Cracking The Enterprise AI Sales Code

When Praveer Kocchar, the cofounder of KOGO AI, an enterprise AI platform, was asked about his first enterprise deal, he said it could never see the light of day. The problem was not the technology. The product worked and the prospective client liked what it saw. So what didn’t work? 

KOGO had built a horizontal AI stack that allowed enterprises to create custom AI-powered workflows. But this particular enterprise wanted ready-made point solutions that it could immediately deploy.

“This wasn’t a setback but a market signal that enterprise clients want plug-and-play solutions,” Kocchar told Inc42. 

The insight pushed KOGO to launch tooling that allowed enterprises to create point solutions faster on top of its stack. 

KOGO’s example captures a broader reality emerging across India’s AI startup ecosystem. Cracking enterprise deals is no longer just about building capable AI products. It is about understanding how enterprises buy software. What does it really take to sell AI to enterprises? 

At a time when every startup claims to have cutting-edge AI, what makes an enterprise choose one vendor over another? Let’s understand at length in this edition of The AI Shift.

Moving Beyond Demos

For many early stage AI startups, the hardest part of enterprise selling begins even before procurement discussions start. Founders say identifying the real decision maker inside an organisation is often the first challenge. The person enthusiastic about AI adoption is not always the person controlling budgets, compliance approvals, or deployment mandates.

Floworks AI, a YC-backed startup building autonomous agents, cofounder Sudipta Biswas described this as one of the biggest hidden risks in enterprise sales. Founders frequently spend weeks nurturing internal champions only to discover they lack authority to push the deal through. And if the startup relies too much on a single supporter within the company, the deal can disappear overnight because of internal restructuring or shifting priorities.

Then comes the machinery enterprises rarely compromise on: security audits, procurement layers, legal reviews, AI-specific data governance checks, and long master service agreement negotiations.

For startups, especially smaller ones, this process can feel disproportionately exhausting. But founders increasingly acknowledge that much of this caution is not irrational.

Trupeer.ai, an AI-based content creation platform, cofounder Pritish Gupta said enterprises today are primarily trying to minimise operational and compliance risk. Questions around where AI models process data, whether enterprise information is used for training, regional data residency mandates, and liability around incorrect outputs have become standard requirements.

The rise of regulations such as India’s DPDP Act and Europe’s stricter data frameworks is only intensifying these expectations.

Besides, enterprises also evaluate the survival of the startups they are onboarding. That question rarely appears directly in procurement documents, but founders say it influences enterprise buying behaviour significantly. Once workflows are integrated deeply into an AI platform, switching costs become expensive. This is why enterprises evaluate not just product quality, but startup stability.

The Stability Bias Among Enterprises

There is a bias built into enterprise procurement. Most large enterprises prefer working with vendors that already have strong funding, established credibility or existing relationships within the organisation because it reduces personal and operational risks for decision makers. 

Even when a startup’s product may be technically superior, procurement teams often hesitate to onboard an unfamiliar vendor. Founders say this is why enterprise AI sales frequently depend on internal champions, long-term relationship building, or partnerships with large consultancies.

This is also where AI startups face a unique paradox. The AI wave has created a level playing field where startups can compete directly against incumbents because enterprises actively want newer AI capabilities. But the same enterprises also demand the maturity, predictability and operational reliability usually associated with larger software vendors.

What Enterprises Want

AI founders often complain about endless enterprise feature requests. But experienced operators say the challenge is figuring out which demands are genuine and which are negotiation tactics disguised as roadmap discussions.

Floworks’ Biswas believes many enterprise expectations that appear unreasonable are actually rooted in fear. Enterprises want near-perfect AI accuracy, reduced hallucinations, compliance safeguards, and human oversight because they have genuine operational risk exposure.

An enterprise client may ask for extensive roadmap commitments not because they need every feature immediately, but because internal stakeholders want reassurance before approving deployment. This creates one of the biggest traps for AI startups: overpromising.

“If you say yes to everything, the deal dies on an over-promise,” Biswas noted.

Several founders echoed a similar sentiment. Being willing to say “no” has increasingly become a credibility signal in enterprise AI sales.

Flaunt, a startup building multimodal marketing agents, cofounder Swaraj Chauhan said clearly defining the product’s scope during discovery calls has helped avoid unnecessary roadmap drift. Minor customisations remain common, especially for large enterprise accounts, but successful startups avoid letting enterprise desires derail the broader product vision.

Geography also shapes enterprise behaviour differently.

Chauhan noted that enterprises in India are slower than their US or European counterparts when it comes to experimenting with AI tools in a structured way. Many companies continue to approach AI procurement like traditional software buying, which often leads to long discussions around pricing, contracts, approvals, and implementation before a deal moves forward.

ROI Is The Only Metric That Matters 

If there is one consistent theme across conversations with AI founders, it is this: enterprises do not buy AI because it sounds futuristic. They buy it because the economic impact becomes undeniable.

The central challenge for AI startups today is proving whether AI is a must-have capability or simply a good-to-have layer added on top of existing software.

This distinction determines if the deal will close. Enterprise adoption usually happens incrementally. A startup proves value in a small workflow, expands into a medium-scale deployment, and only then earns larger contracts.

The pattern repeats across partnerships with large enterprises, consulting firms, and even government organisations. This is also changing how AI startups position themselves.

Founders are increasingly moving away from selling AI as a broad transformational vision and instead focusing on highly measurable impact: reducing processing time, improving employee efficiency, cutting operational costs, or accelerating internal workflows.

