Q1. How do you handle conflicts between product and engineering priorities?
First, I recognize that conflict between product and engineering is not a problem—it’s a sign that both sides care about delivering value. Product typically optimizes for customer needs, time-to-market, and competitive advantage, while engineering focuses on quality, scalability, and technical health. These conflicts are natural and in some way healthy to ensure that proper balanced decisions can be made.
- I will first try to evaluate the Technical Debt work or Architectural improvement if any work that the team is planning and what could be the cost involved if the work is delayed. For example , if the work involve any change to Database/ storage then the priority of such work could be high. I would try to explain the cost and why it needed to be done first.
- I would encourage both sides to evaluate priorities using a shared prioritization framework — one that considers business value, risk, technical feasibility, customer impact, and urgency.
Ultimately, my goal is to move the organization from “Product vs. Engineering” to “Product + Engineering vs. the Problem.” When both teams see themselves as partners working toward the same outcome, conflicts become collaborative problem-solving exercises instead of battle
Q2. Assume two or major customers are asking for their features to be delivered first. How do you handle that conflict?
First as an Engineering Manager/Director, I assume the Product Manager’s (Product) would be the first contact point to the customers . If I have a say in the decision or I need to advise ‘Product’ – my perspective would be that it’s crucial to shift the conversation from “who is louder” to “what’s most strategic for the company or my account”
I start by working with Sales, Customer Success, and Product to understand the business value of the features
- Business impact (revenue, retention) – Is it tied to a renewal, expansion, or upsell? Is there revenue, contract, or churn risk involved?
- Is this a long-term strategic customer or a short-term gain?
- Does it affect a large user base or solve a critical blocker?
- What’s the potential revenue risk if it’s delayed?
- Does one request create significant tech debt or misalign with platform strategy?
If possible, we may propose/offer workarounds, phased delivery, or early access/beta programs to keep them engaged
Once we decide on a plan, I ensure that communication to both customers is honest and proactive. For the customer whose request is being sequenced second, we would explain:
- The expected delivery timeline
- What will be done now vs. later
- How their needs are still being prioritized like we are planning a Spike/PoC first etc
Give a sample scenario you had handled this scenario like
“Recently, two enterprise clients requested exclusive reporting dashboards—both tying it to QBRs. One had a $1M renewal risk; the other was a new logo expansion. We engaged Sales, Product, and Engineering to assess delivery impact and decided to prioritize the renewal-critical feature first. Meanwhile, we delivered a partial dashboard to the second customer that addressed 70% of their needs. Both were satisfied with the transparent handling”
Q3. How do you see Generative AI impacting engineering organizations in the next 2–3 years?
On the developer productivity front, tools like GitHub Copilot and ChatGPT are already reducing time spent on boilerplate code, test writing, and documentation. It is already a game changer. In my project we are able to increase the productivity by minimum 30% using the GitHub Copilot with help of its Auto Code completion feature, Unit Test writing and generating boilerplate code. Auto PR review feature inbuilt in GitHub also helps save big time on the PR reviews.
GitHub Copilot agent has upped the game already with its capability to independently look for build issues /error, refer documentation and iterate multiple times to resolve the issues on its own and also it can raise the PR -effectively acting like a Junior pair programmer.
I can see Automation testing capacity to be reduced at least by 50%. With Copilot/Gen AI tools becoming more mature, it will not be long before test cases can be generated based on the acceptance criteria defined in the User Stories.
As we move toward AI-assisted development and testing, the demand for techno-functional roles — individuals who understand both business logic and technical implementation — will grow significantly. Professionals who can understand and better utilize these tools over other will be in demand in future.
However on the other side, GenAI also introduces new challenges around governance, data privacy, security, and hallucination risks. So, I believe organizations will need to establish AI guidelines, safe experimentation spaces, and internal review processes to manage these risks responsibly.
Finally, GenAI will reshape engineering orgs structurally — teams will shift toward AI-assisted workflows, and leadership will need to invest in upskilling engineers in prompt engineering, AI safety, and responsible usage.
In short, Generative AI will augment, not replace, engineering teams — making them faster, smarter, and more business-aligned. Those who adapt early will have a strong competitive advantage