In this conversation with CXO XPERTS, explains why the firm is now pushing into the mid-market — and why AI, more than pricing, is the real unlock. He discusses outcome-based pricing that removes upfront cost, how contract and invoice data can self-fund an enterprise AI budget, why overcoming organizational resistance comes down to speed of first results, and what a realistic AI adoption roadmap looks like for a mid-sized business.
1. Why the Mid-Market, Why Now
Q: USEReady built its business serving Fortune 500 and S&P 500 companies. What’s driving the move into the mid-market, and how are you defining that expansion strategy?
A: Our original model was built around large enterprises largely because of economics — we were expensive, and that pricing never worked for mid-sized companies, particularly in India, where we hadn’t yet learned how to engage local enterprises effectively. There was also a simpler logic at play: if large clients were already generating strong revenue, there was little incentive to go downstream.
That calculus has changed. The real difference between a large company and a small one has always come down to budget: large firms could afford the data and technology to make better decisions, and they used that advantage relentlessly. AI is closing that gap — what I’d call the “information arbitrage” between big and small companies is disappearing, because a smaller company can now make data-driven decisions without a massive infrastructure investment. Companies that adapt to this, regardless of size, will pull ahead — and mid-market firms finally have a real chance to close the gap with better-resourced competitors.
What counts as “mid-market” for USEReady?
USEReady has traditionally served the Fortune 1000 and S&P 500. It’s now expanding into two new tiers: Large Enterprise ($1B–$5B in revenue) and Mid-Market ($500M–$1B).
Pricing was the other barrier, and we’ve restructured our model around it. Instead of quoting a mid-market company millions of dollars for a large implementation, we now say: we won’t charge you upfront — we’ll take a percentage of what we save or generate for you. That’s a fundamentally easier conversation, and it accelerates outcomes.
Large enterprises today split into two camps: those already embracing this shift, and those still weighed down by bureaucracy and resistance. Mid-market companies — including the generational, promoter-led businesses I meet regularly — are, in many ways, better positioned to move fast. Telling a manufacturing founder “if I make you $20 million, what will you give me?” is a far more compelling conversation than the one I’d have had two years ago, which opened with an 800-hour, $2 million price tag.
2. US vs. India: Two Different Playbooks
Q: You’ve built out the US market extensively and are now looking closely at India. How does your approach differ across the two, particularly for mid-market clients?
A: In the US, most companies are doing one of a few things: rolling out ChatGPT or Copilot to every employee at $10 a head, running a string of proofs-of-concept, and hoping something sticks. In India, companies typically go to vendors and ask, “everyone’s doing AI — what should I do?” — and the vendor’s answer is almost always to sell more technology. Both approaches are siloed, and both end the same way: a CFO asking why everyone wants to spend more money without showing any return. I’ve seen mid-sized US banks roll out ChatGPT enterprise-wide, run fifty proofs-of-concept, and see none of them reach production.
Our actual offerings differ by market. In the US, we focus heavily on eliminating “shelfware” — software licenses a company pays for but doesn’t use. We applied this internally first, retiring a number of licenses and design tools we’d stopped using. The same applies to CRM, analytics, and other seat- or consumption-based licenses that renew automatically without anyone checking utilization. We identify the gap between what’s paid for and what’s actually used, help renegotiate with the vendor, and take a percentage of the savings — with the rest reinvested in AI. We do similar work on insurance, reading every policy a company holds to flag where it’s over- or under-insured; businesses routinely keep paying for coverage tied to assets or exposures that no longer exist.
In India, the sharper use cases are around operational waste and fraud. One example from the chemicals sector: delivery trucks often can’t fully offload at a factory because of tank capacity constraints, so product that should have been delivered is quietly discarded on the return trip, with no tracking or accountability for the loss. AI makes that loss visible and fixable. We’re also seeing strong demand for AI-driven fraud detection in accounts payable — catching payments that shouldn’t have gone out, months before a routine book-closing process would ever surface them.
