3 early lessons with generative AI

Generative AI products like ChatGPT have introduced a new era of competition to almost every industry. As business leaders seek to quickly adopt ChatGPT and other products like it, they are shuffling through dozens, if not hundreds, of use cases being proposed.

The bottom line: The companies that strike the right balance of risk and innovation when adopting generative AI will win. The question is, how do you find the right balance for your business? Here are the lessons we’ve learned so far from our approach.

1. Don’t wait to start experimenting with generative AI

The sooner a company starts developing a framework for adopting generative AI, the sooner the use cases can be rolled out and start showing ROI. Employees are excited about the potential implications this will have on productivity and efficiency.

However, issues can arise when this excitement leads to a scenario where employees in various departments are using generative AI tools with no coordination and little-to-no oversight. Not only is this risky—siloed employees may not be considering the risk and liability being introduced to the company—but also inefficient, since there are bound to be redundancies.

Our biggest initial to-do was getting a handle on how employees are using generative AI, determining what the “acceptable” and “too-risky” uses are, and finding a balance between efficient adoption and proper vetting.

We now have a taskforce that acts as the collection point for all of our uses of generative AI, making sure the lessons learned from them are being routed into one location. This has led to more informed decision-making as well as better knowledge of the tools being used by our team to know which ones are adding the most value.

2. Assign a multidisciplinary team to prioritize and communicate

Our research shows that high-performing, digitally enabled organizations are already moving away from hierarchical organizational structures. Instead, their structures are flexible and adaptable to enable collaboration. This encourages and enables individuals to work and learn across different job functions more easily, and we have really leaned into this as we create a framework that will be applied across the entire company.

Determining your path toward generative AI adoption does not just live with one department—whether that’s IT, risk & compliance, or innovation. Instead, develop a multidisciplinary team that gives every department a seat at the table to ensure that potential use cases are viewed from all angles.

Does financial automation require insight from the IT department? How do changes in marketing processes impact business development? By developing a 360° view of the pros and cons for each possible use, you are set up to make smart decisions in a timely manner.

3. Treat generative AI with a product mindset

Similar to many product development initiatives, our approach to implementing generative AI starts with use cases and proof of concepts. Our teams have been asked to identify what their most essential uses for this technology would be, including how it increases efficiency, how it impacts customer experience and where the potential pitfalls are. Then, our taskforce chooses which use cases to greenlight on a trial basis.

Once a use case has been given tentative approval, we develop workstreams to properly oversee each implementation and collect useful data and qualitative feedback. This testing and learning is critical to make informed decisions regarding what to greenlight next.

In the long term, as these proofs of concept begin to show results, we can pivot these successes into standard operating procedures and apply the uses more efficiently on more projects.

Showcasing ROI can be difficult when the best-case scenario is “nothing bad happened.” We view it as a good sign if, as we start incorporating generative AI into day-to-day processes, we do so without compromising sensitive data or receiving pushback from key stakeholders.

But this is not as helpful long-term, so we also focus on the ROI that each successful use case provides: how has client satisfaction/engagement been impacted, where have efficiencies been realized, how have costs been reduced, etc.

Conclusion: Find your own balance between risk and agility

Generative AI is just the beginning—we’re in an era where opportunities will continue to emerge for companies to embrace new, cutting-edge technology in ways that will revolutionize their work. And the speed at which any company chooses to adopt the newest tools will depend on their appetite for risk versus their desire for agility. Being first to innovate and first to develop a more efficient way of doing business is great, but is it worth the reputational risk if something goes awry? To learn more, visit West Monroe

Artificial Intelligence, Machine Learning