This is Digital, Episode 39: How to make GenAI a Cultural Movement, Not a Mandate

Q&A

There are so many different AI tools out there, and employees are bringing them to work. We’ve been using the term "BYO AI," but this could potentially be problematic for companies. What are some of those problems and what can companies do? 

EJ: Many people bring AI tools to work for productivity. This can be an issue for two reasons: data privacy and security. Some tools use input data to improve their functionality, which means proprietary company data could be exposed. It’s crucial to understand data usage and governance with these tools.  Secondly, it’s hard to measure productivity impact across the organization if these tools are used secretly. Sharing AI tools and techniques can help scale productivity improvements across teams and departments, benefiting the entire company. 

Companies should be giving employees sandbox environments or secure places to test and learn as soon as possible. There are custom solutions that companies like ours can deploy and SaaS providers with data and privacy compliant solutions. Employees need training on these tools to experiment and identify new areas of impact or opportunities to create value. This can only happen if they have the time, training, and opportunity to explore these solutions in a safe, secure place. 

Some employees see AI as a threat or replacement rather than a partner. How can they shift that mindset? 

Sam: Repeated use of the tool helps employees understand its capabilities and limitations. This bottom-up approach highlights what work can't be replaced by AI. From a top-down perspective, companies should demonstrate use cases where AI augments, rather than replaces, work to prove its value. 

Let's discuss the ROI of generative AI at work, particularly in terms of employee engagement and satisfaction. Are we measuring these aspects? How important is this measurement? 

EJ: Before talking about ROI, I want to note that adopting AI tools is a culture change. Adoption can't be mandated; it must be inspired. You can’t just tell employees to use the tool; you have to show how it can help them in their jobs and work together to unlock productivity. Many organizations claim specific productivity increases with AI, but without cultural adoption, these claims fall short once the consultants leave. 

As for measuring ROI, we’ve instrumented Nigel, West Monroe’s proprietary AI model, with basic product telemetry, tracking engagement, churn, adoption, and usage statistics. We also gather qualitative feedback on how AI improves day-to-day delivery and execution. Additionally, we've started an intake process for innovation ideas, where employees pitch how AI could help. If the idea fits, we have an accelerator process to test, prove, and quantify its value. Finally, we align all these initiatives with our company goals and strategies. 

The value in AI is inspiring people to achieve the end result. If you only communicate the end result, however, you can lose people. Can you share thoughts on how companies should approach AI with their employees and how do we engage with our clients around it?  

Sam: For companies, it’s essential to understand where they and their employees are in the AI journey. The initial approach needs genuine excitement and interest in the human element of using these tools.  

We work with companies who are either just starting their AI journey, so we begin it with them and get them excited about AI, or some who are further past that, so we can go in with a more pointed approach toward the goal. In the latter, our clients are already enthusiastic and have clear use cases in mind. It’s essential to not lose sight of the excitement and the people engagement aspect and meet clients where they are in their AI journey.  

We recently developed a thought leadership piece, Your workforce is using GenAI wrong—here's how to fix it, which dives into organizational change management around AI, which is crucial in this process. Can you share some insights from the piece, are companies relying on traditional change management methods, or are new approaches emerging for generative AI? 

EJ: Yeah, I see it like this—while there are some tried and true principles, working with generative AI is somewhat new. Unlike implementing ERP or CRM systems with exhaustive training decks, there's no established blueprint here. You still need to understand the technology and govern data use, as Sam mentioned. But the focus shifts to empowering people to innovate based on their understanding of the tool. We're promoting this cultural shift at West Monroe, like our upcoming innovation contest with substantial rewards. It's a different mindset from traditional tech implementations, blending governance with new ways of thinking and working.