Enterprise Execution Has a New Definition

The markets sent a clear signal this year. Shares of major enterprise software providers have fallen more than 30 percent on average over the past year. Shareholders are questioning whether traditional models can sustain value in an AI-driven era. The message isn't that the industry failed. It's that the definition of value has changed faster than the models built to deliver it.

Enterprise transformation has reached an inflection point. The ambition hasn't changed. What needs to change is what execution actually means and how it is orchestrated.

What Enterprise Leaders Are Actually Saying

At USA House in Davos earlier this year, our founder Kabir Nagrecha articulated what many leaders are experiencing but struggling to name: AI's value is visible, but only at the margins. Productivity gains remain confined to individual and team-level wins rather than enterprise-wide impact.

The dominant question has shifted. It's no longer "Can AI help?" It's "Can AI execute safely inside real workflows?"

That's a harder question, and the gap between aspiration and execution is measurable. A 2025 MIT NANDA study analyzing 300+ enterprise AI initiatives found that 95% of pilots deliver zero measurable return. Companies are spending an estimated $30-40 billion annually on generative AI, yet McKinsey finds that only 17% of organizations report 5% or more of their EBIT coming from AI, and over 80% see no tangible enterprise-level impact at all.

This isn't a technology problem. AI works. The gap is structural. It lives in the space between a proof-of-concept and a workflow that actually runs the business.

Across conversations with enterprise executives over the past year, a clear pattern has emerged. The gaps they're navigating aren't random, and they aren't closing on their own:

Fragmented execution. AI tools perform in isolation. They stall when they encounter the actual complexity of enterprise environments: legacy data structures, multi-system dependencies, exception handling, and approval chains that weren't designed with automation in mind. The tools aren't the bottleneck. The architecture of how they get embedded is.

Governance and trust. A 2024 PwC survey found that 80% of business leaders don't trust agentic AI to handle fully autonomous workflows or financial tasks. The capability exists. The trust infrastructure, including governance frameworks, auditability, and cross-system accountability, largely doesn't yet.

The pilot-to-production gap. Only 26% of organizations have the capability to move a proof-of-concept into production. Large enterprises take nine months on average to scale a pilot. Most of the value is sitting in that gap, waiting.

Productivity confined to individuals. More than 90% of employees are using personal AI tools at work, often generating higher ROI than official enterprise programs. The gap between what individuals can accomplish and what organizations can systematically embed is widening. That asymmetry is the real problem to solve.

Enterprise IT Budgets at an Inflection Point

A disproportionate share of IT spend flows toward maintenance, legacy complexity, and project-based engagements. The structural challenge is real: modernization crowds out innovation because so much capital is committed to keeping current systems viable.

The cost of this is becoming visible. Enterprises lost more than $104 million in 2024 due to underused technology and low adoption, investments made but never operationalized. Only 14% of U.S. CFOs report clear, measurable impact from their AI investments to date, yet 66% expect to see that impact within two years. That expectation gap is the window.

C-suite leaders want transformation velocity. Their budgets are still structured around stability. The opportunity isn't incremental cost cutting. It's reallocating spend from non-value work toward execution that compounds over time.

The industry has done a remarkable job building the foundation. SAP implementations, systems integrations, data migrations — these programs created the infrastructure that enterprises run on today. The question organizations are now asking isn't whether that work mattered. It's what comes next. Because the environment has changed in ways that require a different kind of motion:

• AI can absorb workflow complexity that once required specialist labor

• Time-to-value is measured in weeks, not implementation cycles

• Internal capability needs to grow continuously alongside external partnership

• Transformation windows are compressing and the pressure is real

70% of digital transformation initiatives still fail to meet their objectives in 2025, at an estimated cost of $2.3 trillion per year globally. The failure mode isn't ambition. It's the mismatch between how transformation is structured and what the current environment demands.

The CIO Mandate Has Quietly Become a Different Job

The CIO sitting in that board meeting already knows what the data says. They've seen the McKinsey slides. They've watched pilots that performed beautifully in controlled environments stall the moment they touched real workflows. They're managing a backlog that isn't shrinking fast enough, upgrade cycles that keep compressing, and requests from business leaders who want AI outcomes but aren't yet structured to co-own the work required to get there.

What rarely gets named is how structurally exposed that position is.

The mandate has expanded in ways that have no established playbook. It now includes building enterprise execution capability, driving adoption readiness across functions that don't report to IT, and translating AI potential into language a CFO will approve and a CEO will champion. That is a fundamentally different job than it was five years ago, and most organizations haven't restructured around it.

The 86% of CFOs who cite legacy systems as a barrier to AI adoption aren't pushing back on AI. They're asking the same question the CIO is: how do we build something that compounds rather than just costs? How do we move from funding projects to building capability? How do we get to a place where the next transformation cycle is faster than the last one, not the same speed at higher cost?

That question is at the center of what Tessera is built around.

Our view is that AI's potential at enterprise scale is only realized when it is embedded into the core workflows that drive business outcomes, not validated in isolation and handed over. The goal isn't a successful implementation. The goal is an organization that executes differently and keeps getting faster. AI is the catalyst. The real outcome is a fundamentally changed execution model.

In practice that means structuring engagements around business process outcomes rather than project milestones. Embedding AI into enterprise workflows from day one, not as a layer added after go-live. Treating internal capability transfer as a primary deliverable, so what gets built continues to compound after the engagement closes. And operating at a pace that matches how enterprise budgets are actually approved and how leadership patience is actually structured.

The organizations capturing real AI value aren't the ones with the biggest budgets. They're the ones that changed how they execute, and then kept changing. That is what we are here to build.

What Compounding Execution Actually Looks Like

At Tessera, we think about this in three motions. Map the workflows where AI can absorb complexity and accelerate time to value. Govern the execution so it runs reliably inside real enterprise environments. Execute in cycles that compound, so each deployment makes the next one faster. That sequence is what separates organizations that capture AI value from those still waiting for their pilots to scale.

McKinsey's research confirms what we see in the market: organizations with the most ambitious AI agendas capture the greatest value. Companies attributing 5% or more of EBIT impact to AI don't treat it as a feature. They embed it into transformation. They redesign workflows, scale faster, and compound value through disciplined execution.

As AI becomes governed and operationalized across enterprise environments, the measure of a successful engagement changes. Leaders will buy outcomes measured in throughput and time to value, not team size or depth of expertise. Competitive advantage moves from services intensity to execution capability.

The organizations that win will be those who turn transformation into a compounding internal capability rather than a recurring external investment. Compounding capability means each cycle of improvement makes the next one faster and more valuable.

AI's real power isn't automation. It's restructuring how change gets sustained, and how quickly an organization learns to absorb it. Learning and execution compound. Spending does not.

If that's the problem on the table, we'd like to show you how we're solving it.

Sources:

1. McKinsey State of AI: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-how-organizations-are-rewiring-to-capture-value

2. MIT NANDA GenAI Divide: https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

3. RAND AI Failure Report: https://www.rand.org/pubs/research_reports/RRA2680-1.html

4. PwC AI Trust Survey: https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/ai-trust-survey.html

5. CFO.com AI ROI Survey: https://www.cfo.com/news/ai-roi-survey-cfos/

6. MeltingSpot / Gartner: https://meltingspot.io/blog/digital-transformation-failure-rate