AI insights series #1: Stop using AI to do the same work faster
Erica Fox, Director, Microsoft 365 Global Black Belt at Microsoft
Editor’s Note: This article is part of an exclusive series featuring insights from our latest ebook, AI in the workplace: An expert’s guide for CIOs. We sat down with AI leaders from Microsoft, Google, Logitech, and Unisys to move past the hype and get to the practical realities of AI at work.
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I see a lot of organizations rushing to buy AI tools just so they can say they “have AI.”
It’s an understandable impulse. The pressure to innovate is huge right now. But if you just hand these tools to your employees without a clear plan, you aren’t innovating – you’re just adding noise. Even worse, I see IT departments trying to make these adoption decisions in a silo. That approach just doesn’t work anymore.
For an organization to realize the full potential of artificial intelligence, you have to understand that this isn’t just a new tool. It’s a complete transformation of how your company functions.
Too many teams still see AI as a way to do the same old work, just faster. But the real value comes when we reimagine how work gets done – and who does it.
Here is how I believe leaders should be thinking about AI right now.
Turn down the volume
We’re all drowning in digital noise. Between chat apps, emails, and project boards, it is easy for the important stuff to get lost in the shuffle.
I believe AI’s best immediate use case is acting as an intelligent filter and prioritization engine. Instead of asking your employees to sift through hundreds of notifications across different platforms, AI can surface what is most relevant based on context, urgency, and role.
This goes beyond simple summaries. It’s about intent detection.
Imagine an AI that doesn’t just read your messages – it flags the ones that contain actual decisions, deadlines, or requests, while filtering out the casual chatter. It can unify messages from email, chat, and project tools into a single, coherent view organized by relevance, not just time. That is how you give your team their focus back.
Move from automation to amplification
The mindset shift that hasn’t fully happened yet—but urgently needs to—is moving from task automation to capability amplification.
“Doing more with less” is a tired goal. The new goal should be “doing what only humans can do.”
We need to let AI handle the routine and the repetitive so people can focus on judgment, creativity, and relationships. This means treating AI less like a tool and more like a team member—one that you collaborate with, iterate with, and check work against.
This requires a shift from standard “change management” to true capability building. Thriving with AI isn’t just about adoption; it’s about building new muscles in your workforce, specifically around prompt engineering, data literacy, and critical thinking.
Address the fear in the room
You can’t effectively roll out AI if you ignore the elephant in the room: employee anxiety.
The questions I hear most often are, “Will AI take my job?” and “Is this technology watching me?” If you don’t address these fears, they will kill your adoption rates.
Transparency is key here. Be clear about what the AI is doing, what data it uses, and how it supports – not replaces – people. Normalize AI as a co-pilot. When an employee understands what AI can and can’t do within the context of their specific role, they tend to be more willing to adopt the technology.
Don’t skip the data hygiene
I cannot stress this enough: In order to get high-quality results from AI, your environment needs to be ready.
When you deploy AI in an environment with outdated, overshared, or mismanaged content, the experience suffers. If you have a policy document from 2010 hidden in a SharePoint folder, a generative AI tool will find it and present it as fact.
You don’t need to rebuild your entire data estate overnight, but you do need to prioritize data hygiene. Clean up access, establish your data loss prevention (DLP) protocols, and make sure your house is in order before you invite AI in.
Focus on business value, not just “time saved”
Finally, we need to stop measuring AI success solely by “hours saved.”
To truly realize ROI, you need a business-first approach. Align your AI investments with core business KPIs like revenue growth, cost reduction, and employee retention.
AI can do more than just automate a report. Here are unexpected tasks it can excel in:
- Sales forecasting: Analyze CRM data to recommend which deals to prioritize.
- Employee wellbeing: Detect subtle signals in communication patterns to help managers spot employee burnout before it leads to attrition.
- Operations: Flag manufacturing anomalies in real-time to prevent issues before they escalate.
Don’t implement tech for tech’s sake. Focus on scenarios based on your real business processes.
The road ahead: Your new teammate
In five years, I believe employees will expect AI to be more than a tool. They’ll expect it to be a trusted teammate.
We will likely see personalized AI agents that know your work style and handle the mundane, freeing you up for strategy. We might also see AI acting as a real-time coach, suggesting learning opportunities or giving feedback on communication tone. And AI can help bridge silos by automatically pulling in the right people, content, and context across teams, tools, and time zones.
But to get there, we have to start building the foundation today.
Want more AI insights?
I’m just one of the AI experts Appspace interviewed for their latest guide.
If you want to read the full range of AI strategies – including deeper dives into governance, culture change, and the future of the digital workplace – you can download the complete guide below. It features additional insights from AI leaders at Google, Logitech, Unisys, and Appspace.
