With over 25 years of executive and entrepreneurial experience in enterprise software and a track record of successful business exits, Pete’s leadership gives ModelOp customers, partners and employees a high level of trust and confidence in the company and its future.
The Promise—and the Reality—of Enterprise AI
Enterprise investment in AI is skyrocketing. IDC projects global spending on AI and generative AI will double to $631 billion by 2028. Yet for all the boardroom ambition and budget allocation, most organizations remain in the early stages of execution.
ModelOp’s 2025 AI Governance Benchmark Report titled AI’s Time-to-Market Quagmire: Why Enterprises Struggle to Scale AI Innovation, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a sobering disconnect: while more than 80% of enterprises have 51 or more GenAI use cases in the proposal phase, only 18% have more than 20 models in production. Most GenAI projects still take 6 to 18 months to go live—if they get there at all.
This gap between aspiration and execution is not a failure of innovation. It’s a failure of operationalization. And it’s why governance—done right—is no longer just about compliance. It’s about accelerating time to value. This is why speed, scale, and trust must be built into AI innovation.
Why Are Enterprises Stuck?
The biggest barriers to scale aren’t technical—they’re structural. The report identifies several major challenges:
- 58% cite fragmented systems as a top obstacle to adopting governance platforms.
- 55% still rely on manual processes—like spreadsheets and email—to manage AI use case intake.
- Only 23% have implemented standardized intake, development, and model management processes.
- Just 14% perform AI assurance at the enterprise level, increasing the risk of duplicated work and inconsistent oversight.
These obstacles create a “time-to-market quagmire,” where projects stall, ROI is delayed, and the business loses trust in the AI pipeline.
These findings echo the Wall Street Journal’s recent coverage on AI’s return-on-investment problem: “Companies Are Struggling to Drive a Return on AI. It Doesn’t Have to Be That Way.” The article underscores that most organizations still lack the infrastructure, discipline, and lifecycle alignment to scale AI responsibly and efficiently—just as our benchmark data shows.
AI Governance as an Accelerator—Not a Brake
There’s a persistent myth that governance slows down innovation. Our findings—and our work with clients—prove the opposite. Enterprises that embed AI governance early in the lifecycle are able to:
- Cut time to production by 2x
- More than 10x increase the number of models managed effectively at once
- Ensure a 100% industrialized AI lifecycle in which models follow enterprise policies and processes from intake to retirement
AI lifecycle automation is essential. Without it, accountability slips, documentation is lost, and audits become fire drills. With it, enterprises enforce policy, track performance, and move fast—without losing control.
The Strategic Shift: Governance as a Driver of Innovation
A major shift is happening: governance is no longer viewed as a risk function alone. In our survey:
- 46% say their Chief Innovation Officer is now accountable for AI governance—more than 4X the number who said Legal or Compliance.
- 54% have budgeted for AI Portfolio Intelligence to track value and ROI at the enterprise level.
- 36% have budgeted at least $1 million annually for AI Governance software - demonstrating its priority.
This strategic repositioning reflects a new mindset: AI governance is the mechanism that enables scale, speed, and trust. It’s how executives ensure alignment between business priorities and AI investments—and make smarter decisions about which models to accelerate, and which to retire.
What the Leaders Are Doing Differently
Enterprises that are pulling ahead are those treating governance as an enabler of innovation. They are:
- Standardizing intake, development, and model review.
- Centralizing inventory and documentation.
- Automating governance checkpoints across the lifecycle.
- Embedding traceability, assurance, and accountability at scale.
One financial services firm profiled in the report saw a 2x increase in speed to production and an 80% drop in issue resolution time after adopting ModelOp’s lifecycle automation platform. These are the kinds of gains that drive real, measurable business value—and give leaders the confidence to double down on AI.
Download the Report: Benchmark Your AI Strategy
The gap between AI ambition and execution is real—but solvable. With the right structure, automation, and leadership, enterprises can bring AI to market faster, at scale, and with trust.
If you're leading AI, innovation, or digital transformation at your company, I encourage you to download the full report:
👉 Get the Report: AI’s Time-to-Market Quagmire
This is not just a benchmark. It’s a blueprint for how to move from stuck to scaled in the age of enterprise AI.
