Cisco's AI readiness report finds AI usage outpaces AI value. Achieving more consistent results with AI starts with leadership and requires strategic investments in infrastructure, governance and skills.
Nearly all the companies I talk to today are using AI, but most fall short when it comes to getting real value from AI, aside from a select few. Cisco calls these AI standouts the “pacesetters.”
The third iteration of Cisco’s AI Readiness Index surveyed more than 8,000 senior leaders across 30 markets and 26 industries to assess how far companies have come with adopting AI. This year, the study highlights how pacesetters (only 13% of all companies) consistently get measurable returns from AI. What do they have that others are missing? Effective leadership, upgraded infrastructure, and in-house AI skills, to name a few distinctions.
AI success requires leadership support
Pacesetters aren’t necessarily the biggest spenders or the ones with the most impressive pilots. What sets them apart is discipline. They treat AI as part of their operations not as a side project, with 95% tracking the impact of their AI investments. They’ve built data pipelines that can support large models and networks designed for growth. Because of that, 90% report higher profitability, productivity, and innovation, compared with about 60% overall.
Given how early we are in the AI cycle, this is what I would have expected to see. When one looks back at other technology shifts, such as cloud, mobile and the rise of the Internet, there were a handful of companies that were willing to be aggressive and try things, and they realized much of the value. This is why every technology transformation tends to reset market leadership in almost every industry.
Nearly all (99%) pacesetters have a well-defined AI strategy versus 58% of other companies. The majority of pacesetters embrace change and have formal programs to help employees adopt AI, which is less true for non-pacesetters. Their funding decisions reflect that focus. Seventy-nine percent of pacesetters make AI their top investment, and 96% have both short- and long-term budgets. When it comes to other companies, AI is a top investment priority for only 24%.
From my conversations with IT and business leaders, it’s not hard to see what creates the gap between companies getting measurable value from AI and not —it’s leadership. AI systems are widely available today, so this is no longer a technology issue but rather one of leadership. AI programs need to be driven from the top down in a thoughtful and programmatic way. This lets companies better measure the impact of AI on the organization.
Organizations must ensure the infrastructure is AI ready
Infrastructure is another area where Cisco found a major difference. Pacesetters are designing their networks for future demands. Seventy-one percent say their networks can scale instantly for new AI projects. Roughly three-quarters of pacesetters are investing in new data center capacity over the next year. Currently, about two-thirds say their infrastructure can accommodate AI workloads.
Most pacesetters (93%) also have data systems that are fully prepared for AI, compared with 34% of other companies. About 76% have fully centralized their in-house data, while only 19% of other companies have done the same. Eighty-four percent report strong governance readiness, while 95% have mature processes to measure the impact of AI.
If ever there was a technological shift that requires the right infrastructure, it’s AI. AI generates a significant amount of data, needs large amounts of processes and low latency, high-capacity networks. Historically, businesses could operate with networks that operated on the premise of “best effort,” but that’s no longer the case. From the data center to campus to branch offices, in most companies, the network will require a refresh.
Scaling AI requires the right processes
When it comes to being disciplined, 62% of pacesetters have an established process for generating, piloting, and scaling AI use cases. Only 13% of other organizations (non-pacesetters) have reached this level of maturity. Most pacesetters say their AI models achieve at least 75% accuracy. Almost half also expect a 50% to 100% return on investment (ROI) within a year, far above the average. Cisco notes that over the past six months, pressure has been building for companies to show tangible ROI. Executives and IT leaders are pushing for results, and so are competitors.
By contrast, most other companies are in early stages of readiness. Although 83% plan to deploy AI agents within a year, many admit their networks aren’t ready for that kind of scale or complexity. Cisco attributes this to “infrastructure debt.” It’s the AI-era version of technical debt where companies take shortcuts by not upgrading their infrastructure, skipping security reviews, or not hiring skilled professionals.
These shortcuts might seem minor at first but can snowball into much bigger problems that prevent companies from realizing AI’s full value. Cisco lists early warning signs like high compute costs, unpredictable hybrid-infrastructure expenses, and resource strain. Many companies also struggle with security and centralizing their data. While pacesetters face these issues as well, they’re better equipped to handle them.
AI readiness will lead to a business-level competitive advantage
The study findings clearly point to the fact that AI readiness is a key differentiator. Many talk about value, but it’s the pacesetters that are truly capturing it. As discussed above, pacesetters consistently invest across six pillars of AI readiness. One, they have a clear AI strategy in place. Two, they’re investing in new data center capacity. Three, they have clean, centralized data integrated for AI agents. Four, their governance includes guardrails and live monitoring. Five, they have skilled in-house AI talent. Six, they have a company-wide plan to guide employees through the transition to using AI.
Cisco believes that by following the pacesetters’ lead (the six pillars), other companies can achieve more consistent results with AI.
Final thoughts
As the report highlights, there is no lack of interest in AI. However, production deployments lag as many organizations are trying to figure out the best strategy. My advice is to think “chip shots” and not “moon shots,” meaning start small, learn from there and expand. Attempting a companywide AI rollout that solves all company problems will only overwhelm everyone and lead to failure.
The most important thing is to get the ball rolling and then adjust as one needs to. At an event earlier this year, one of the speakers stated that with AI there won’t be the ability to be a fast follower. AI will move fast, and those companies that the Cisco report has identified as pacesetters are poised to leapfrog the competition. Use the report as a wakeup call, get started with AI, and close that pacesetter gap.
