Partners without engineering depth risk becoming ‘box pushers’ in AI era, says USEReady CEO Uday Hegde

As model providers move into services and enterprise spending shifts toward execution, partners are being forced to build architecture and engineering capabilities or risk irrelevance.

AI is pushing the channel ecosystem away from licence resale and toward engineering-led, outcome-driven delivery, as enterprise value increasingly shifts from tools to execution.

Speaking to CRN India, Uday Hegde, co-founder and CEO of USEReady, said partners that continue operating purely as resellers risk being reduced to “box pushers” unless they build capabilities in architecture, engineering, and AI delivery.

He described the change as a structural reset in the way technology value is created, delivered, and monetised across enterprise environments.

For decades, the partner ecosystem was built around a predictable structure where vendors supplied software or infrastructure, while partners captured services revenue through implementation, configuration, and support.

Hegde pointed to software ecosystems such as Tally as examples of how traditional reseller-led models created sustainable partner businesses through product distribution and implementation. He argued that AI is changing those economics by bringing software, services and execution closer together.

“That model worked because the product did what it was supposed to do, and partners could install it, get customers up and running, and generate consistent revenue,” he said.

That structure, he argued, does not hold in the AI era.

AI compresses software and services into a single stack

The shift begins with economics. Hegde said enterprise technology historically followed a clear spending pattern where software licensing formed the base layer, but the majority of value was created and captured in services.

For every dollar spent on software, multiple dollars are typically spent on implementation, integration, and operational support.

“What is happening now is convergence,” he said.

“The companies building the models are spending so much that they want a share of the services dollars as well.”

Hegde argued that model providers are moving closer to implementation and services opportunities, compressing the traditional separation between software and services layers.

That shift leaves less room for partners that operate purely as intermediaries.

At the same time, customers are changing how they evaluate technology investments, moving away from tool adoption toward measurable business outcomes.

“The conversation is no longer about access to technology,” Hegde said.

“It is about accountability for outcomes.”

Resale alone no longer defines partner value

As that shift accelerates, the traditional reseller role begins to lose relevance.

Hegde said partners that do not build deeper technical capabilities will struggle to justify their position in the value chain.

“If you are just reselling, you are not bringing enough to the table,” he said.

“You do not have the engineering depth required to deliver accuracy, governance, and outcomes.”

The transition, he added, is similar to earlier technology cycles, but is happening at a much faster pace. In the past, partners could rely on certification, distribution, and vendor alignment to sustain their business.

In AI, however, those advantages are insufficient without execution capability.

“That is the challenge many resellers face today,” he said.

“They either build these capabilities or they become irrelevant.”

Engineering capability becomes the new entry barrier

The shift from resale to execution is forcing partners to invest across multiple technical layers. According to Hegde, delivering enterprise AI requires capabilities in data engineering, application development, orchestration, and model validation.

Partners must be able to design systems that not only run models, but also integrate them into business workflows, measure output quality, and continuously optimise performance.

“When you get into AI, you need serious engineering,” he said.

“You need to understand fidelity, validation, and how to make these systems work inside real environments.”

This requirement fundamentally changes how partners operate.

Instead of deploying tools, partners are expected to build complete systems that produce consistent and measurable results.

Partner IP moves into orchestration, not models

In an agentic AI stack, Hegde said the core intellectual property does not sit in the model itself but in how systems are engineered and controlled.

Partners are increasingly responsible for designing workflows, defining execution logic, and managing how agents behave across different tasks.

This includes selecting the right model for each use case, determining cost-performance trade-offs, and setting clear thresholds for system behaviour.

“An agent continuously makes decisions,” he said.

“It has to know which model to use, how much to spend, and when to stop.”

Those decisions are governed by the architecture put in place by the partner.

Without that layer, AI systems can continue running indefinitely, consuming resources and generating unpredictable outcomes.

“If you do not define those guardrails upfront, costs can escalate very quickly,” he said.

That makes orchestration and control systems central to partner differentiation.

Economics shift toward higher margins and lower headcount

The move toward engineering-led delivery is also beginning to reshape partner economics. Hegde said firms that successfully transition to AI delivery models are likely to see significantly higher margins compared to traditional services businesses.

At the same time, the underlying operating model is expected to change.

As AI systems take over large parts of maintenance, support, and operational work, the need for large delivery teams reduces. In place of scale, productivity becomes the defining factor.

“Revenue per employee becomes a much more important metric,” Hegde said.

AI-native organisations are already demonstrating significantly higher output per employee, with smaller teams delivering revenue that would traditionally require much larger workforces.

“You can have a smaller team delivering the same or even higher revenue,” he said.

“That changes the economics completely.”

That shift, he added, is similar to how software-as-a-service models redefined business metrics compared to traditional IT services.

Enterprises still depend on partners for execution

Despite the structural shift, Hegde said enterprises will continue to rely on partners, particularly in complex implementation environments.

Large organisations are unlikely to outsource governance, security, or risk validation, which remain internal responsibilities.

However, many lack the internal capacity to architect and deploy AI systems at scale.

“The question is not whether enterprises can do it themselves,” he said.

“It is whether they have the implementation capability at that point in time.”

That gap creates a continuing role for partners, but only for those that can demonstrate execution capability rather than product access.

For partners looking to transition, Hegde said readiness is increasingly validated through external benchmarks rather than internal claims.

This includes hyperscaler competencies, analyst evaluations, and demonstrated customer outcomes.

“A partner has to show that they have done this before,” he said.

“Just saying ‘we can do it’ does not mean much.”

Customer references, repeatable delivery models, and validated expertise are becoming key signals for buyers evaluating partners. According to Hegde, this is particularly important in markets with a large base of mid-sized and regional partners, where differentiation historically depended on vendor relationships rather than engineering depth.

As AI adoption moves beyond experimentation, Hegde said the defining shift for the channel is the movement from selling technology to owning results.

Partners are increasingly expected to align with business outcomes, take responsibility for system performance, and participate in value creation rather than transaction fulfilment.

“The future partner is not the one selling licences,” he said.

“It is the one delivering outcomes.”