In This Article:
How IT leaders can build an AI-ready organizational culture
The year was 1804. Richard Trevithick rolled out the first full-scale working engine locomotive, slashing delivery time for raw goods from 3 days to just 4 hours. But what truly defined Trevithick’s legacy wasn’t just the speed he achieved; it was his vision to make locomotives accessible for everyone, despite initial cultural pushback and slow buy-in.
Fast-forward two centuries, and CIOs are facing a similar moment with AI, and much like Trevithick, they need to get buy-in from everyone in the organization while weaving AI into the company’s DNA. CIOs can be the change makers and cultivate a culture where AI is a growth enabler rather than a routine operational tool.
We share insights from nine IT leaders driving this change to hear about what it takes to build an AI-ready organization in 2024 and beyond.
1. Define your AI vision and principles for lasting impact
The biggest impediment to AI adoption is not a lack of desire but a lack of vision.Without a clear vision and guiding principles, CIOs risk losing ground to competitors and end up putting out internal fires around technologies, siloes, and cross-functional barriers that don’t help anyone innovate.
Unfortunately, we’re seeing this play out now, with just 9% of organizations having an AI vision statement, while over a third don’t plan to develop one at all.
Mary Mesaglio, a distinguished Gartner VP analyst, suggests establishing a set of lighthouse principles that align with your organizational values before starting an AI project.
A technology decision is not just a technology decision anymore. It is a technological, economic, social, and ethical decision. To navigate decisions about AI in their organization, CIOs and IT leaders need lighthouse principles — a vision for AI that lights the way and says what kind of human-machine relationships they will and will not accept. Mary Mesaglio, VP Analyst, Gartner
CIOs should treat shared vision as a blueprint for defining their tech stack, focus areas, and success metrics, so cross-functional teams stay aligned on achieving business goals.
2. Focus on high-impact AI use cases
While the AI craze took over every team and organization in 2023–24, only about 15% have seen a meaningful impact on their bottom line. There are plenty of reasons for this lack of success, but one overarching issue stands out: companies, their resources, manpower, and even their C-suites are stretched too thin across a dozen AI use cases.
Charles Araujo, founder of DX Report, points out this challenge and encourages industry peers to think about the why before the how.
CIOs need to ruthlessly eliminate underperforming AI pilots and redirect their focus toward projects with real business value. For instance, in IT support, AI shines in bringing together scattered data sources and improving agent productivity by self-servicing repetitive tasks like password resets to improve service quality.
3. Create a strong data pipeline for your AI projects
77% of executives are worried about poor data quality, which in turn could hurt their AI efforts. High-performing, non-hallucinating AI isn’t possible without clean, accurate data, and that takes real effort. AI can only deliver value when it is fed accurate data from multiple sources.
As David Williamson, CIO of Abzena, rightly points out, organizations need to be data-ready before being AI-ready.
With a solid groundwork of vector databases and streamlined pre- and post-data pipelines, organizations can easily funnel data into their AI systems for optimal performance.
4. Beat AI anxiety by upskilling your IT talent
The arrival of AI has caused many IT workers to feel psychologically alienated, with 36% even fearing job loss. While it’s understandable that people worry about roles being automated, IT veteran and top ITSM consultant, Roy Atkinson, points out that this isn’t necessarily a bad thing and even offers an optimistic view.
For him, AI needs people as much as people need AI.
There’s a lot of focus on AI automating jobs, but that’s not always negative. It’s a great opportunity to upskill your staff in areas like prompt engineering, data governance, AI ethics, and knowledge management—skills that will help them thrive even as AI reshapes certain roles. Roy Atkinson, CEO, Clifton Butterfield LLC
Identifying the skills humans and AI are good at and complementing one another, helps IT leaders empower their teams with new roles and ambitions.
5. Adopt security by design as you embrace AI
As with any new technology, generative AI comes with different security risks. In the recent Slack AI incident, hackers managed to use prompt injections to snag sensitive employee information from private DMs.
In a saturated market, failing to prioritize security in your AI strategy can result in losing the trust of your employees, customers, and the market.
Ayesha Khan, who oversees both IT and security at Treasure Data, has a solution: design your AI with both security and privacy in mind.
