AI Adoption Strategy: Why Business Leaders Need to Rethink How They Work

AI adoption is accelerating, but implementing new technology doesn't automatically translate into better business outcomes.

For organizations investing in artificial intelligence, the bigger challenge is determining where AI creates real value, how to manage its risks, and how to prepare employees for a fundamentally different way of working.

In our Voices of Leadership series, we spoke with Brian Kuhn, Chief Technology Officer at Steeves and Associates, about what successful AI adoption really requires. Joining the conversation was Alexandra Fajgenbaum, our Head of Operations, who explored how businesses can balance innovation, governance, and long-term strategy as AI continues to evolve.

With more than 40 years of technology leadership experience, Kuhn has seen how organizations succeed and struggle through major technological shifts.

His message for today's business leaders is simple:

"We need to be able to map technology tools to a business benefit."

As AI capabilities continue to evolve, that distinction has never been more important.

1. A Successful AI Adoption Strategy Starts with Business Outcomes

The pressure to adopt AI is growing. But how can organizations distinguish meaningful innovation from the latest technology trend?

For Kuhn, the answer lies in measurable business value.

"The signal for me is it saves time, increases more revenue, and enables people to do what they couldn't do before."

Rather than beginning with a particular AI tool, organizations should identify the workflows, operational challenges, and opportunities that could benefit most from improvement.

Kuhn shared an example of working with a large packaging manufacturer to develop its AI roadmap. Instead of focusing exclusively on IT, his team interviewed senior executives and representatives from nine departments to understand where AI could make the greatest difference.

This business-first approach helps organizations prioritize AI investments based on actual needs rather than technological excitement.

The takeaway? AI adoption should solve business problems, not create new technology projects without a clear purpose.

2. Why AI Strategy Needs to Be More Flexible

Traditional technology roadmaps often span several years. But when AI capabilities evolve in months rather than years, rigid strategies can quickly become outdated.

During our conversation, we explored a challenge many business leaders face: how can organizations plan for the long term without missing emerging opportunities?

Kuhn's response:

"I think we need to hold strategy loosely."

His approach combines long-term vision with shorter planning cycles that allow organizations to continuously reassess priorities.

An initiative that seemed essential six months ago may no longer represent the greatest opportunity.

For business leaders, this means treating AI strategy as an evolving process rather than a fixed implementation plan.

Organizations need to remain focused on their objectives while being willing to rethink how they achieve them.

3. From AI Tools to AI-First Thinking

What separates organizations experimenting with AI from those experiencing meaningful transformation?

According to Kuhn, it's a shift in mindset.

He describes successful AI adoption as developing AI-first thinkers: employees who consider how AI can support their work before automatically relying on traditional processes.

"You look to AI for everything you do. You see where it can help you and then where you need to step in."

This approach changes how employees spend their time.

Instead of manually completing every step of a task, they can increasingly use AI to organize information, automate repetitive activities, and support decision-making.

As Kuhn puts it:

"They basically shift to orchestrating the work as opposed to just doing the work."

But this transformation requires more than access to Microsoft Copilot or other AI tools.

Organizations must invest in AI change management, practical training, and ongoing support to help employees build confidence and develop new working habits.

Kuhn emphasizes that training alone is insufficient. Employees also need opportunities to practice and reinforce what they've learned.

Successful AI adoption isn't measured by how many employees have access to AI. It's measured by how effectively AI improves the way they work.

4. AI Governance: Balancing Innovation with Security

As organizations expand AI adoption, governance becomes essential to protecting sensitive information and maintaining accountability.

But establishing an AI policy is not enough.

As Kuhn emphasized during our discussion, policies must become part of everyday operations, with clear expectations around data access, acceptable use, and human oversight.

Organizations need to ask important questions before scaling AI:

• What sensitive or proprietary information could AI systems access?
• Are existing permissions exposing more data than necessary?
• Who is responsible for reviewing AI-generated outputs?
• How can employees experiment safely without introducing unnecessary risk?

Kuhn recommends a risk-based approach: allowing greater experimentation in lower-risk environments while applying stronger controls to sensitive processes.

We also discussed the importance of auditing existing environments and addressing unnecessary data exposure before expanding AI access.

For organizations using Microsoft 365, solutions such as ProvisionPoint Premium can support governance through controlled workspace provisioning and lifecycle management.

The objective isn't to slow innovation.

It's to establish the safeguards that allow organizations to adopt AI with greater confidence.

5. Agentic AI Could Redefine Business Automation

One of the most compelling moments of our conversation came when Kuhn discussed the emerging potential of agentic AI.

Unlike traditional AI assistants that primarily respond to individual requests, agentic AI systems can coordinate multiple actions and workflows with varying degrees of autonomy.

Kuhn described a client demonstration involving a digital-twin system that could use organizational knowledge and coordinate groups of AI agents, or as he called them “swarms” to perform complex tasks.

In one example he shared, a software engineering team reportedly cleared months of backlog in days using the system.

Although this was an individual client experience rather than an independently verified industry benchmark, it illustrates the possibilities that increasingly capable AI agents could unlock.

For business leaders, the implications extend beyond productivity.

Agentic AI could fundamentally change how organizations manage processes, allocate resources, and deliver services.

Instead of asking which individual tasks AI can automate, organizations may need to ask a much bigger question:

What would our workflows look like if we redesigned them around AI?

That shift could define the next stage of enterprise AI adoption.

6. Preparing for the Future Requires Continuous Learning

With technology advancing rapidly, organizations face a familiar challenge: balancing today's operational demands with tomorrow's opportunities.

Kuhn believes businesses must deliberately make room for research, experimentation, and strategic thinking.

"I like to allocate 20% of my time to learning and research."

He acknowledges that finding this time can be difficult, particularly for smaller organizations managing competing priorities.

But failing to invest in learning can leave businesses unprepared for emerging technological shifts.

Throughout our conversation, Kuhn emphasized the importance of encouraging employees to experiment with new ideas, reconsider existing business processes, and explore what might become possible.

His philosophy is rooted in advice he received early in his career: become a lifelong learner.

That mindset remains central to his approach to technology leadership today.

The Bottom Line: AI Adoption is About More than Technology

Our conversation with Brian Kuhn reinforced a critical lesson for organizations navigating AI transformation: adopting technology is only the beginning.

The real opportunity lies in connecting AI to business outcomes, building adaptable strategies, establishing effective governance, and empowering employees to work differently.

As AI and automation become increasingly capable, organizations will need to rethink not only which tools they use, but how work gets done.

And as Kuhn reminds us:

"How are you going to get better if you're not working on it?"

The businesses best positioned for the future won't simply be those adopting AI the fastest.

They'll be the ones turning AI adoption into meaningful, lasting business impact.