What you’ll learn:
- The technology AI-focused teams need to succeed.
- How to bolster existing teams with new hires and training.
- What to expect during team restructuring.
Since the Fourth Industrial Revolution introduced new ways of working and technologies like artificial intelligence (AI), every business has had to become a technology company to succeed. However, companies can’t immediately implement these new AI capabilities without the requisite training, restructuring, and experimentation.
In the same way that an aspiring athlete doesn’t wake up as a professional athlete, an organization must also invest in the right foundational technology and build solid engineering teams before it can achieve the lucrative status of an AI-first business.
The Technology Empowering AI-Focused Engineering Teams
Businesses can support their engineering teams by equipping them with different applications, such as Microsoft Copilot, to help their engineers automate time-consuming tasks like writing code. Large language models (LLMs) are another powerful tool that should be at the disposal of engineering teams, as they will enable them to supercharge productivity for considerable cost savings.
AI and LLMs will also help teams validate, ideate, and implement new features within half the time of traditional timelines. In turn, they can dedicate time and resources to critical and innovative business objectives to drive greater value.
Other AI-powered software that companies should utilize to support their engineering teams are TuringBots—visit Forrester’s website for a detailed explanation. TuringBots are AI-powered software that assists software engineers and developers during the different stages of the software development lifestyle (SDLC), whether writing, deploying, and testing code or spinning up new environments.
As a result, the entire SDLC will undergo significant change, so much so that AI-enabled engineering teams operating within mature DevOps environments could compress the usually two-week sprint into a single week. Moreover, as TuringBots become more pervasive, the SDLC will continue to accelerate.
Prompt Library Platforms and Embeddings for AI Contextual Understanding
AI-powered automation will make engineers quicker-paced and more agile than ever before. However, if there’s no way for an individual to share their ChatGPT or OpenAI prompts with their team members (or the entire organization, for that matter), then the value of AI will remain siloed with that one person.
Companies need to invest in a prompt library platform to overcome this issue and enable engineering teams to share and reuse prompts within the enterprise. For large enterprises with multiple lines of business, having a platform that can segment prompt libraries is particularly valuable.
Likewise, best-in-class prompt library platforms will let engineers create and deploy embeddings across their businesses. When an engineer uses an AI model to build an API lacking embeddings, the outcome will be a generic response; plus, chances are that the code won’t integrate seamlessly into existing architecture.
However, by incorporating embeddings, the AI model gains contextual understanding, enabling it to generate code that fits the user’s architecture. This will significantly boost productivity and automate repetitive work.
Building an AI-Focused Team: Recruiting and Retraining
Having the proper technology in place to empower AI-focused engineering teams is essential. Of course, to maximize their effectiveness, these tools, applications, and platforms need to be in the hands of the right individuals. As such, businesses must evaluate their current engineering teams and hire and retrain accordingly.
From a recruiting perspective, it was once commonplace to select candidates purely based on their engineering talent, e.g., their skills with Java or Python. Recently, however, due to the demand to deliver more client-centric (and thereby industry-specific) solutions, there’s been a shift toward hiring based on a person’s domain expertise and knowledge. Ideal candidates should also understand modern architecture and have experience building applications in the cloud.
When retraining their legacy teams, companies should note that even tools with a low entry bar, like Microsoft Copilot, still require change management activity. To that end, organizations must provide legacy engineers with reskilling opportunities and incentivize participation through reward mechanisms.
Businesses should also prepare and plan for this transition period. Senior engineering teams may require several months or longer to become proficient with new AI tools.
Inevitable Restructuring
While businesses must recruit and retrain their engineering teams to become more AI-focused, there’s a high likelihood that they will need to reduce headcount and restructure their teams. Today, a typical engineering team includes a product owner, a scrum master, a businessperson, and several developers and testers.
However, because AI tools and TuringBots will automate processes within the SDLC, there won’t need to be as many people writing code or testing. That means an ideal team will probably only require a product owner, one engineering lead or architect, one tester, and one businessperson.
Moreover, companies may have to adjust the head of DevOps to align with the evolving AI-first engineering team. Previously, the head of DevOps oversaw all tooling and supported how code moved, testing, etc. Now, this position will likely shift to focus more on platform engineering to enable various AI-related objectives within the enterprise.
Such a new platform engineering leader is responsible for the platforms that engineers utilize daily. In addition, this leader will spearhead AI enablement across the organization, whether supervising the integration of AI models or managing associated costs by tracking token usage.
Accepting an AI-Centric Future
Although there’s a nigh-universal recognition from businesses on the value of AI, many are still hesitant to implement this technology because of the inevitable change such adoption will entail. Like any other technological innovation, AI is here to stay whether or not companies want to embrace it.
Engineering roles and responsibilities will change, just as any other position adapted to the emergence of new technologies. Businesses that act quickly and maximize AI’s potential will reap incredible rewards, and those who hesitate will play catch-up, possibly for years to come.
