August 2023 | Q&A Spotlight

From hype to action: A deep dive into generative AI

Our Generative AI expert Cory Chaplin discusses the trends we’re seeing, where clients need help most, and how orgs can maximize its potential

From hype to action: A deep dive into generative AI

West Monroe’s Cory Chaplin leads our Generative AI lab and recently chatted with Crain’s Business Chicago to discuss the capabilities of AI. His insights empower organizations to harness AI's boundless potential, paving the way for transformative innovation. The original Q&A can be read here

West Monroe has advised and built tech for companies for 20+ years. Why is the business world going crazy over generative AI? 

Generative AI has caught the attention of the business world due to its unique characteristics and potential. The underlying technology has been around for years but has recently come to the forefront with the release of more advanced tools. The generated content holds immense value as it is not simply regurgitated information but rather something entirely new. 

We firmly believe that generative AI lives up to the hype and represents a level of innovation and interest that rivals the early days of the internet. While it is something to be adopted and explored with enthusiasm, it is essential to approach it smartly and make progress wisely, ensuring a balanced perspective.

You work with companies across industries. How are they approaching generative AI right now? 

We're starting to see industries approaching generative AI in diverse ways, driven by their specific pain points and priorities. In manufacturing, the focus is on optimizing the workforce, leveraging generative AI to improve productivity and efficiency. Software companies are focused on using it to write and ship code faster. And in private equity, generative AI is being evaluated across the entire portfolio so they know where to invest time and money.

What is the biggest barrier for companies to get going? 

The most significant barrier for companies to embrace generative AI is often not knowing what the technology is really capable of. Many organizations tend to focus solely on the most obvious use cases and fail to explore the true possibilities of deeper integration. Consequently, they get stuck with basic output without producing tangible business results.

What are the top questions you're hearing? 

Clients frequently raise several key questions when it comes to generative AI. They seek guidance in determining which tools are safe and suitable for their employees to use. Clients also want to understand the best use cases for their specific industry and how to safeguard their data throughout the process. Questions about centralization versus democratization of generative AI within their organization arise as well. Overall, clients express a desire for comprehensive knowledge about the technology itself, including risks, opportunities, and practical implementation strategies. Most of our conversations have been at the C-Suite or board level at this point.

How are you helping clients in this space? 

West Monroe actively assists clients in navigating the world of generative AI. Our support extends beyond education, as we engage in numerous conversations with clients to provide a deep understanding of the technology, its potential, and associated risks. We work closely with clients to identify the most feasible applications of generative AI based on their pain points, strategic goals, and areas with the highest potential for success. 

But we don't just advise—we help companies build and measure, too. Before allocating resources, we help companies ensure their generative AI investments are directed toward projects that yield tangible and meaningful results. One example of recent work includes a "search" product that answers complex insurance benefit questions based on an employer's specific benefits. Additionally, we have evaluated how a Fortune 500 company can integrate generative AI into their B2B customer service operations to renegotiate outsourcing contracts and achieve cost savings.

What is your best advice for companies to accelerate generative AI adoption? 

Don't wait too long to get started. Generative AI offers some big benefits, and you don't want to fall behind your competitors. When you dive into generative AI, think like a product developer rather than getting caught up in the latest trend. Focus on creating value for your business instead of just following the crowd. Start by envisioning what you want to achieve with generative AI and set clear goals. And remember, don't try to force a one-size-fits-all approach. Customize the use of generative AI to fit your specific needs, taking into account factors like your data maturity, customer base, and previous investments. That way, you'll get the most out of generative AI and make it work for your company.

Should generative AI be a bottoms-up or top-down approach? 

I think the answer is both. Executives should be working on enterprise-level, strategic decisions about AI and where to apply it in their business, but employees should also be encouraged to incorporate it into their everyday work—with guardrails. Consider this: An MIT study found that business professionals who used generative AI in their work saw their productivity improve by nearly 60%.

What does "good" look like when you are leveraging generative AI? 

It's very important to think beyond, how do I get every employee a license to ChatGPT? Instead, think about how you can build a piece of technology (web app, mobile app, etc.) that will interact with a language model as part of a business process. That technology can govern the interaction with the language model in a way that makes sense to the power user of that process—think well-written prompts using sufficient background data asking for an output in the right format, which can make all the difference in the output.

What other opportunities does generative AI bring? 

We've seen the enthusiasm around generative AI spark some additional benefits. First, we've seen companies get more comfortable with sister technologies to generative AI—like machine learning—after they learn generative AI may not be the right fit to solve a business problem. And second, we've seen a greater sense of urgency to maturing data governance and data capabilities to prepare for more advanced analytics and democratization with the advent of generative AI. So we're getting a lot of interest in other data work that isn't AI-related because of the hype.

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