10 guidelines for successfully scaling AI

There are plenty of individual AI success stories across the enterprise, but scaling AI continues to be a challenge.

Data from McKinsey’s “The state of AI in 2026: On the road to ROI” survey shows that four in five respondents report that AI has improved individual productivity. The survey also found that in organizations with annual revenue of at least $1 billion, 40% are scaling AI agents and 31% are scaling software coding agents. At smaller organizations, 22% are scaling AI agents and 17% are scaling software coding agents. Further, the proportion of AI high performers, those who report significant impact and attribute at least 5% of EBIT to using AI, remains flat at 6% year over year. Still, only 37% report AI making a positive contribution to their company’s EBIT.

In conversations with CIOs and IT executives, scaling AI is a high priority. Based on a synthesis of these various interactions, here are ten guidelines for successfully scaling AI across the enterprise.

1. Learning by experimenting with AI is a high priority

Encourage individual experimentation with AI and share outcomes. Start by exploring alternatives to restrictive control including hub-and-spoke participation, education and self-governance.

For some knowledge workers, experimentation will require changing their view of AI beyond being a powerful search engine.

Assessing progress during the experimental phase comes down to how much AI is used, actual time savings versus perceived savings, and the reported usefulness. It helps to establish baselines and measure whenever possible to avoid the common mistake of employees confusing AI activity with delivering value.

The shared learning is critical to getting employees comfortable using AI as well as applying lessons learned to build a successful AI foundation. With the proper care and feeding, AI moves from being an optional digital assistant to a vital digital colleague.

2. Choose pilots strategically

Select fewer, prioritized pilots in areas of the business that combine high business impact and lower risks. Ideally, these pilots will have access to sufficient data and include stable processes. Success also requires a sufficient workflow volume, integration feasibility and measurable results. The primary goal of such strategic pilots is to determine if and how AI can improve a specific business task.

The viability of the pilot should be evident by the quality of the tasks augmented by AI, the time and costs to develop it, and the ability to integrate the pilot with the overall system.

3. Assign business ownership

Successfully scaling AI across the enterprise requires business ownership, shared business/technology accountability and persistent product teams.

Pilots should be led by a business sponsor to make sure that, beyond a technology experiment and learning opportunity, it’s an application area that will positively impact the business.

The leader should also be accountable for the outcome and the impact of the pilots across lines of business including technology, data, security, legal, risk management, HR and operational partners. Value realization is the finish line, and business outcomes are distinct from technical outcomes.

4. Know when to redesign enterprise workflows

One important signal for enterprise workflow redesign is when the impact of AI on people, systems, controls and decisions reveals broken processes that need to be resolved. The redesigned workflows should be specific about where and when AI can make recommendations and resolve issues through human-AI collaboration, and when AI can make decisions on its own and take actions by automating specific tasks. In general, automation should be reserved for relatively simple, well-understood, low-risk tasks.

When human-AI collaboration is involved, the employee must have the required time, skills and authority to intervene as necessary. Human decisions or interventions should not be reduced to simply checking a box to approve.

5. Make governance part of the workflow

Responsibility and accountability are foundational governance principles.

Think of governance as a decision architecture that defines who is responsible, where the boundaries are and when human judgment adds value, based on a risk assessment. While the AI system can be permitted to recommend decisions and act within defined limits, organizational accountability must remain with people in their authorized roles.

Governance should be in place throughout the workflow process, not only at the end. The governance will include explicit rules for decision rights, permitted data, risk tiers, test criteria, monitoring, auditing, escalation, incident response and retirement of the newly built business products.

Employees should view governance as an enablement framework for safe growth.

6. Shift from tool sprawl into reusable foundations

Scaling workflows demands an architectural shift away from technology and tool sprawl into common platforms and reusable enterprise foundations. It requires identifying which shared services, patterns, data products and domain governance can be quickly redeployed while increasing the value of the portfolio. A lack of shared standards, platform visibility and an architectural playbook will limit the ability to scale.

To ensure that individual teams are not building their own custom stacks, it is critical to have a strategic view of each project, workflow and business product throughout the enterprise.

7. Build in flexibility for continuous improvement

As AI scales, CIOs need to determine how the organization will continue to govern the portfolio and continuously improve the system while supporting ongoing enterprise learning. One strategy is to use open architectures that can evolve as platforms change.

The foundation should be sound enough to scale yet offer flexibility when it is necessary to shift decision rights, standards, feedback loops and employee roles. Strive for the achievable balance between a centralized foundation that supports common platforms and standards to reduce fragmentation while allowing continuous learning and local innovation as AI evolves

8. Measure outcomes

To successfully scale AI, leaders need visible transformation and dashboards, pre-defined business metrics and continued measurements.

According to the McKinsey survey, 80% of respondents report that AI has improved their individual productivity, and 50% report that it helps them make better decisions.

However, achieving these types of results and better ones starts with establishing a pre-AI baseline. From there, include the following metrics for the measurement process: cycle time, throughput, quality, revenue, cost, risks, customer and employee experiences, and enterprise adoption.

Another measurement factor is anecdotal successes. It reflects the organization’s willingness to experiment and share learnings. However, anecdotes should be benchmarked against actual data, when possible, to identify areas that need more attention and resources.

9. Check for signs a pilot is ready to scale

Successful deployments start when AI is connected to roles, data, rules and systems, with strategic use of humans in the loop. Business impact, data readiness, safety, adoption, economics, repeatability and reuse were consistent themes of such readiness.

You will know a pilot is ready for bounded production when ownership is clear, the workflow is redesigned, the integrations run on a live system and success is measured with representative volume. Moving into deployment at scale is possible when the pilot is stable and operating at volume. It becomes reproducible when a second site can adapt the architecture and governance, follow a playbook and prove viability. Finally, pilots can then be expanded into new domains, but it requires different evidence.

10. Anticipate workforce changes

Transformative technologies succeed when innovation is combined with trust, collaboration, entrepreneurship and organizational change. AI adoption will change management practices, employee roles and even career paths. Just as architecture and governance are mapped to support scaling AI, workflow changes will influence roles and activities including executive education, succession planning, job descriptions, training and development, communications and incentives. The needed workforce changes will vary by task, industry and organizational maturity.

Choose the right path for your organization

Every CIO has their own path for scaling and deploying AI in their organization. As we learned during the 2026 MIT Sloan CIO Symposium, there is no one specific or perfect route, and several approaches may be used simultaneously. CIOs will need to look beyond the infrastructure. They will also need to redesign work, establish governance and build AI skills. It will be critical to share lessons learned and best practices to collectively accelerate productive, cost-effective, scalable AI in the enterprise.