Previous AI cycles generated a lot of hype and fewer results. Here’s how you can make sure that your Generative AI projects deliver on their promises.

It’s no secret that Generative AI and large language models (LLMs) are having a moment. ChatGPT brought these technologies to the masses and sparked the imagination of business leaders across verticals. At the same time, past waves of AI-enthusiasm led to disappointment and left other people feeling cynical. 

Perhaps something like pragmatic excitement is more appropriate. Generative AI and LLMs have already delivered substantial business value. In fact, they already power many of SoftServe’s technology solutions. Of course, we can’t yet know if Generative AI will be as earth-shattering as the steam engine. But we do know that it can produce incredible business results. It’s all a matter of knowing how to use it correctly. 

That’s why the key to unlocking the true potential of Generative AI and LLMs is to create a Generative AI playbook for your company. Read on — or listen to our podcast — to learn how you can achieve real results with Generative AI. 

Three steps to a sound Generative AI deployment

There are three key stages to implementing strong Generative AI applications:

Increased costs
Loss of productivity
Crushed employee morale

Identifying your use case

Generative AI is not a cure-all. The most fertile ground for its use is in business functions with large information bases. That’s because it’s extremely efficient in extracting, analyzing, combining, and producing information.


Improving through iteration

You will need to anticipate several iterations to perfect the algorithm. The best method entails getting user feedback and using shortened feedback loops to integrate suggestions quickly. Not only does that support adoption, but it also helps to develop guardrails to control the algorithm’s behavior and stave off biases that corrupt results.


Picking the right audience

Though the greatest returns from Generative AI will come from customer-facing applications, that is also where the risks of a malfunction are greatest. That is why you should start with internal implementation to work out all the kinks before introducing it to your customers.

Generative AI

How to use Generative AI right: an example from financial services

Large information bases abound in the financial services sector. That’s why a large financial corporation sought to deploy an LLM to automate the processing of internal risk policy queries. The information requests had grown so vast in quantity that the department could no longer keep up.

The initial plan was to build an LLM to assist the workers in the risk policy department. However, testing quickly showed that the LLM would work best if it was designed to assist the requestors themselves. Together, SoftServe did a proof-of-concept study and determined that this strategy could automatically answer 60% of the information requests.

After perfecting the Generative AI algorithm through user feedback and employing guardrails to ensure conformity with regulations and data privacy requirements, SoftServe’s client scaled the solution throughout its operations — eventually using it to satisfy most of its client-facing knowledge requests. By designing and carrying through a Generative AI playbook, the client was able to see real results from its initiative.

Real business value from Generative AI

The Generative AI revolution shouldn’t contain empty promises that leave you with sunk costs. All you need is a playbook before jumping on the Generative AI train. If you want to learn more about the right way to approach Generative AI, listen to SoftServe’s Generative AI expert Iurii Milovanov talk about the current state of LLMs on Chief Nation’s CEO.Digital podcast.

And if you want to get rolling on your Generative AI project, SoftServe can help. For many years, SoftServe has helped clients soundly deploy LLMs and Generative AI to automate processes and generate business value. Reach out to a SoftServe expert to start the conversation!