Selling Enterprise AI Requires Education Before Contracts, Says kAIgentic’s Ahmed Mazhari
At Inc42's AI Summit 2026, kAIgentic CEO Ahmed Mazhari emphasized that enterprise AI adoption requires founders to educate customers on solutions and pricing before securing contracts, as many buyers are still evaluating offerings.
Intelligence analysis by Gemini 2.5 Flash
Ahmed Mazhari of kAIgentic argues that enterprise AI sales are hindered by customers' lack of understanding regarding AI solutions, their pricing, and evaluation methods. He suggests that AI founders must prioritize educating customers on organizational transformation and measurable business outcomes over merely pitching technology.
Imagine you have a super-smart robot that can help with your chores. But before you get one, someone needs to teach you what kind of robot is best for your house, how much it should cost, and how it will actually make your life easier. This story says that companies selling these super-smart computer helpers (AI) need to first teach other big companies how to pick the right ones and use them properly, rather than just trying to sell them a robot right away.
Analysis
During Inc42's AI Summit 2026, Ahmed Mazhari, founder and CEO of kAIgentic, stated that despite the projected growth of India's enterprise AI market to $71 billion by 2030, most enterprises are in the very early stages of AI adoption. Mazhari countered the misconception that enterprise buyers fully understand their AI needs, noting that many are still evaluating solutions, assessing competing offerings, and questioning pricing structures.
The Educational Imperative
Mazhari stressed that AI founders must dedicate significant effort to educating customers, moving beyond discussions of mere productivity gains to emphasize organizational transformation. He believes successful enterprise AI adoption involves redesigning workflows and achieving measurable business outcomes. Trust can open doors, but long-term success relies on demonstrating product-market fit and tangible value within existing enterprise operations.
Reducing Friction and Workflow Ownership
Ankush Sabharwal, founder and CEO of CoRover.ai, suggested that AI vendors should focus on rapid deployment, measurable results, and customer ownership. He advocated aligning AI solutions with existing business objectives rather than imposing predefined workflows. Ganesh Gopalan, cofounder and CEO of Gnani.ai, questioned the efficacy of traditional proofs-of-concept (POCs) due to a lack of commercial commitment, proposing structured field trials with clear budgets and timelines instead. Gopalan also predicted a shift towards outcome-based pricing models, where vendors share responsibility for business results.
Dushyant Garg from Nugget by Zomato underscored the importance of workflow ownership, drawing parallels to earlier enterprise software like ERP. He argued that AI generates lasting value when deeply integrated into operational systems, not as a standalone tool. Executive sponsorship and workflow redesign are critical factors determining the success of enterprise AI projects beyond experimental stages. The panel concluded that as the enterprise AI market matures, the focus will shift from pitching technology to guiding customers through evaluation, implementation, and value extraction.
Key points
- Enterprise AI adoption in India is in early stages, requiring extensive customer education.
- Many enterprise buyers lack understanding of AI solutions, pricing, and evaluation methods.
- Founders must emphasize organizational transformation and measurable business outcomes over simple productivity gains.
- Prioritizing rapid deployment, measurable results, and customer ownership is crucial for reducing adoption friction.
- There is a growing need to move beyond proofs-of-concept (POCs) to structured field trials with commercial commitment.
- Lasting value from AI is created when it's embedded into operational workflows, not as a standalone tool.
Increased education for enterprise customers could lead to more informed purchasing decisions and smoother AI integration. This proactive approach could foster greater trust and accelerate the adoption of AI solutions across diverse sectors in India, ultimately driving significant economic growth and operational efficiencies.
If AI companies fail to adequately educate their enterprise clients, it could result in continued slow adoption rates and failed implementations. Misunderstanding of AI capabilities and pricing could lead to wasted investments and a general skepticism towards AI solutions, hindering the market's projected growth.
