I have seen the AI hype up close—big Enterprises, sprawling operations, billions on the line. The promise is real: smarter customer recommendations that increase in millions, chatbots that save hours, and analytics that improve ESG. But here’s the truth: pulling it off at scale with tools like Azure OpenAI and Vertex AI isn’t just about flipping a switch. It’s complex, costly, and constantly shifting—think new models coming up and old ones being deprecated.
After digging into AI adoption for a while, I have a take on how a large enterprise can make it work without losing the opportunity. It’s all about teamwork, smart spending, and staying ahead of the game.
The Big Picture: What’s at Stake
Imagine you’re running a retail giant—thousands of stores, customers in the millions. AI could push your online sales up 5%, adding hundreds of millions to the bottom line, or reduce 10% off supply chain costs. That is game-changing. But it’s not free. You’re looking at token charges piling up, cloud bills ticking higher, and models like GPT-3.5 getting phased out just as you get comfortable with the models. It’s a lot to manage - investment, technology, people, and all the updates to the models. So, how do you make it sustainable?
Who’s in the Driver’s Seat?
No one team can do this alone, it’s a group effort.
- Business Team: The Dreamers
These are the key stakeholders who see the return—like a $100M sales increase from personalized recommendations or slashing customer wait times by 20%. They’re the ones saying, “This is why we’re doing it,” and they’ll invest because it’s their win. They’re also the ones pushing for green goals, like cutting waste by 10%. Without them, it’s just tech for tech’s sake.
- Applications Team: The Makers
This team is the muscle that builds the stuff you see, like a chatbot powered by GPT-4o or a recommendation engine using Azure embeddings. They invest $500K-$1M a year in engineers, and when models get updated—like GPT-3.5 swapping for GPT-4o mini—they tweak code, test new outputs, and keep things smooth. That’s another $50K-$100K per switch, but they make it happen.
- Infrastructure Team: The Backbone
They are the unsung heroes, keeping Azure and Vertex AI running—think $200K-$500K a year on cloud costs. They host models like Gemini Pro, update embeddings when Vertex rolls out Gemini 1.5, and manage the shift when old versions fade away. That’s $20K-$50K additional to redeploy and test, but it’s what keeps the lights on for millions of transactions.
- Solution Provider: The Coach
Picture an outside pro—maybe a consulting firm or a tech partner—costing $500K-$2M a year. They’re the glue, syncing everyone up, bringing tricks like BigQuery for demand forecasts ($50K/year), and training 200+ staff for $100K. They also watch for model updates—like Azure’s o1 beta—and help manage deprecation challenges. They are in for a couple of years, then hand it over to the customer.
Keeping It Together
You need a referee in your tech strategy. An AI Steering Committee—think leaders from each team plus a CFO or CIO—meets monthly to greenlight budgets (say, $1.5M to start) and check enterprise wins (like a 4% sales bump). Then there’s the AI Champ, a $150K/year go-getter who tracks token costs, flags version shifts (e.g., Gemini Pro to 1.5), and keeps the train on the tracks.
Cost Breakdown
AI is an investment, and an ongoing one:
- Starting Line (Year 1): $1M-$3M
- Tokens: $150K-$500K—Azure’s $0.0001-$0.0004 per 1K, Vertex’s $0.00025-$0.001. Millions of queries add up fast.
- Cloud: $200K-$500K for Azure Search or Google hosting.
- Provider: $500K-$2M for heavy lifting.
- Staff: $400K-$600K for five key players.
- Version Tweaks: $50K-$100K when GPT-4o mini drops.
- Keeping It Going (Year 2+): $500K-$1.5M
- Tokens: $200K-$600K as usage grows.
- Cloud: $200K-$400K, steady unless holiday spikes hit.
- Staff: $600K after the provider steps back.
- Updates: $20K-$50K/year for model swaps.
- Who Pays?
Start with Business (50%), IT (40%), and an innovation group (10%). Later, Business takes 60% since they cash in, with IT at 40%. Batch jobs and lighter models (e.g., GPT-4o mini) reduce 20-50% off token bills.
The Game Plan: Step by Step
- Months 1-3: Hire the provider and accelerate the AI adoption.
- Months 4-6: Pilot it and watch the ROI.
- Months 7-12: Hit 50% scale, train staff, and score efficiency gains.
- Year 2: Go full scale.
Additional Considerations:
- Privacy: Allocate resources for Azure/Google security to avoid fines and protect the enterprise's reputation.
- Bias: Allocate money for audits to keep recommendations fair.
- Deprecations: Six-month plans for model sunsets (e.g., GPT-3.5), keep apps and infra relevant.
The Bottom Line
AI is an ongoing investment, complex but worth it. With Business dreaming big, Applications building fast, Infrastructure holding steady, and a Solution Provider steering the ship, you can turn AI investment into operational savings, process efficiencies, and increase in sales.
