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.
TL;DR
- Most AI projects fail due to a lack of a proper operating model, not technology issues.
- Focus on specific business problems and P&L impact, not just experimenting with AI models.
- Unify data and workflows on a secure platform and govern AI like a business system.
- Redesign work for human-AI collaboration, with AI handling routine tasks and humans focusing on strategy.
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.
ServiceNow pinpointed crucial applications delivering significant benefits to both staff and clients, beginning with service desks. The company operates a completely automated IT service desk, where AI manages 90% of employee inquiries, especially regarding client assistance, AI facilitates self-service for 89% of requests, leading to 50% quicker resolution for intricate matters. We're applying this adaptable framework to areas like HR, finance, sales, and beyond. This isn't a trial or a demonstration; these are tangible results.
- Fix data chaos with platform power
AI implementation begins with a robust data strategy and concludes with user experience. Prior to integrating new models, focus on a platform that facilitates and utilizes AI rather than merely housing it. By unifying any model, agent, data source, and workflow on one secure AI platform, you can dismantle silos, convert insights into tangible outcomes, and foster widespread adoption and value throughout the organization.
Opting for a robust, integrated platform from the outset bypasses the substantial time and financial investment required for later additions. This approach prevents AI projects from becoming fragmented and frequently faltering during their pilot phases.
- Govern AI like a business system
AI model and tool governance shouldn't be a singular committee review; it needs to be an ongoing operational practice. A crucial element is setting up a central oversight body to manage all agents and models, covering everything from their deployment and access rights to monitoring and decommissioning.
Consider cybersecurity or financial sectors. Neither is scaled without proper oversight, and the same principle applies to AI.
- Redesign work for human + agent teams
The aim isn't to substitute people. It's to remove the digital obstacles that impede their progress.
Microsoft’s 2025 Work Trend Index indicates that employees face interruptions every couple of minutes from meetings, messages, or alerts. Close to half of all workers report their day feels disjointed and disorganized. This isn't a productivity deficit; it's a systemic breakdown.
It's crucial to rethink how work is accomplished using AI, rather than simply automating existing workflows. The goal should be to reshape and enhance human responsibilities, fostering a future where humans lead and AI supports them. In this model, AI manages routine and moderately complex assignments, freeing up humans to concentrate on crucial strategic objectives, pioneering new ideas, and nurturing connections—areas where their strengths truly shine.
- 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 extends past simple collaboration to shared ownership of substantial business evolution.
A 90-Day AI playbook
To translate strategy into action, you don't need a complete digital overhaul; instead, you need structure, velocity, and unambiguous accountability. This 90-day guide simplifies the challenging process of AI transformation into four concentrated sprints. Every stage is crafted to build momentum, demonstrate value promptly, and equip business leaders with the clarity necessary for confident scaling.
By implementing these steps, AI is integrated into production, forming the foundation of the autonomous enterprise. In this model, AI agents, data, and workflows function cohesively to foster resilience and expansion on a large 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: Implement essential AI processes. Track deflection rates, how long it takes to resolve issues, and how happy users are. Now's the moment to experiment, refine, and enhance.
- 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?”
- Staff dedicate more effort to producing results and less to switching between applications.
- Governance reviews are boringly predictable, because the system just works.
Why it matters now
Generative AI is projected by IDC to contribute as much as $22 trillion annually to the global economy by 2030. However, this economic benefit won't accrue to firms showcasing the most sophisticated demonstrations. Instead, it will favor organizations demonstrating the discipline for scaling, the governance for reliability, and the collaborative spirit for leadership.
When CIOs and COOs share ownership of the AI operating model, AI transitions from a mere headline to an ingrained practice. As AI advances, this collaboration will form the bedrock of a novel enterprise partnership, enabling CFOs, CHROs, CMOs, and others to unite via intelligent systems that operate with agility, clarity, and confidence.
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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