Customers want AI, but most are not ready for it, says Plus91Labs

Enterprises rush to adopt AI, and partners are finding that data quality, use-case definition and organisational readiness often matter more than the technology itself, creating new opportunities in consulting and managed services.

Enterprise demand for AI may be accelerating, but many organisations are still struggling with a more fundamental question: what exactly do they want AI to do? That gap between ambition and readiness is increasingly creating a new opportunity for technology partners.

While AI platforms, copilots and autonomous agents continue to dominate boardroom conversations, partners are finding that customers lack the data foundations, process maturity and clearly defined use cases required to move beyond experimentation.

“Enterprises are approaching partners with a strong desire to adopt AI, but many are still in the early stages of understanding how the technology can be applied within their businesses,” Jyoti Singh, co-founder of Plus91Labs, told CRN India.

"Many organisations say they want AI, but they are still trying to figure out what exactly they want AI to do for them," Singh said.

That reality is reshaping the role partners play in AI engagements. Rather than beginning with implementation, many projects are now starting with consulting, readiness assessments and business process discussions.

According to Singh, AI adoption increasingly depends on two factors: data readiness and use-case readiness.

AI demand is outpacing enterprise readiness

The rapid rise of AI has created significant pressure on organisations to define strategies and identify opportunities.

However, Singh believes many enterprises remain at the exploration stage.

While AI budgets are beginning to emerge and leadership teams are eager to discuss automation and intelligent agents, organisations often struggle to identify the specific business problems they want AI to solve.

According to her, that challenge is forcing partners to spend more time helping customers understand potential use cases before discussing implementation.

Rather than immediately deploying AI technologies, Plus91Labs now begins many engagements by evaluating organisational readiness and identifying areas where AI can create measurable business value.

The company has invested heavily in AI capabilities and currently has between 50 and 60 professionals certified on Salesforce's Agentforce platform.

However, Singh said technical readiness within partner organisations is only one part of the equation.

"The bigger question is whether customers are ready," she said.

Data quality is emerging as the biggest barrier

According to Singh, the most common obstacle to AI adoption is not technology availability but data quality.

As organisations explore autonomous agents and AI-driven decision making, the quality of underlying enterprise data increasingly determines the quality of outcomes.

"Agents can only act on the data they are given," Singh said.

"If the underlying data is inaccurate or incomplete, the outcomes generated by the agents will also be unreliable."

That reality is changing the nature of AI projects.

Instead of beginning with technology deployment, organisations are increasingly being asked to assess data quality, governance frameworks and information management practices before moving into implementation.

According to Singh, AI systems will ultimately reflect the quality of the information they receive, making data preparation a critical prerequisite for successful adoption.

The challenge becomes even more significant as organisations move from traditional automation to agentic AI models, where software agents are expected to make decisions, perform actions and interact with enterprise systems with minimal human intervention.

Consulting emerges before implementation

The growing focus on readiness is also creating a shift in partner business models.

Historically, much of the technology services industry has been built around implementation projects.

AI, however, is creating demand for a different type of engagement.

According to Singh, customers need guidance around readiness, use-case identification and business transformation before they need technology deployment.

As a result, consulting is becoming a larger part of the AI conversation.

"We are not only providing implementation services; we are also helping customers understand what AI can realistically do for them," Singh said.

That shift is prompting partners to move further upstream in customer engagements.

Instead of responding to implementation requirements, they are helping organisations define strategy, assess maturity and establish AI roadmaps.

The trend also reflects a broader evolution within the channel ecosystem as partners seek to differentiate themselves beyond technical deployment capabilities.

According to Singh, organisations are willing to invest in advisory expertise if it helps them achieve measurable business outcomes.

Managed services become the next recurring revenue opportunity

Beyond consulting, Singh believes AI will create a second major opportunity for partners through managed services.

As enterprises deploy AI agents and automation platforms, ongoing management becomes increasingly important.

Unlike traditional software deployments, AI systems require continuous monitoring, performance evaluation and refinement.

According to Singh, organisations need mechanisms to validate outputs, identify inaccuracies and improve agent performance over time.

Once agents are deployed, someone must ensure they continue operating effectively, monitor outcomes and adjust as business requirements evolve.

That responsibility creates an opportunity for partners to build recurring revenue streams around AI operations and management.

"We take responsibility for supporting them, ensuring they continue to operate effectively and making enhancements whenever changes or improvements are required," Singh said.

Industry observers increasingly view managed AI services as one of the more sustainable monetisation opportunities emerging from enterprise AI adoption.

For partners, the opportunity extends beyond deployment into long-term operational ownership.

Outcome-led partners may have the advantage

The shift toward consulting and managed services is also changing how partners position themselves in the market.

According to Singh, implementation alone is becoming less of a differentiator.

Customers want partners that can help improve processes, identify inefficiencies and drive measurable business outcomes.

"We don't just implement technology. We deliver outcomes," she said.

That approach requires partners to move beyond software deployment and become more involved in business transformation discussions.

As AI adoption matures, Singh believes the partners that succeed will be those capable of combining technology expertise with consulting, domain knowledge and long-term operational support.

For now, however, the immediate challenge remains helping organisations prepare for AI itself.

Despite growing enthusiasm around AI agents and enterprise automation, Singh believes many organisations still have foundational work to do before they can realise meaningful value.

The opportunity for partners, therefore, may not begin with implementing AI.

It may begin with helping customers become ready for it.