AI Standouts at Work - What High Performers Do Differently: Volume 8

AI adoption in the workplace is accelerating as more companies weave it into their daily business operations. Recently, Wharton and McKinsey released comprehensive studies on the current state of AI in the enterprise. The theme is consistent: AI deployment has moved out of the exploration and experimentation phase. We’re entering an era of acceleration and accountability. Here are a handful of stats that I found eye-opening from the studies:

• 82% use Gen AI at least weekly (+10pp YoY), and 46% (+17pp YoY) daily. (Wharton)
• 62% of survey respondents say their organizations are at least experimenting with AI agents. (McKinsey)
• 88% say their organizations are regularly using AI in at least one business function. (McKinsey)
• 23% of respondents report their organizations are already scaling an agentic AI system somewhere in their enterprises. (McKinsey)
• 72% of business leaders report tracking formal, structured Return on Investment (ROI) metrics for their Gen AI technology investments. (Wharton)

I highly recommend reading through each of these studies, but the trend is clear. Leaders across the country are successfully using AI in their everyday work, while building AI systems into their current architecture. Many of you work in the corporate world, and the larger the organization, the harder it can be to understand where your company is on its AI journey. It can be challenging to experiment, as you might not have access to the right tools. A lack of visibility into other departments might hinder your ability to collaborate to solve problems where AI could be part of the solution. I’m sure you’ve experienced at least one of these challenges and can list additional scenarios that slow down AI adoption within your organization. Luckily, there is time to act and positively impact the trajectory of your department, and hopefully your company. Pilot mode is still the norm across industries, despite increases in overall AI usage. According to the McKinsey study, nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise. The time to act is now, and I want to share 5 practical tips to help you be ready for the next phase of AI within your company.

1. Find ways to use AI daily

Seek any and all opportunities to build your AI skills in the workplace. This could be as simple as using Copilot to summarize emails, write or edit executive summaries, or build a talk track for an upcoming leadership readout. Start small, build a portfolio, and be creative. Your role might not have obvious AI use cases, and it will be up to you to find scenarios to test your skills and build confidence. 

2. Workflow first

AI adoption requires a mindset shift that prioritizes problem solving. Pilots will often fail when AI capabilities are forced onto an existing process. You can get ahead by building out detailed problem statements to rewire legacy workflows. This framework will be a foundation for any company that expects to see positive outcomes from AI initiatives. The McKinsey survey highlights this concept emphatically: ‘AI high performers—organizations seeing the greatest financial impact from AI—are nearly three times as likely as other organizations to report that they have fundamentally redesigned individual workflows in their deployment of AI (55% vs. 20%).’ 

3. Rethink ROI

Every company’s leadership will expect ROI from AI initiatives. That’s how the world works. And the ROI story will be challenging if decision makers and executives are not keeping up with the world of AI. It’s up to you, as an AI leader, to help bridge those gaps by evolving how you position ROI. Adopting a transformative mindset that pushes for growth and innovation, not just cost savings, will win in the long term. This simple reframe can help evolve how your organization approaches ROI and drive better alignment for AI infused projects. Here are two stats from the McKinsey study that hammer home this point:

• While cost efficiency is often the objective of AI efforts (reported by 82% of organizations), organizations achieving the greatest value (AI high performers) are more likely to also set growth (80%) and innovation (79%) as objectives.
• Organizations intending to use AI to bring about transformative change to their businesses are 3.6 times more likely to be AI high performers.

4. Knowledge share

Not everyone around you will have the same level of AI knowledge and skill in the workplace. And that’s okay. It will be important to help others and seek mentorship as AI’s influence at work continues to evolve. I’m working through this with my team at work by building mechanisms to share AI use cases and best practices. I make it a point to talk about AI during check-ins and gather ideas for the team. I’m not afraid to ask my peers for their opinions and thoughts on AI topics to broaden my viewpoints and understand how others are thinking about the future. There is no right or wrong way to share insights with others. A ‘rising tide lifts all boats’ mentality will ensure everyone is set up for success and can influence the future of work within their organization. 

5. Don’t forget about governance 

View AI as a powerful companion that requires human expertise, judgment, and validation, rather than a fully autonomous replacement. You cannot blindly trust that AI’s output is accurate and ready for mass consumption within your company. Prioritize auditing outputs and building a personal governance policy for AI use in your workflows. This will help build a framework that aligns (or will align) with broader governance strategies implemented at the corporate level. Prioritizing quality over speed is what actually accelerates AI usage. This is particularly important because inaccurate results remain one of the top three concerns leaders cite when using Gen AI (Wharton). 

Bringing it all together

AI is no longer a side project, it is becoming part of how work gets done. The good news is you do not need a new title or a massive budget to have an impact. Use AI daily, redesign workflows instead of bolting tools onto old processes, evolve how you talk about ROI, share what you learn, and keep quality and governance front and center. If you do that consistently, you will be ready for whatever phase of AI your company enters next, and you will have real examples to show the value you are creating along the way.