Creating a People-Centered AI Strategy
Imagine two burly men in overalls come to your office and install a substantial, amorphous machine in the middle of the conference room. They plug it in, flip a switch and leave with no explanation. Over the next few weeks, people from your team wander in and out of the room, as if looking for leftover meeting snacks. And every single person walks out with a different perspective on what they saw.
Some see a gadget, some see magic, some see nothing at all.
Some spend hours poking and prodding.
Others just walk by, only slowing down to eye it suspiciously as they pass.
It isn’t so different from AI, is it?
As an executive coach and expert in leadership development, I am in daily conversations with executives across a range of industries. Each of my clients views AI differently. Some are enthusiastic early adopters; others are skeptical. The majority, however, simply do not know where to begin and lack the bandwidth to do more than dabble.
While I am intrigued by the technology, my focus is on the people using it. My work has always taken me inside the minds of leaders. The questions I explore with clients are especially salient as we consider AI adoption:
How does our perspective and emotions influence what we pay attention to?
What are the universal reactions to fear and uncertainty?
How can leaders take emotions into account, even when unspoken, when introducing change?
Organizations have an unprecedented opportunity to differentiate themselves if they view AI as a leadership competency. This white paper will examine how we got here, the challenges of integrating AI into the workplace, and what smart organizations should do to capitalize on this sea change.
How Did We Get Here?
Let’s rewind to March 2020, when our way of working was fundamentally upended. Organizations that never intended to become a distributed workforce were suddenly forced to adopt new technologies. Zoom, Teams, Slack, and an array of technologies went from useful to necessary. For all the benefits of remote and hybrid working arrangements, it has also created greater complexity. The good news is that most leaders have done a remarkable job of adapting to these technologies and the new interpersonal norms they require.
The less-good news is that no sooner had leaders adjusted to this new normal when AI came onto the scene. The rapid advancement of AI technology promises greater productivity, higher quality, and the ability to spend more time on what is truly important.
However, organizations and their leaders have limited bandwidth to know what steps to take, which tools to use, how to construct use cases, and how to implement a widescale rollout. According to a Microsoft study, 45% of US executives are not currently investing in AI tools or products for employees【1】.
Obstacles
Time and attention are key challenges in getting leaders to focus on AI. Additionally, there are other forms of hesitation, frustration, and resistance:
- “I don’t know where to start.”
- “I can’t add another thing to my plate right now.”
- “I’ve tried it, and it doesn’t really work.”
- “I could do it faster on my own.”
- “It feels like cutting corners.”
- “How can I trust the validity of it?”
- “My job is so specialized. Maybe it works for others but not for me.”
- “I’m embarrassed that I don’t know more.”
- “It feels like one more thing to figure out. I just can’t.”
In larger organizations, we often see pockets of users experimenting with or adopting tools for their jobs. Many of these early adopters do so under the radar, believing they need to seek approval or risk going unnoticed.
Steps to Creating a People-Centered AI Strategy
1. Create an Approach that Respects Psychological Strain and Employee Well-being
AI adoption must address the psychological strain within organizations. For instance, rapid technological changes can significantly impact employees' mental health. A recent study by Gartner indicates that 62% of employees report high levels of stress related to digital transformation efforts【2】. AI can evoke fear and uncertainty, with headlines constantly highlighting the threat of job displacement. This fear taps into core survival instincts, making AI seem more daunting than exciting.
2. Approach AI Adoption as a Cultural Change, Not Just a Technology Initiative
Most clients we work with lack a real implementation strategy. They experiment with different AI tools in isolated pockets of the organization. Clients who see the power of AI are those who create cohesive plans for piloting, messaging, and sponsorship. According to McKinsey, companies that successfully integrate AI into their operations are 2.3 times more likely to experience revenue growth【3】.
3. Find Your Why
Understand the benefit to the organization or team. For example, leveraging AI can free up time for more impactful and interesting projects. Productivity has been stretched to its limits, and many feel ineffective despite endless hacks, tips, and tricks. AI offers an alternative with the potential to exponentially increase accomplishments.
4. Decide Where to Start: Top-Down or Bottom-Up
Recognize that your thoughts, feelings, and biases impact team adoption and success. Your role in implementation depends on your level within the organization. A study by Deloitte found that top-down approaches in AI implementation resulted in higher adoption rates compared to bottom-up approaches【4】.
5. Commit to Habit Formation
Making AI a priority is crucial. It's not just about using AI but committing to growth and moving from fear to understanding its benefits. Fear sparks a fixed mindset, hindering learning and change. Shifting to curiosity can drive meaningful change.
6. Adopt a Growth Mindset
Embrace not-knowing. Organizations often do not allow room for uncertainty, leading to guarded and less honest conversations. A growth mindset fosters an environment where learning and adaptation are encouraged. Carol Dweck's research on growth mindset emphasizes that embracing challenges and persisting through setbacks leads to greater achievement【5】.
7. Find a Trusted Partner
It can be appealing to try to develop an AI strategy in-house. The beauty of the tools is their simplicity and usability. However, we have seen that this usually results in a decentralized and piecemeal approach that is measurably less effective. Investing in thought partners can help navigate the complexities of AI adoption. It is worth the investment to partner with an experienced organization that understands both the technology and the mindsets of people who will be using it.
Conclusion
Viewing AI as a leadership competency positions organizations to harness its full potential while addressing the human elements essential for successful integration. By focusing on people-centered strategies, leaders can create environments where AI drives innovation and growth without compromising employee well-being.
Looking to build a people-centered AI strategy? Get in touch to discuss a tailored approach that fits your team’s unique needs.
About the Authors
Starla Sireno
Starla is recognized as one of the top executive coaches in the field. She has partnered with countless client leaders in over 60 organizations globally, from Fortune 500 to fast growth technology companies.
LinkedIn Profile
Website
Tony Jones
Tony leverages his extensive experience as a Creative Director to teach AI-enhanced creativity as Co-Founder of Creative AI Academy and at Pratt Institute. His patented AI products and award-winning career in top agencies like McCann underscores his expertise in integrating AI into creative workflows.
References
1. Microsoft Work Trend Index: [AI at Work: The New Normal](https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part/#section3)
2. Gartner: [Digital Transformation and Employee Stress](https://www.gartner.com/en/newsroom/press-releases/2023-03-08-gartner-survey-shows-increased-stress-levels-among-employees-due-to-digital-transformation)
3. McKinsey: [The State of AI in 2023](https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/global-survey-the-state-of-ai-in-2023)
4. Deloitte: [AI Implementation Success](https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/ai-implementation-best-practices.html)
5. Carol Dweck: [Growth Mindset Research](https://www.mindsetworks.com/science/)
