AI agents will expand, not replace, Indian partners’ role in supply chain transformation, says Kinaxis CEO

Future partner growth will depend on supply-chain expertise, data engineering and co-innovation capabilities rather than traditional implementation and labour arbitrage models.

As AI agents become embedded in enterprise supply chain operations, concerns are growing across the channel ecosystem about whether automation could eventually reduce the role system integrators and consulting partners play in large transformation projects.

Kinaxis sees the opposite happening.

Speaking to CRN India, Kinaxis’ chief executive officer, Razat Gaurav, said AI-driven supply chain planning will increase the importance of partners, while simultaneously reshaping the skills, services and business models they need to remain relevant.

According to Gaurav, the next phase of partner growth will be driven less by traditional implementation services and more by domain expertise, data engineering, AI enablement and co-innovation capabilities.

The shift comes as enterprises look to deploy AI agents across demand forecasting, supply planning, inventory management and production planning, creating new implementation and operational challenges that extend beyond software deployment.

Gaurav pushed back on the assumption that AI agents could reduce the role of implementation partners within enterprise supply chain environments.

“I don't agree with the assumption that because of agentic AI, partners will have a smaller role to play,” Gaurav said.

“I think they will have an even bigger role.”

According to him, enterprises still require significant implementation expertise to make AI agents operational inside complex supply chain environments.

That work extends beyond software deployment and includes modelling agents, identifying the right datasets, feature engineering, algorithm selection and ensuring the underlying planning models align with customer requirements.

Gaurav said supply chain transformation remains fundamentally complex regardless of how much AI is introduced into the process.

To make these systems successful, organisations need expertise across data engineering, data science and implementation services, while also requiring ongoing operational support after deployment.

He added that managed services will continue to play an important role as customers seek to optimise AI-driven planning environments and identify new automation opportunities over time.

For Kinaxis, partners remain a “critical part” of the delivery model.

Globally, over “80 percent of the company’s software bookings involve an implementation partner”, highlighting the central role the ecosystem continues to play in customer engagements.

Future partners will move from implementation to co-innovation

While partners remain important, Gaurav believes the nature of their work is changing.

Rather than focusing primarily on traditional implementation projects, he expects partners to become “involved in collaborative development and innovation initiatives” alongside customers and vendors.

“Partners of the future will be doing co-build and co-innovation work on our platform,” Gaurav said.

According to him, AI-driven projects are moving away from large, waterfall-style implementation programmes toward shorter, more agile delivery cycles.

These engagements require continuous experimentation, rapid iteration and closer collaboration between customers, vendors and partners.

Gaurav said future implementations will involve more co-development and require a deeper understanding of customer business processes and supply chain operations.

Management consulting capabilities are also becoming increasingly important.

Partners are expected to help customers determine where AI agents should be deployed, redesign business processes, evolve operating models and establish the governance frameworks required to support AI-enabled decision-making.

The transition, he argued, is creating a different service opportunity than the one many traditional system integrators have historically relied upon.

Labour arbitrage no longer enough

One of Gaurav’s strongest messages was that traditional labour-arbitrage models alone will no longer be sufficient as AI adoption accelerates.

“Labour arbitrage alone will not be enough,” he said.

According to him, enterprises will continue to seek cost efficiency, but they are prioritising specialised expertise over access to large pools of implementation resources.

That places greater emphasis on supply chain knowledge, industry expertise, data science capabilities and data engineering skills.

Gaurav argued that successful partners will be those capable of combining deep domain understanding with technical expertise rather than relying solely on scale.

Customers expect partners to understand supply chain planning, demand forecasting, inventory optimisation and production orchestration in addition to the technology stack itself.

The evolution is also prompting system integrators to rethink their own workforce strategies.

Gaurav said Kinaxis is already seeing partners such as Genpact, Accenture, EY and Deloitte actively reskilling their organisations to align with emerging AI-driven opportunities.

As agentic AI adoption expands, he expects partners to continue investing in new capabilities that allow them to create differentiated value inside customer environments.

How Kinaxis manages channel conflict

As the company expands its ecosystem, Kinaxis is also attempting to avoid channel conflicts between partners operating in the same markets.

Gaurav said the company follows a segmented go-to-market approach that combines direct sales, co-sell engagements and co-implementation models depending on customer type and market segment.

For value-added resellers, Kinaxis limits participation within a specific segment to a single reseller.

“We do not have multiple VARs pursuing the same segment,” Gaurav said.

Segments may be defined by geography, industry or customer size.

In large enterprise accounts, Kinaxis typically works directly with customers while engaging system integrators and consulting partners through co-sell and implementation motions.

According to Gaurav, partners are primarily involved in implementation, transformation and delivery activities rather than acting as direct sales representatives on behalf of the company.

That structure enables Kinaxis to scale implementation capacity while reducing overlap across the ecosystem.

What Kinaxis looks for in partners

As the company expands in India, Gaurav said partner selection is centred around a small number of strategic criteria.

The first is domain expertise.

“They need deep expertise in supply chain planning, decision-making and orchestration,” he said.

According to Gaurav, partners that lack supply chain expertise or the willingness to develop it are unlikely to be successful within the Kinaxis ecosystem.

The second requirement is investment in skills.

While Kinaxis continues to invest in partner training and enablement programmes, the company also expects partners to commit resources toward developing platform-specific expertise.

The third criterion is customer success.

Gaurav said partners must share Kinaxis’ focus on delivering measurable customer outcomes through implementation services, feature engineering and ongoing managed services.

Because the company operates a subscription-based model, long-term customer value creation remains central to the relationship.

“When those things exist, we can grow our business together,” he said.

Kinaxis is continuing to expand both its ecosystem and its India presence. The company currently has operations in Chennai and Bengaluru, with India accounting for roughly 23 percent of its global workforce.

Gaurav said the company plans to grow its India talent base by 22 percent this year.

As AI becomes more deeply embedded in supply chain operations, Kinaxis expects partners to remain central to customer success.

The difference, according to the company, is that future growth will increasingly depend on specialised supply chain expertise, AI-related skills and co-innovation capabilities rather than traditional implementation capacity alone.