Building an AI business is not about hype, tools, or models—it is about execution, profitability, and timing. At a recent leadership interaction, Gajendra Singh, Founder of Caasaa, met Mr. Aditya Malik, COO of Veranda Group, where practical, real-world advice was shared on what truly works while building an AI startup.
This article captures those insights—validated by real business outcomes—and explains why only 6% of AI implementations succeed, how startups should approach AI execution, and why AI services must precede AI products.
The Context: A Strategic Conversation That Matters
During a recent professional interaction, Gajendra Singh engaged in a detailed discussion with Mr. Aditya Malik (COO, Veranda Group), along with insights aligned with industry leaders like Shakti Pratap, on the current realities of AI-led businesses.
The discussion was not theoretical. It was grounded in:
- Client acquisition challenges in AI
- Real-world AI implementation failures
- Startup survival strategies
- Monetization before innovation hype
Why AI Businesses Fail More Than They Succeed
Despite global AI adoption, the success ratio of AI implementation in businesses is only ~6%.
The Reason?
Most companies:
- Build AI products before understanding the business process
- Over-invest without validation
- Force AI into problems that don’t need it
- Ignore client readiness and mindset
AI is not magic. It is a business tool.
4 Core Principles for Building a Profitable AI Company
1. Understand AI Implementation Before Chasing Profit:
AI profitability depends on business relevance, not intelligence level.
- AI must directly impact cost reduction, revenue growth, or efficiency
- Most AI projects fail because they are technically impressive but commercially irrelevant
- Only 6% of AI use cases deliver measurable ROI
- Key Insight: AI must solve a business problem, not a technical curiosity.
2. Start Small: Implement AI in One Business Process Only
Instead of deploying AI across the organization:
- Pick one process
- Implement AI
- Test for 45–90 days
- Measure impact on growth, efficiency, or revenue
- If it fails → dump it and move on ( To New process & AI)
- Sticking to a failing AI use case reduces success probability to the same 6%.
- Smart AI founders quit faster—not later.
3. Build AI Services First, Not AI Products
This is where most AI startups get it wrong.
Why AI Services First?
- AI technology changes daily
- Product development needs:
- Unique differentiation
- Large budgets
- Long timelines
- Market education
- Competing with Big Tech early is unrealistic
What Works Instead:
- AI Consulting
- Generative AI Solutions
- Custom AI Implementations
- AI Process Automation Services
- Services generate cash flow. Products consume it.
- Once the market, use cases, and clients are understood—then build products.
4. Define Your AI Market & Sales Benchmark Clearly
AI businesses fail when they sell to “everyone”.
You must define:
- Target regions
- Client size (SMEs, Enterprises, Govt)
- Minimum project value ( Rs.10 lac, 25 Lac, 50 Lac, 1 Cr)
- Industry focus ( Edtech, Manufacturing, Real Estate, Services, IT/ITES)
Then:
- Build a precise sales network
- Create strategic partnerships
- Sign MOUs
- Use recommendations and channel partners
- Crack fewer but larger deals
