The PC. The internet. The cloud.
Whenever new technology transforms the way we work, people—and organizations—follow what’s commonly called the technology adoption curve. First you have the innovators, adventurous folks who are the very first to try new technology. Next are the early adopters, opinion leaders who embrace new tech quickly and help spread the word. The early majority adopts it before the average person, while the late majority is more skeptical, waiting until there’s broad acceptance of an innovation. Finally, the laggards actively resist change and risk falling so far behind that they’re never really able to catch up.
Technology Adoption Curve
Like other disruptive technologies before it, AI is following this classic pattern of adoption.
While the lightning speed and staggering implications of AI feel unprecedented, the adoption curve for AI follows a similar pattern to those of previous disruptive technologies. So it follows that organizations that have successfully undergone technology transformations in the past can do it again with AI.
Of course, every leader wants to move fast and see real gains with AI today. Working with customers and navigating Microsoft’s own AI adoption journey, I’ve observed three key areas of opportunity for accelerating the AI adoption curve: access, ROI, and governance.
Access: Let’s face it: AI is a significant investment—and budgets simply haven’t been able to keep pace. As a result, leaders are forced to prioritize certain roles or functions over others, giving AI tools to only a select few. While this approach makes sense given the very real financial constraints, it’s ultimately not setting an organization up for long-term success.
Let’s consider an analogy: Imagine you gave email to only 10% of your employees, leaving 90% to rely on communicating in the old-fashioned way, by word of mouth and hard-copy memos. Communication would quickly become siloed because email users would primarily communicate with one another. It wouldn’t take long for employees to simply abandon email in favor of old ways of working that, while a lot slower and inefficient, were ultimately more familiar and accessible.
The same is true of AI. Broad access is one of the most powerful forces behind helping individual employees and entire functions begin to develop the new competencies required for this age of AI.
ROI: With the cost of AI bumping up against limited budgets, there’s a lot of pressure to prove the ROI of AI. And the most significant ROI is only going to come from wholesale change—redesigning entire business processes with AI at the center.
By redesigning business processes for AI—whether it’s invoice management or sales intake—leaders will be able to measure how AI is contributing to the bottom line through reduced costs and increased revenue.
It’s worth noting that access and ROI are intrinsically linked—a bit of a chicken-or-egg problem. Only by offering broad access to AI can organizations truly transform business processes and see compelling ROI. However, organizations need compelling ROI to both justify and pay for broad access.
Let’s reframe the email analogy: Imagine attempting to redesign your sales pipeline, but only 10% of your sellers have access to the AI tools needed to implement this new workflow. The other 90% rely on old tools and old processes. It just won’t work.
So what’s a leader to do?
I’m seeing a lot of organizations tackle this problem by starting with the low-hanging fruit. They target a clearly defined business process that has real opportunity to gain efficiencies and save money—like identifying and prioritizing leads in their sales pipeline. They closely monitor and measure those results so they can clearly see the financial impact, like how many new deals they were able to secure thanks to AI. They use those results to make the business case for the next process transformation and literally put their dollars generated (or saved) back into funding broader AI access.
The Access-ROI Cycle
Providing AI access to more employees increases ROI, which should unlock budgets to give more access, leading to more ROI.
Governance: Of course, you can’t talk about AI without addressing governance—delivering secure and compliant AI.
It goes back to access. If you don’t give your people company-approved AI, they take matters into their own hands: in 2024, 78% of AI users brought their own AI tools to work—many of which simply aren’t designed for an enterprise setting. This “BYOAI” approach can inadvertently put company data at risk. That kind of fragmentation hampers a unified approach to AI adoption: siloed practices develop, slowing efforts to grow an organization-wide AI culture.
It also leads to what can only be described as an IT pro’s worst nightmare: managing a growing system of disparate, bespoke AI tools. And fragmentation becomes even more of an issue as organizations try to manage constellations of AI agents working behind the scenes to power business processes.
A way forward
At Microsoft, we recently introduced Microsoft 365 Copilot Chat—a free and secure AI chat experience with access to pay-as-you-go agents. By pairing it with M365 Copilot, our best-in-class AI assistant for work, leaders can more easily navigate the AI adoption curve and accelerate their organization’s transformation.
Regardless of what AI tools your organization uses, the ideal state is a unified solution that can meet the varying needs of employees and functions across the organization, offer a consistent user experience, and provide standardized governance and controls. Scaling adoption across an organization is critical. And no one should be left behind.
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