Data and AI

In the Wild West of AI, readiness begins with trust

Article Feb. 5, 2025 Read time: 1 min

By Kim Basile, Chief Information Officer at Kyndryl

Artificial intelligence (AI) is reshaping industries at a relentless pace, and organizations must do more than adopt new technologies; they must build trust to make them work. Trust is the linchpin of AI readiness. It’s about transparency, communication and empowering people to lean into change, not fear it.

When people hear the words “automation” and “efficiency,” some often assume their jobs are at stake. But the reality is that AI’s true potential isn’t in eliminating roles — it’s in elevating them. Our focus has always been to free up teams from repetitive, low-value tasks so they can focus on work that requires creativity and strategic thinking.

Many leaders expect AI to fundamentally change their business
in the near future, yet many still struggle with internal
trust and adoption.

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A full quarter of enterprise leaders say they struggle with integrating AI into their operations, according to the recently-published Kyndryl Readiness Report. This underscores the importance of clear messaging and consistent reinforcement. When you help people understand AI as a tool to make their work more meaningful, you see the shift happen. It turns uncertainty into curiosity. But you can’t just say it once and move on. The message must be reinforced in everything you do.

To that end, it’s crucial to internalize the fact that trust doesn’t happen by accident — it needs structure. That’s why we’ve developed a governance framework that puts thoughtful guardrails around every AI initiative. Proposed AI use cases go through a vetting process involving multiple departments. We want people to know that we’re not throwing untested algorithms into the mix and hoping for the best.

Another noteworthy finding from the Kyndryl Readiness Report is that while 90% of business leaders feel confident about their current IT infrastructure, only 39% believe their systems are ready to handle future challenges. This gap reinforces why our governance approach is essential — it provides a roadmap for using AI responsibly and building trust along the way.

Lessons from past transformations

Through past transformative initiatives, I’ve learned that successful innovation requires a balance between ambition and practicality. It’s not always about chasing the newest, flashiest tools but about refining existing systems to maximize value.

We’ve used AI to integrate systems so they “talk” to each other, rather than operating as disjointed parts. This keeps operations streamlined and people focused. In today’s rapidly evolving AI landscape, where there’s always a temptation to start fresh with the latest tool, the real win is making the most of your existing ecosystem.

We’ve also found that the best AI use cases don’t come from the top — they come from the people doing the work. That’s why we empower employees to self-nominate tasks they believe could benefit from automation. When they see their ideas come to life, it’s a powerful trust-builder. It tells them that we’re not imposing change, we’re collaborating on it.

Innovation happens when people feel like co-creators rather than bystanders. This approach has transformed how our teams engage with AI. Instead of asking, “What will this take away from me?” they ask, “How can this help me do my job better?”

25% of leaders report difficulty integrating AI technologies with existing systems and workflows.

Source: Kyndryl AI Readiness Report

The power of clear communication

You can’t build trust without communication. That doesn’t mean delivering polished corporate memos — it means being open about the good, the bad and the ambiguous. From the start of an AI initiative, we share what we’re doing, why we’re doing it and what we hope to achieve. When things don’t go as planned, we don’t hide it. We explain it.

It’s also important to connect the dots for people. Efficiency gains shouldn’t feel like an end in themselves — they’re a way to make space for more meaningful, high-impact work. When you show people how AI can lighten their load and make room for creative problem-solving, you turn skeptics into advocates.

No matter how advanced AI becomes, it’s people who drive progress. Technology is only as good as the people behind it. We’ve made it a priority to embed this belief into our processes — whether it’s through governance, feedback loops, or celebrating the wins that come from our people. One valuable takeaway is the importance of allowing employees to fail fast in this space. Transformation projects can be fast and furious, after all you’re often changing processes. There has to be room for experimentation, course correction and learning — and creating that permission can go a long way toward building trust.

Leadership in the AI era requires more than technical expertise — it calls for empathy and transparency. If we tell our teams that AI will empower them, we need to show them what that looks like in real terms. For example, if we free up 25% of someone’s time through automation, we help them use that time to grow, whether that means learning new skills or contributing to higher-level projects.

Building the future of AI readiness

AI isn’t just about having the latest tools. It’s about creating a culture where change feels exciting, not threatening. Trust, transparency and collaboration make that possible. We’re not using AI to replace roles — we’re using it to unlock new possibilities.

When people understand the “why” behind the technology, they’re more than just users — they become advocates. By building trust at every step, we’ve created an environment where our teams don’t just adapt to change — they help lead it.