Pavan Kumar T V

CTO | Technology Leader

Building AI-First Organizations: Lessons from the Trenches

From BRIDGEi2i Analytics Solutions to Trilogy's PE-driven portfolio companies , I've collected a few learning and insights worth sharing. This isn't theoretical advice; it's what actually worked (and what spectacularly failed) when turning traditional companies into AI-powered organizations.

Real-World AI Transformation

Trilogy's Portfolio Transformation

When I joined as SVP of Engineering across the TelcoDR, Ignite, and DevFactory groups, I noticed a fragmented tech landscape. I was involed in the acquisition of AnswerHub and InsideSales, with completely different tech stacks and engineering cultures.

Over 18 months, we:

  • Stitched together systems from acquired companies (trust me, nothing tests your skills when you takeover with no co-operation or knowledge transfer)
  • Remodel everything to new optimized services
  • Pushed for 1-click Dev Environments to avoid "works on my machine"
  • Started using AWS Graviton3 for AI workloads, giving us computational power without less money

Cross-Portfolio Innovation

The real magic happened when we started working across portfolio boundaries. We:

  • Built telco BSS systems with AI at their core—something the industry had talked about for years but rarely actually did
  • Created a remote work platform for engineers
  • Used AI to extract useful knowledge from legacy codebases that nobody wanted to touch
  • Made tech stacks similar enough that engineers could move between products without six weeks of learning curve

BRIDGEi2i Experience

My time as Director of Technology at BRIDGEi2i was equally formative. We were building AI solutions before it was cool, which meant:

  • Building ML infrastructure when most companies were still figuring out what ML even meant
  • Making reusable AI components that cut solution delivery time
  • Creating frameworks that turned data scientists' experiments into production-ready systems
  • Teaching teams to think "ML-Engineering" when that wasn't yet a LinkedIn buzzword

Key Learnings from Implementation

1. Portfolio-Wide AI Strategy

The biggest revelation at Trilogy? Working across portfolio companies created exponential value. We:

  • Built cloud infrastructure once but used it everywhere
  • Made development platforms that worked across products
  • Started AI initiatives that learned from all our data, not just siloed portions
  • Set up cost optimization that worked across the entire portfolio

2. Technical Foundation

We learned the hard way that AI transformation fails without solid fundamentals:

  • Cloud-native architecture (because putting AI into monoliths is like putting a jet engine on a bicycle)
  • CI/CD pipelines that actually built confidence, not fear
  • Knowledge sharing that prevented teams from solving the same problem twice

3. Team Evolution

The human element was the hardest yet most rewarding part:

  • Changing from traditional dev to Engineering culture (I still have the "but that's not my job" t-shirt)
  • Teaching cloud skills to teams who'd never used AWS
  • Developing AI/ML capabilities when talent was scarce
  • Breaking down product silos to enable true collaboration

Common Challenges I've Faced

1. Legacy Modernization

I've never met a legacy system that went down without a fight. We succeeded by:

  • Taking a methodical refactoring approach—no big bang rewrites that never ship
  • Moving to cloud-native bit by bit
  • Making performance better where it mattered, not where it felt good

2. Technical Integration

Post-acquisition integration is where strategies live or die. Our winning formula:

  • Building modular systems that could exist side by side during transition
  • Creating similar enough deployment patterns to ease migration pain
  • Managing infrastructure as code to eliminate configuration drift headaches
  • Setting up monitoring so we had a single source of truth when problems arose

Measuring AI Impact

The metrics that actually mattered:

  • Infrastructure cost reduction
  • Development velocity doubling without adding headcount
  • Resource utilization improving by through smart allocation

Current Focus Areas

I'm obsessed with:

  • Building cloud-native AI platforms that make ML capabilities available to more people
  • Creating developer experience tools that make AI accessible to more engineers
  • Making infrastructure work better for the new generation of AI workloads
  • Building knowledge systems that capture and amplify organizational learning

Looking Forward

Based on everything I've seen in the AI transformation trenches:

  • FinOps isn't a nice-to-have—it's the difference between sustainable AI and burning cash
  • Developer experience automation will separate winners from losers
  • AI-driven infrastructure optimization will become table stakes
  • Cross-product integration will create the biggest competitive advantages
  • Scaling practices need to be environmentally and fiscally sustainable

The honest truth? Successful AI transformation in a PE environment requires constant navigation between innovation and pragmatism. You need to embrace cutting-edge tech while rigorously focusing on cost optimization and developer productivity. It's less about following AI hype cycles and more about building sustainable systems that deliver real value.