This Q&A originally was featured in Crain’s Chicago Business, featuring AI insight and analysis from Erik Brown, a senior partner in our Product Experience & Engineering Lab; Eric Johnson, a director in our Product Experience & Engineering Lab; and Ryan Elmore, an Innovation Fellow and the leader of our Data Science Technology team.
Eric Johnson: There’s a split in how generative AI is seen. Regulated industries have generally worried about rules and risks, and sometimes think it’s not practical – like when generative AI can’t do simple math or compute simple logic. Generally, this skeptical group needs to know when it’s useful, when it’s not and how it works. On the flip side, we’ve seen older companies, some over a century old, that are really into this tech, way more than other emerging tech like blockchain. They like how easy it is to understand and use – a quick tutorial, and they’re ready to get started.
Ryan Elmore: This year, we’ve observed some common missteps in how businesses approach AI. One mistake is preventing any exploration of its potential. Also, ignoring the data hygiene aspects and just expecting AI to be able to overcome that obstacle rather than addressing it head-on. Another is trying to explore all possible use cases instead of focusing on a handful of the most promising ones that align with your company’s core strengths.
Some companies mistakenly believe AI can swiftly replace entire departments, like marketing or finance, which is far from reality. Treating AI as a separate, isolated initiative is also a misstep. It’s more effective to integrate AI into the business at a grassroots level and empower employees to leverage it in their daily tasks while also suggesting broader, more programmatic use cases. While Silicon Valley has made significant advancements, we believe the practical application of generative AI is just beginning to unfold. We anticipate that 2024 will be a pivotal year for more substantial use and implementation of AI in broader business.
Erik Brown: Investing in scalable cloud infrastructure is crucial. Cloud platforms offer the flexibility and scalability needed for AI algorithms. Additionally, consider investing in AI-specific hardware like GPUs or TPUs, which can significantly accelerate AI processing. It’s also wise to invest in data integration and management tools, as AI systems require high-quality, well-organized data to function effectively. In cybersecurity, AI can predict and mitigate threats more efficiently. For cloud computing, AI optimizes resource allocation and improves service personalization. Regarding data, AI enhances data analysis capabilities, leading to more informed decision-making. However, this integration also means increased complexity and potential security risks, requiring more robust security measures.
Ryan Elmore: We break down AI skill-building into two parts: Specialization and upskilling. Larger companies are already investing in data scientists and AI specialists. However, they shouldn’t overlook the need for data engineers to manage data pipelines and infrastructure. Equally important are business analysts who can translate AI insights into actionable business strategies and software developers who are open to—and capable of—using AI to aid their development processes. In our experience, middle market organizations have a harder time attracting and retaining specialized technology talent and instead can access highly sought-after talent pools via consulting partners — as both a strategic advisor and execution partner.
Upskilling your entire workforce on AI is another crucial piece of the puzzle. Not everyone is going to be an AI expert, but everyone in your organization can know how to use AI—whether that’s knowing the right prompts to use with a chatbot (like ChatGPT), incorporating new AI plugins and advancements into software they use every day, or simply being aware of your company’s ethics and governance around AI. Training your entire workforce in basic AI and data literacy leads to a more AI-centric culture and can propel your organization to be more tech-forward.
Eric Johnson: At West Monroe, we’re not just talking about AI; we’re actively integrating it into our work. In 2023, we launched Nigel, a secure AI-powered tool that’s part of our daily operations. It’s based on the same AI technology as ChatGPT and is a game-changer for our team of 2,000 that prioritizes security and privacy for clients. We use Nigel for a variety of tasks, from drafting emails to data analysis.
We also spun up an AI Lab this year, bringing together experts from different departments. This lab is a melting pot of ideas and expertise, helping us deeply embed AI into our culture and processes. It’s not just about improving our own productivity, it’s about being better equipped to advise our clients.
Erik Brown: The three of us talk about what we see in the market, with clients and at West Monroe—through the lens of addressing burning questions on AI. More specifically, we record the podcast on video so we can show real-life examples of how to code alongside AI or how to improve your prompts. We created the podcast for those who want to learn how to apply AI to their business practically today—and have something for both beginners and more advanced users.
Erik Brown: Business leaders should anticipate more advanced natural language processing capabilities, AI in edge computing and the growing importance of AI ethics and regulation. Staying informed and most importantly, adaptable, is key to leveraging these emerging trends.
AI applications like you’ve never seen them—In this video podcast miniseries, West Monroe leaders break down real-world examples and use cases to revolutionize your business.