Product Engineer Job is Changing With AI: Here Are 3 New KPIs You Need to Look For
The integration of AI into roles across industries, let alone product development, is going beyond mere task automation.
When AI assumes more responsibility in roles (regardless of industry), those roles that have any overlaps will likely be condensed and owned by people focused on direct revenue such as business leaders and those who directly build the product, a.k.a engineers.
Yet many have speculated and argued over whether Product Engineering jobs will be gone in the next 5 years and taken over by AI and its relentless development. Yes, that’s the prediction.
With this evolution in sight another question emerges: will the traditional role of Product Engineer be replaced by AI, or will it simply be forced to evolve into a new role with a different approach (and KPIs)?
Traditional Role of Product Engineers (Before LLM Integrations)
For the sake of providing full context, to understand the role of a Product Engineer I’ll provide a mental picture:
Engineers who don’t just speak in code but in product design and customer problems.
The role naturally lends itself to responsibilities such as managing the product development lifecycle from conception to deployment via market research through to roadmapping, managing development, product testing and maintaining updates post launch.
With a set of responsibilities like the above it also goes without saying that Product Engineers have always needed effective cross functional communication for teams and stakeholders to fully leverage their holistic view of the product.
However, before the surge of AI tools and integrations, a lot of the challenges involved tasks that would take Product Engineers away from higher order responsibilities that require their deep understanding of product and market.
These tasks often come with a lack of real time data analysis, heavy dependence on manual feedback collection (surveys, focus groups, 1:1 meetings), and limited automation for monitoring product performance and user behaviour.
In a lot of ways, it seems counterintuitive then that there’s a question around AI replacing Product Engineers when it’s facilitated them to focus more energy into the human critical aspects of the role that involve strategic oversight and creative direction.
So how has this progression and more integration of AI changed their KPIs?
The Shift in KPIs With AI and LLM Integration
The rise of AI tools in software development is rapidly setting new expectations for engineers.
Not just in terms of speed and efficiency but also in terms of broadening their area of responsibilities such as ethical considerations and continuous learning of AI’s practical capabilities.
For Product Engineers however this has opened a higher level of abstraction. With AI tools handling more of the routine tasks, they can allocate more time to creative problem solving, ideation and exploring new ways to meet customer needs.
This goes hand in hand with more adoption of AI as better technology provides more real time analysis and user data to draw insights from to fuel more effective iteration of LLM products.
With that in mind here are 3 KPIs for Product Engineers in the AI Era:
1) Real Time User Interaction Analysis
The dream scenario for any Product Engineer is to know what the users want the second they want it.
Practically speaking though, we need tools to achieve this (preferably in as little time as possible) to allow quick iteration towards a better product in line with our users.
Real time user interaction analysis with text analytics tools such as Phospho would allow Product Engineers to track their users’ responses and extract insights about trends and patterns in their behaviour and sentiment as they occur.
What’s the tangible benefit of that?
- Faster feedback loop and iteration
- Untapped data source for insights
- Address problems proactively
2) Custom KPI Extraction from Text Analytics
One of the bridges Product Engineers oversee is connecting business needs directly to product development.
This KPI involves deriving insights specific to their product and business goals from text their users share within an LLM app.
The significance of this data is understated and not yet fully adopted into mainstream LLM app development but can be leveraged with Phospho to:
- Facilitate more data driven product decisions in line with business needs
- Provide more targeted and effective product iterations (features, updates, use cases)
- Access insights to previously unknown user pains and desires
3) Continuous Performance Evaluation
It goes without saying, LLM apps need constant evolution. Arguably more so than traditional products given the rate at which AI is developing in capability.
That’s why a KPI for evaluating performance ensures the product is meeting user needs and business objectives over time.
The closest gateway and touchpoint with users to derive these insights for your LLM app is with text analytics.
You can use our tool Phospho to establish clear performance metrics and implement A/B tests to develop a rich feedback loop to involve far more user insights in the evaluation process.
A Synergistic Future
Regardless of how much AI is transforming the role; Product Engineers will still need to ask crucial questions:
- How does this feature add value to the user?
- What impact will this improvement have on the product's overall vision?
- How can we measure the success of our work?
They don't just build features based on specifications, they contribute to the design and roadmap of the product through a robust understanding of the customer's needs and business strategy.
While text analytics are not yet mainstreamed in product development, at Phospho, we have made them a way to provide insights into answering these questions with far more clarity.
By tapping into these insights derived from user conversations in LLM apps, product development can meet AI and human dependency at the intersection to fuel highly effective iteration.
Sign up here to try Phospho with your own data and see for yourself!