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ELI - Building the world's first Engineering Leadership Intelligence

A few ideas on Building Engineering Assistive Intelligence

Published by Karthik Bacherao on May 16, 2024.

Why do we need an Engineering Leadership AI?

When we think AI, most people envision an artificial-intelligent computer (often times with a large red "eye") that takes over control. But when I think AI, I think of an assistive intelligence tech that complements human intelligence and arms leaders with carefully-trained wisdom empowering them to take better decisions and lead effectively.

Have you ever encountered a situation where a high-performing engineer quit a team not because he didn't find the work satisfying or the company goals aligning with his longer term career objectives but just because his work in the team wasn't intellectually stimulating; because management was just not empowered with the right tools to manage him more effectively? Sometimes all it would take is asking just one right question. How cool would it be if there were an AI to suggest: "I've noticed a certain repetitive pattern in this engineer's code checkins and emails in the past couple months. Can you try asking him if he find his work intellectually stimulating and meaningful to his near- and mid-term career goals?".

Have you seen situations when one talented engineer is over-burdened with work just because he is the "go-to" guy in the team while there are several other qualified engineers in the team with enough cycles? Burnouts are more common than you think particularly in high-performing organization. What would it take to prevent burnouts?

As counterintutive as it may be, most of these cases is not management's fault or even lack of clear communication by engineers, but I would say it's the lack of an assitive intelligence capable of putting the pieces of the puzzle together, enabling leadership to generate proactive signals and take preventive measures.

Introducing ELI

An assistive intelligence to leading organizations - perform more effectively, efficiently.

Knowledge Base - building actionable intelligence with data.

A few things to think about:

  • Company principles and values
  • Bulding organizational guidelines
  • Inferring Organizational Priorities
  • Technical Knowledge - what's the current state of tech. What's the cost to build a certain complex piece of tech.
  • Business Intelligence - effective leadership intelligence requires a deep understanding of the business
  • Understanding people - an ongoing task keeping in mind that people's priorities, aspirations and life situations change.
  • Understanding strengths and weaknesses - of teams, people, and organizations.
  • Identifying the right/key metrics to measure
  • Proactively measure - Measuring proactively and perhaps even automate this with AI
  • Proactive Signals - Sending the right signals proactively. Not just rewarding people who send signals but encouraging engineers and managers to identify the right signals. "There is a high probability that we may miss the delivery date by one week".
  • Actively Respond
  • Building the feedback channels both top down and bottom up.
  • Setting priorites, translation high-level goals to organization-level objectives.
  • Handling multiple priorites and communicating effectively.
  • Clear Objectives
  • Crisp Communication: communicate objectives in simple, measurable terms.
  • Risk Assessment
  • Work Assignment - consider strengths, priorities, and risks.
  • 1:1 -- asking the right questions.
  • Handling conflicts.
  • Organizational Wisdom