The irony is that while AI is marketed globally as revolutionary technology, enterprise adoption often depends on proving something remarkably old-fashioned, which is saving money, improving productivity, and generating revenue faster than existing systems. Everything else is secondary.

Inc42’s AI Summit: 5 Key Takeaways

Inc42’s AI Summit, held on May 28 at the Grand Sheraton in Bengaluru, was designed to cut through the noise around AI and focus on what matters: execution. From enterprise adoption and AI agents to monetisation and defensible businesses, the conversations moved beyond hype to real-world impact. 

Here are the key takeaways that emerged from the day:

• Inc42’s AI Summit 2026 created opportunities for AI founders to meet potential customers, with some discussions progressing towards deals on the spot.
• For early stage startups, the summit emerged as a valuable platform to expand networks, attract investors, discover prospective customers, and exchange ideas with fellow founders.
• It also helped builders identify real-world problems worth solving, offering deeper insight into market demand and the gaps that remain unaddressed.
• A recurring theme across sessions and discussions was the growing interest in understanding how to embed AI into workflows and build systems around it. The focus has clearly shifted from using AI as a chatbot to deploying it in ways that drive meaningful outcomes and value.
• The summit also reinforced an important lesson: as AI adoption accelerates, innovation through collaboration will be critical for navigating the next phase of growth

Top Stories From India & Around The World

• • Cars24’s $20 Mn AI Push: IPO-bound used car marketplace has committed the corpus to its newly launched AI Labs initiative, which will build AI-first products and back early-stage AI ventures. The company has partnered with OpenAI, ElevenLabs and AWS as it looks to deepen AI adoption across inspections, audits and customer support workflows.
  • Predictive Banking With AI: IDFC FIRST Bank CEO V Vaidyanathan believes that AI will shift banking from reactive customer support to predictive experiences, where systems can anticipate customer issues before they raise requests. The bank is already using AI in underwriting, document intelligence, internal knowledge systems, and customer service.
  • Anthropic Expands India Leadership: The AI juggernaut has hired ex-Microsoft executive Sangeeta Bavi as head of sales for digital natives and startups in India to drive Claude adoption among mid-market firms. The move comes as India emerges as Anthropic’s second-largest market globally, accounting for over 7% of Claude usage.
  • NVIDIA Unveils Cosmos 3: The chipmaker has launched the open multimodal foundation model designed for robotics, autonomous vehicles, and physical AI systems. The company claims the model combines reasoning, world simulation, and action generation in a single architecture.

Startup In The Spotlight: realfast

As enterprises move from experimenting with AI to deploying it at scale, Bengaluru-based realfast is helping companies bring AI into their day-to-day operations through Salesforce.

Founded in 2022 by Sidu Ponnappa, Aakash Dharmadhikari and Steve Sule, realfast was built on the belief that AI adoption often gets delayed due to lengthy consulting processes, fragmented decision-making and unclear execution plans. The startup simplifies this by helping enterprises move quickly from planning to implementation.

realfast works across Salesforce products such as Sales Cloud, Service Cloud, Data Cloud, Experience Cloud and Agentforce. Its flagship 1-Day AI Blueprint service provides enterprises with a complete AI adoption roadmap within 24 hours, including architecture recommendations, implementation timelines and cost estimates.

Beyond consulting, the startup develops AI agents that are already being used in production environments for tasks such as sales development, customer support, proposal generation, follow-ups and pipeline forecasting.

With a focus on fast deployment, enterprise security and measurable business outcomes, realfast operates with SOC 2 compliance and keeps all data within Salesforce infrastructure. Going forward, it plans to expand its AI agent offerings across revenue operations and enterprise automation workflows.

Prompt Of The Week

What prompts and hacks are CTOs, CEOs and cofounders using these days to streamline their work? 

Here’s the prompt used by Ankush Sabharwal, founder & CEO of CoRover, to pressure-test foundational AI models against real-world adoption, deployment, and monetisation challenges in emerging markets.

“You are a brutally honest AI product and go-to-market advisor with deep experience in enterprise SaaS, public sector deployments and emerging markets. I’m going to describe an AI platform or solution. Your job is NOT to encourage me, it’s to rigorously stress-test whether this is a scalable, defensible and monetisable business.

Here is my AI solution:

[Describe your solution in 3–5 sentences: what it does, who it serves, deployment context and how it generates revenue]

Please analyse it across the following dimensions, in order:

1. Problem, Adoption & Market Reality

• Is this a real, scalable problem that truly needs AI?
• What are the biggest barriers to adoption, deployment and scaling, especially in India?
• What is the realistic market size and where is demand being overestimated?
  

2. Competition, Alternatives & Moat

• What are organisations using today instead?
• Why haven’t incumbents or global players solved this already?
• What is the real moat: data, distribution, localisation, partnerships, or technology?
  

3. Risks & Assumptions

• What are the three assumptions that could break the business if wrong?
• How can they be validated quickly and cheaply?

4. Steelman Case

• What is the strongest argument for success?
• What must go right in adoption, timing, execution, or policy for this to become a category leader?

 End with a single verdict:

A score from 1–10 on product-market fit and one sentence on the single most critical validation needed before scaling further.”

Editor’s Note: Some prompts may need to be adjusted by users for best results or may not work as intended for certain users.

[Edited by Shishir Parasher]
[Creatives by Varshita Srivastava]

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