accessible, unified data layer that every team queries from and every leader believes.
Every board in 2026 is asking what the organization is actually getting from its AI investment. The programs surviving budget scrutiny are framed in outcome language — revenue cycle impact, risk reduction, operational efficiency — not activity metrics.
The enterprise LLM market is tracking from ~$11B in 2025 toward ~$60B by 2027. The firms building deep LLM provider relationships and Foundational Data Engineering capabilities are establishing positions that will be very difficult to replicate once the market matures.
50 senior data and technology leaders. Three hours. No pitches — just the conversations that don't happen in vendor meetings or board rooms. Here's what the room kept returning to.
Execution Under Uncertainty:
How CXOs Drive Clarity Without Certainty
Markets are shifting faster than strategy decks can keep up. Boards demand precision. Teams demand direction. Data is abundant — but clarity is rare.
Today's CXOs are leading in ambiguity — where decisions must be made before all variables are known, and where the cost of waiting for certainty is often higher than the cost of being wrong.
This session moves beyond theory into real operating frameworks. We'll explore how senior leaders make high-stakes decisions with incomplete information, build decision velocity without creating chaos, and create operating models that tolerate ambiguity.
Part of the East Bay CXO Community — a Teqfocus-led initiative to foster trusted peer relationships and collective innovation in the Bay Area technology leadership circle.
In this episode of TeqTalk, Jas Kaur speaks with Jaspreet Singh, Chief Customer Officer at Empower Pharmacy, about why therapy journeys break and what healthcare leaders must rethink across operations, regulation, experience design, and digital infrastructure.
HIMSS 2026
Find us at Booth #4000 in Las Vegas, March 9–12. We'll be demonstrating our Health Data Cloud framework — how operational and clinical data becomes intelligence that drives measurable outcomes.
Meet Us at HIMSSInsurTech 2026 Spring Conference
Engaging insurance and financial services leaders on how AI and unified data are reshaping risk modeling, member experience, and operational efficiency. Reach out to connect ahead of the event.
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