3. The Self-Funded AI Model: Turning Waste into Budget
Q: Mid-market companies often lack the capital to invest upfront in AI. How does the self-funded AI strategy actually work?
A: Every large mid-size enterprise carries a meaningful amount of wasted spend, and that’s where we start. We treat it as a business problem, not a technical one: where is the waste — insurance, shelfware, procurement overspend — and how do we convert that into a funding source for the AI journey?
We pick the use case based on the client. I have a friend at a large bank who was confident there was no waste in his organization — they reconcile licenses every month. But when his team tried auditing actual seat usage, the sales and editorial teams pushed back, framing any cut as a threat to revenue. Nobody had independent visibility into real usage, and most licensing models are seat-based rather than consumption-based, which hides the waste. That’s the gap we close.
Beyond finding waste, the exercise forces a business to feed its contracts and invoices into a system that can actually cross-reference them — something that rarely happens naturally. That surfaces direct, previously unanswerable questions. One I ask often: how much revenue are we leaving on the table? We found our own answer internally — a client engagement had been billed at 20 hours a week for three years even though the contract allowed for 40 and our consultant had the capacity; nobody had simply asked why. In another recurring pattern, customers who reliably buy one service from us should, by pattern, also buy a related one — when they don’t, that’s a signal to reach out before they churn, not after.
The same discipline applies to spend. We’ve found procurement patterns where a business pays a premium for small, urgent orders it could have avoided with planning, or splits volume across multiple suppliers with weaker terms instead of consolidating with the one offering the best terms — for a straightforward, immediate saving. These are questions a standard ERP or procurement system was never built to answer. Once a business starts asking them, the savings — and the AI budget they fund — tend to compound on their own.
4. Breaking Down Resistance: The Real Cost of Change
Q: Mid-market companies — especially generational, promoter-led businesses — tend to take a more conservative approach, and convincing them can require significant hand-holding. With sales cycles that can run two quarters or more in India, how do you manage that cost as a business?
A: There’s resistance built into every layer. The founder or promoter usually wants change, but the next layer of management is often insecure about what it means for them — not out of malice, usually, but loyalty to their teams and fear that roles will disappear. On top of that, incumbent vendors know that once our solution is in, pricing gaps become visible — what we can deliver for a fraction of the cost, they may have been charging ten or twenty times more for. So they push back hard.
I’ve had cases — one large regional newspaper being a good example — where the incumbent vendor’s argument was essentially, “this works for American companies, not for us,” simply because the current setup includes licenses and spend that shouldn’t exist and would be exposed. Meanwhile, the founder can see the destination but not the path, and we still need the incumbent team’s cooperation to get there — so it’s a real transition challenge at every level.
What mistakes are mid-market firms making with AI today?
Buying into existing vendors’ AI add-ons, handing out ChatGPT or Copilot seats, and running fragmented proofs-of-concept — then hoping something sticks. The result: siloed activity, no visible productivity gain, and a CFO asking who’s paying the token bill, while outside advisors push even bigger tech investments. The better path: eliminate operational waste first, and use those savings to fund long-term AI modernization.
Our pricing model is built to cut through exactly this friction. Because we take a percentage of savings rather than charging upfront, there’s very little downside to saying yes quickly — I can show measurable impact within about two weeks rather than asking for a six-month commitment before any value is proven.
And once a client sees one result, the dynamic shifts fast. The person who was most resistant often becomes the biggest advocate, because they’re now the one who delivered the win — sometimes becoming the internal face of the transformation. In one shelfware engagement, projected savings reached several millions within two weeks, and the resulting fee — a percentage of that — was so large that the client had to renegotiate the contract to cap it, since no one internally was authorized to approve a check that size.