She also stresses the need for open, tough conversations, personal accountability, and consistency to make this approach work. IT leaders should be thinking about security frameworks and guidelines for responsible AI practices before executing their AI vision.
6. Fuel AI growth with cross-functional collaboration
Building an AI-ready culture can’t happen in a vacuum. You need solid input from your engineering, DevSecOps, security, product, data, and design teams, to deliver a robust, secure, and user-friendly AI solution.
For Tony North, Senior manager of IT services at King County, cross-functional collaboration and constant customer feedback are the secret sauce to building successful AI products.
The future is about being proactive, not reactive, and one way to get there is by creating an IT culture that unites different teams, backgrounds, and perspectives to tackle shared challenges. With agile methodologies, we can quickly adapt to changing needs and deliver top-quality solutions. That’s how we foster innovation. Tony North, Senior Manager of IT Service at King County
This approach ensures that AI product teams are always in sync with the needs of everyone, from end users to executives and design and legal.
7. Reassess your IT tech stack and tools to support AI investments
In McKinsey's recent AI adoption survey, 25% of participants cited “lack of infrastructure to support AI” as their top challenge, even though they’re keen to get on board.
Traditional tools and platforms have a ‘bolt-on’ AI approach, offering only rudimentary benefits, while costing more in terms of dollars and hours spent by your already-burdened IT team.
Lenin Gali, Chief Business Officer at Atomicwork and previously a CIO at leading tech companies, sums up this dilemma perfectly: “Many companies still do a disjointed, multi-system coordination, cross-team activity in the most laborious ways. But the results have been mixed and mediocre at best. Calculating the ROI of such projects is still a daunting prospect.”
To dodge performance bottlenecks, tackle increased latency, and make scaling AI models easier for evolving business demands, Lenin suggests creating an AI-native infrastructure firsthand.
Ensure to reassess your tool stack and gauge what platforms need to be replaced or upgraded to support your AI initiatives.
8. Win over small teams before going to the top
Zuora’s senior IT director, Mark Gill, has some simple advice for CIOs looking to improve AI adoption and build an AI-ready culture: Gather a one to two-pizza team to co-start an AI pilot project with you.
This small, agile group can dive in, experiment, and get hands-on experience with building and using an AI solution before pitching it to management. These members are your internal AI champions. Those quick wins by smaller, but focused pilot teams can be just the thing to convince others to embrace the big picture—the true business potential of your project.
A positive attitude is contagious, and it's amazing what a team can achieve with the right mindset. Slowly but surely, involve everyone from the service desk to leadership to feel connected to the bigger picture—ask them for inputs to show how their work contributes to the bigger picture. Mark Gill, Senior Director of IT at Zuora
Think like a user instead of a builder, and you will see AI adoption shooting across sales and marketing, customer support, and data science.
9. Track the right metrics to highlight value derived from AI
BCG’s recent AI report shows that focusing on mission-specific use cases can bump revenue by 6%. But how do leaders measure that 6% ROI? And which use cases are churning out this growth? That’s where things get tricky.
Chad Ghosn, CIO and CTO of Ammex, breaks it down by using business-critical metrics like ticket deflection rates, ticket resolution time, and an uptick in IT productivity. When Ammex rolled out Atom, Atomicwork’s AI assistant, for service management, Ghosn relied on the 'hard numbers’ to show how AI delivers to their bottom line, revenue, and employee experience.
He focused on the uptick in ticket deflection rates from 20% to 65% within six months and scaling IT services without adding to headcount to prove to management the tangible value AI brings to the table.
Pioneer the AI transformation at your organization
CIOs play a pivotal role in leading the shift towards an AI-ready culture. A clear AI vision aligned with business goals, tight collaboration between IT and business teams, sound AI governance, and a strong bias towards investing in AI upskilling help foster a culture of continuous innovation and learning.
With a 360-degree view of the internal IT infra as well as the organization’s core business objectives, ultimately, CIOs can lead the change in embracing AI for long-term business success.
To help IT leaders get started, we’ve also put together a handy checklist that’ll enable you to assess how AI-ready your enterprise is.
