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How to Build and Enable an AI-Focused Engineering Team

    Recognizing the full potential of AI integration will help all industries become technology companies to avoid getting left behind.

How to Build and Enable an AI-Focused Engineering Team

With the rapid rise of disruptive technologies—most notably, artificial intelligence (AI)—every company needs to become a technology company. Consequently, this shift will cause engineering teams to undergo significant changes, not only in their composition but in how they perform their requisite tasks. To that end, companies should invest in the right tools to support AI-focused engineering teams, ultimately empowering overall business transformation.

The necessary ingredients to build the engineering team 

In the last decade, it was common for businesses to hire candidates primarily based on engineering talent alone. While exceptional Java or Python engineers are still helpful, there is an emerging preference for individuals with particular domain expertise. Companies want to deliver more client-centric solutions, which requires individuals with industry-specific knowledge and experience.

Businesses also need engineering teams that understand modern architectures and know how to build applications in the cloud. These teams must deliver on the constant increments of prototyping, hitting the market early and often. They should strive to save time by leveraging AI tools that can automate busy work, like writing code. Moreover, AI-focused engineering teams will need to be fast and agile. Traditionally, teams operated within two-week sprints; now, AI-enabled teams may only require one-week sprints.

Retraining and restructuring legacy teams 

Assembling an AI-focused team is one thing; teaching legacy teams new ways of working is another task entirely. Businesses will need to dedicate time to educating and building reward mechanisms. In this case, traditional organization chain management-type initiatives will help reskill existing teams. Nevertheless, it will take several months or longer to get senior engineering teams to a point where they can proficiently use new AI tooling regularly, and companies will need to plan accordingly.

Another consideration is the consequences of automation. Of course, AI-enabled automation makes engineers more effective and their lives easier, but it may also cause companies to reduce the size of their teams. Today, most engineering teams are small, consisting of a product owner, scrum master, business person and several developers and testers. With AI tools and bots, not as many people need to be writing code or testing. An ideal team may soon only require a product owner, an engineering lead or architect, one tester and one business person.

Automating tasks with prompt engineering

AI-focused engineering teams will utilize many tools, like Microsoft Copilot, which function like automated keyboards, helping engineers write code faster. The real power, however, lies in using LLMs for prompt engineering, which can automate various tasks, helping businesses supercharge productivity for significant cost savings. For example, TuringBots, a term coined by Forrester, is a generative AI-enabled software that assists software engineers and development teams throughout every stage of the software development lifecycle. It is worth noting that utilizing LLMs may require businesses to alter their resource models. Likewise, while there are various LLMs, one model might be a better fit over another, depending on a business’s cloud provider.

Improving the value of AI with a prompt library platform and embeddings  

Companies can exponentially enhance the value of AI across the organization by leveraging a prompt library platform. Typically, when an engineer uses ChatGPT or OpenAI to automate tasks, those prompts are siloed to that person and are not available to other team members. However, with a prompt library platform, engineering teams can share and reuse prompts within the enterprise.

Additionally, having different prompt libraries for various products is particularly valuable for larger enterprises. For example, an insurance company with personal and commercial lines could segment prompt libraries based on their lines of business.

Some prompt library platforms allow engineers to create and deploy embeddings across the enterprise for more efficiently structured code. If an engineer uses an AI model to build an API without embeddings, they will get a generic response—likewise, the code won’t necessarily fit within their current architecture. The embeddings add context to the AI model so that the code it generates fits into the user’s architecture, enormously accelerating productivity and eliminating tedious tasks through automation.

No better time to embrace change 

Many companies are still hesitant to use AI. While this technology represents one of the most significant changes in recent times to engineering teams, it is not something that companies should fear. Instead, organizations should seek to embrace AI. The innovators who realize AI’s true potential first will become the next Googles and Apples in the world. Everyone else who waited to adopt will be chasing after them for the next decade or longer.

About The Author

Adam Auerbach is VP, DevTestSecOps Practice at EPAM Systems, Inc., where his his team enables companies to realize “code to value, fast” by supporting DevOps and Agile capabilities through the Engineering Excellence and Quality Engineering practices. Before joining EPAM, Mr. Auerbach served as the VP of Quality and DevOps Engineering at Lincoln Financial Group, where he was responsible for introducing and leading the DevOps and quality engineering transformation across the company. Prior to joining Lincoln, he was the Senior Director of Technology for advanced testing and release services at Capital One Financial Corporation. At Capital One, he led the transformation to agile for the quality assurance group, as well as the adoption of DevOps and continuous testing practices across the enterprise.

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