The broader point: companies like Apple and Walmart aren’t dominant just because they’re big — they’re big because they have superior visibility into their own operations, pricing, and procurement. Smaller firms often assume their limitation is a lack of customers or salespeople, when the real constraint is operating efficiency. That extra 10–20% margin is what funds faster growth, and that’s the value AI unlocks. Despite how much is written about AI adoption, I’d estimate only around 2% of companies are actually capturing this value today — usually not for lack of intent, but for lack of the right systems. AI can deliver that capability even to a business that hasn’t built clean architecture or processes to begin with.
5. Breaking Down Silos with AI Agents
Q: Indian mid-market businesses — manufacturing, supply chain, cooperative and regional banks — tend to be highly siloed. How do you unlock full value from your solution when the underlying organization is structured that way?
A: The silos exist at two levels: organizational and technical. Contract management doesn’t talk to CRM, CRM doesn’t talk to accounting, accounting doesn’t talk to inventory, and inventory doesn’t talk to HR. Traditionally, fixing that meant building custom integration technology, which is slow and expensive. AI makes this far easier.
We deploy an agent on top of each existing system rather than replacing it — an invoicing agent sitting on the finance system, a contract agent on the legal system, and so on — without requiring any migration. The real value comes from what we call “orchestration as a service”: we build individual agents for each function — sales, finance, legal, invoicing — and then orchestrate them to communicate with each other, much like an HR function helps different teams within a company collaborate. As far as I know, we’re one of only two firms globally offering this as a packaged service.
That orchestration layer is exactly how our waste-detection use cases work in practice: we connect the invoicing agent, the contract agent, and the software-usage agent, and let them cross-reference each other. The output is direct — “this was purchased, nobody logged into it, this is what was paid for it, this is lost spend.” That’s how we break down departmental silos without asking a client to rebuild their technology stack.
6. The Coming Divide: Winners and Losers in AI Adoption
Q: Stepping back, how do you see the broader AI services landscape evolving in the mid-market?
A: I think it’s going to be a genuine bloodbath — clear winners, clear losers, very little middle ground. Look at Jane Street: last quarter, its trading revenue reportedly exceeded that of Goldman Sachs, Citibank, and Morgan Stanley — a firm of three to four thousand people outperforming institutions with tens of thousands of employees, largely because they invested aggressively and early in AI infrastructure.
The companies making early moves will be the biggest beneficiaries. Everyone tends to think of AI purely as a cost-cutting tool, but it’s equally a revenue driver — it can help you find customers, grow margin, and free up capital to reinvest in the business. That combination compounds.
We’re already seeing this in the US, where tariff and inflation pressure forced companies to find margin without passing costs on to customers. The ones who embraced AI to do that — rather than cutting headcount or squeezing vendors — came out ahead. I expect the same dynamic to move into India and other developing markets, and the gap between adopters and non-adopters will only widen.
7. The AI Playbook: A Blueprint for Mid-Market Success
Q: In your view, what does a successful AI adoption journey look like for a mid-market company, from start to finish?
A: We call it the AI playbook, and it always starts the same way: identify wastage first — shelfware, insurance, procurement overspend — and use that saving to self-fund the AI budget while building organizational buy-in around a visible, low-risk win.
That first phase typically combines two capabilities: contract intelligence, because everything a company buys or sells runs through a contract, and finance agents covering accounts payable, receivable, and invoicing. Once that use case proves out, most clients ask us to build it as a permanent capability rather than a one-off exercise.
Does USEReady have a set playbook for mid-market AI adoption?
Yes — a blueprint, customized to each business:
• Start by eliminating waste to create a funded AI budget.
• Reinvest the savings into contract and invoicing intelligence — the ability to ask revenue- and spend-related questions.
• Move next into procurement, HR, or operations, based on the client’s priorities.
• Apply AI to grow the business and increase sales.
From there, the roadmap depends on the client’s biggest pain point. Some move into procurement, others into HR, others into broader finance or revenue growth.
The pattern holds across clients: start with legal and finance to find savings, self-fund the first deployment entirely from that saving, then move into whichever function — spend or revenue — matters most to that business.
