AI is no longer a question of “if” or “when”. It’s already here. Embedded in pilots, demos, and proofs of concept across nearly every major enterprise. But there’s a catch: most of those AI projects go nowhere.
In fact, the percentage of companies scrapping a majority of their AI initiatives jumped from 17% to 42% this year, according to S&P Global Market Intelligence. While the technology is real, the operating model isn’t.
At ServiceNow, we’ve led AI through shared leadership—not from the top down. The collaboration between technology and business functions can take different forms, but the goal remains the same: make AI deliver measurable business outcomes and avoid siloed innovation. To make this a reality, we’ve built a pact between the CIO and COO that treats AI as a business system and experience layer, with shared outcomes and measurable results. We’ve already realized more than $355 million in annual value from productivity and time savings.
Our strategy is a blueprint that any organization can adopt. If you want to escape pilot purgatory and move AI into production with meaningful business impact, here are five practical ways to optimize AI at scale within the first 90 days.
- Start with the work, not the model
Too many companies get caught up in experimenting with the latest large language model before identifying where it can solve real business problems. Start with three enterprise use cases that directly impact your P&L. Then set public, CFO-approved targets like faster cycle time, higher deflection rate, and lower cost-to-serve.
At ServiceNow, we identified key use cases that drive the most value for employees and customers, starting with help desks. ServiceNow runs a fully autonomous IT service desk, with 90% of employee requests handled by AI. For customer support, 89% of requests are self-served with AI, with 50% faster case resolution times for more complex issues. We’re extending this scalable model across HR, finance, sales, and more. Not a pilot. Not a demo. Real outcomes.
- Fix data chaos with platform power
AI implementation starts with a strong data strategy and ends with experience. Before layering in new models, invest in a platform that accelerates and acts on AI instead of just hosting it. By connecting any model, any agent, any data, and any workflow on a single, secure, AI platform, you can eliminate silos, turn insights into action, and drive adoption and value across the enterprise.
The alternative to betting on a resilient platform with everything built in now is spending lots of time and money to bolt on later. That’s how AI initiatives become disconnected and often fail while still in a pilot.
- Govern AI like a business system
Governance can’t be a one-time committee review of deployed AI models and tools. It must be an operating discipline. It’s critical to establish a central control tower that oversees every agent and model, from provisioning and permissions to observability and retirement.
Think of it like cybersecurity or finance. You don’t scale those functions without oversight. The same is true for AI.
- Redesign work for human + agent teams
The goal isn’t to replace humans. It’s to eliminate the digital friction that slows them down.
Microsoft’s 2025 Work Trend Index shows that employees are interrupted every two minutes by meetings, messages, or alerts. Nearly half of workers say their day feels fragmented and chaotic. That’s not a productivity gap—it’s a structural failure.
That’s why it’s important to reimagine the way work gets done with AI, instead of just automating old processes. Focus on transforming and elevating human roles to create a future that’s human-led and AI-enabled, where AI handles routine, low-to-medium complexity tasks and humans drive strategic priorities, innovation, and relationship building, which is what they do best.
- Make the CIO–COO pact real
Here’s how we structure our partnership:
- One unified backlog: Fund value streams, not departments.
- Freedom within a framework: Create an environment where innovative AI and responsible AI and governance are not mutually exclusive.
- Real-time AI dashboard: Track outcomes like time saved, risk reduced, and sentiment improved.
- Upskilling as the baseline: Incentivize managers for outcome quality, not deployment quantity.
This goes beyond collaboration with co-ownership of bigger business transformation.
A 90-Day AI playbook
Turning strategy into execution doesn’t require a full digital overhaul—it requires structure, speed, and clear accountability. This 90-day playbook breaks down the daunting task of AI transformation into four focused sprints. Each phase is designed to build momentum, prove value early, and give business leaders the clarity they need to scale with confidence.
These steps get AI into production as the building blocks of the autonomous enterprise, where AI agents, data, and workflows operate in sync to drive resilience and growth at scale.
Run this sequence to move from AI pilots to real AI value:
- Days 0–14: Choose three use cases with CFO-approved metrics. Define clear guardrails (e.g., addressing privacy, auditability, bias).
- Days 15–45: Connect the data you already have. Build the control tower.
- Days 46–75: Deploy minimum viable AI workflows. Measure deflection, resolution time, and user satisfaction. This is the time to test, iterate, and improve.
- Days 76–90: Double down on what works. Publish results. Fund the winners. Retire the rest.
What success looks like
You’ll know it’s working when:
- Your board asks, “What’s next? What else can AI help us achieve?”
- Employees spend less time toggling between tools and more time delivering value.
- Governance reviews are boringly predictable, because the system just works.
Why it matters now
IDC estimates generative AI could add up to $22 trillion to the global economy each year by 2030. But that value won’t go to the companies with the most impressive demos. It’ll go to those with the discipline to scale, the governance to trust, and the partnership to lead.
If CIOs and COOs can co-own the AI operating model, AI stops being a headline—and starts becoming a habit. And as AI continues to evolve, this partnership will become the foundation for a new kind of enterprise collaboration—one where CFOs, CHROs, CMOs, and beyond work together through intelligent systems that move with speed, transparency, and trust.
The “honeymoon” phase of AI is over, and the organizations that lead with smart execution will define the next era of enterprise transformation. The only question left is: who’s ready to lead?
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