The world is getting smarter about Generative artificial intelligence. Some enterprises may be just starting with GenAI. Others are farther along the path. We see our clients moving into a new phase with GenAI: shifting from experimentation to real-world use cases and proof of concepts. This evolution shows that GenAI is durable and scalable if done correctly. Successful companies know the three Es of GenAI: experiences, efficiencies, and effectiveness.
Our clients say employees are more satisfied with the experience of using GenAI tools when those implementations are easy to use and provide good results. With an excellent user interface, employees can make the most of GenAI solutions and use them to their full potential.
Ensuring a good user experience requires trust. If business users across the organization do not trust the results of a GenAI model, they will abandon it. To build that trust, you must continually verify the model’s output. GenAI is very capable, but it is prone to hallucinations. Be willing to refine models regularly to keep them valuable and current. Next, the model must be transparent. You must know how it reaches answers rather than accepting results from a “black box” system. Transparency here helps with trust from your team and the trust that you’re following regulatory and legal guardrails.
We have also seen our clients enjoy greater efficiencies with GenAI. This was one of the biggest promises heard when GenAI went mainstream. Rather than taking away jobs, GenAI is taking over menial and repetitive tasks. This frees up employees for more value-driven work and allows them the freedom to be more creative in their positions. You can argue that GenAI is making humans more inventive.
GenAI is also boosting effectiveness on a larger scale, not just in small teams or business units. This is where the industrialization of GenAI models comes into play. When you have a solution that has proven its worth, you can then scale up and bring that process to more departments across the business.
Some companies want to try GenAI in too many projects at once. This will often waste valuable capital resources. Instead, companies must have careful discussions and develop a focused number of strategic use cases. The challenge is to make these selections considering expected benefit, time to benefit, cost, and risk. Another factor is choosing a use case with the best potential for scalability and, potentially, monetization. You must be wise with your selections. A trusted GenAI partner can help with this decision process.
Once strategic pilot projects show value, you can expand to further areas of your operation. This is not the result of starting small; it is starting smart. With that mindset, you can industrialize with AI data catalogs, strong query management, or a natural language coding assistant, to name a few profitable examples.
The three Es can also be used to check against your projects. If you are not seeing better experiences, effectiveness, or more efficiencies—then it is time to pivot. This, too, is where an experienced GenAI partner can save your initial and ongoing investments.
These three Es serve as a valuable checklist for any GenAI investment. Remember that experiences, efficiencies, and effectiveness have an impact beyond that initial use case. They serve as a launch pad for more GenAI implementations. Building trust, working smarter, and getting actionable results raises the value of your employees and your customers.
