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Madhu Chamarty on Why the Real Opportunity in AI Is at the Lagging Edge
1-2 minute highlight clip from interview
Repeat Founder | Enterprise AI Builder | Advising Startups & VCs
Stanford | 3,400+ LinkedIn Followers
Want to apply these insights to your startup? Get the resource kit ↓
Insights from Madhu Chamarty
Repeat Founder & Investor | Building Enterprise AI | Stanford
Enterprise AI Adoption Resource Kit
How B2B Startups Can Bridge the Gap Between Innovation and Adoption. Designed for B2B AI startups (5-10 people) who have a working AI product, sell to enterprise customers (100+ employees), and keep hearing "we're not ready for AI yet."
The Enterprise Reality
Why Most B2B AI Startups Struggle to Close Enterprise Deals
"The real opportunity in AI isn't at the leading edge—it's at the lagging edge. Most enterprises are still hesitant, unsure, and need scaffolding: literacy, guidance, and enterprise plumbing."
Sound familiar? You've built an incredible AI product. But when you demo it to enterprises, you hear:
- "We're not ready for AI yet"
- "Our team doesn't know how to use this"
- "How does this integrate with our existing systems?"
- "What about security and compliance?"
- "Can you help us train our people?"
- "We need to see ROI before we commit"
Your product isn't the problem. Their readiness is.
The companies that win in enterprise AI sales aren't just building great technology. They're building adoption infrastructure—what Madhu calls "enterprise plumbing and human interaction layers."
Madhu's Framework
Why Enterprise AI Is Different
"AI adoption is fundamentally different from mobile and cloud. With mobile and cloud, the value was obvious and adoption was linear. With AI, the value increases with usage—making your customer the catalyst for growth, not just a user."
Mobile & Cloud Adoption
- Top-down IT decisions with clear ROI
- Linear adoption curves (migrate databases, build mobile apps)
- One-time integration, then it just works
- Value proposition obvious to executives
Enterprise AI Adoption
- Requires bottom-up behavior change across entire organization
- Non-linear returns (the more they use it, the better it gets)
- Continuous learning curve, not one-and-done
- Value proposition requires proof through usage
- Needs scaffolding: literacy, training, change management
The Implication for B2B Startups
You're not just selling software. You're selling transformation. And transformation requires infrastructure you must provide.
The Winning Strategy
The Two Layers Your Customers Need (And You Must Provide)
"The companies that win won't have the best AI models. They'll have the best enterprise plumbing and human interaction layers."
To successfully sell AI products to enterprises, you need to help them solve BOTH layers:
Human Interaction Layer
How do their teams actually work with your AI product?
- User training and literacy programs
- Change management support
- Best practices and playbooks
- Success metrics and ROI frameworks
- Executive buy-in materials
Enterprise Plumbing Layer
How does your AI fit into their existing systems?
- Integration with their tech stack
- Security and compliance documentation
- Data governance frameworks
- Workflow redesign guidance
- API documentation and support
- Migration and rollout strategies
Here's the competitive advantage
Your bigger, slower competitors are building AI features but ignoring adoption infrastructure. You can win by being the company that makes AI easy to actually use.
This is how you turn "we're not ready for AI" into "we can't imagine working without it."
Your Strategic Position
Why You Can Win Against Incumbents
"Incumbents have a significant advantage, but startups can still carve out a niche by focusing on specific workflows or pain points. It's crucial to think strategically about positioning."
Here's why you, as an early-stage AI startup, can win enterprise deals against bigger competitors:
Agility
You can provide white-glove adoption support that big vendors can't
Focus
You're solving ONE specific workflow deeply, not everything shallowly
Partnership mindset
You work WITH customers to implement, not just sell licenses
Customer intimacy
With 10-50 enterprise customers, you know each one personally
Speed
You can customize and adapt faster than enterprise software giants
The trap to avoid
Building a great AI product then expecting enterprises to "figure it out." They won't.
The winning playbook
Build a great AI product AND the adoption infrastructure that makes it successful in their environment.
This is why Madhu emphasizes that "the real opportunity is at the lagging edge"—it's not about having cutting-edge AI. It's about making AI actually work in conservative, complex enterprise environments.
What You'll Get
Get the complete Enterprise AI Adoption Resource Kit
Based on Madhu Chamarty's insights on enterprise AI adoption, this resource kit includes:
- Enterprise Readiness Scoring Rubric:Qualify prospects and predict sales cycle complexity
- Executive Briefing Template:Slides for executive summary, problem analysis, and next steps
- Implementation Kickoff Checklist:Pre-kickoff, kickoff meeting, and first 30 days
- ROI Conversation Script:Talk about AI value with skeptical CFOs
- Objection Handling Playbook:Responses to "We're not ready for AI yet" and more
- Quarterly Business Review Template:Track business impact and roadmap progress
- 90-Day Action Plan:Sprint-based checklist for B2B AI startups
Is This For You?
Who This Resource Kit Is Designed For
This resource kit is designed for:
- Have a working AI product
- Are selling to enterprise customers (100+ employees)
- Keep hearing "we're not ready for AI yet"
- Need to shorten sales cycles and improve implementation success
Specifically valuable if you're hearing:
- "We need to see it work at another company first"
- "Our IT team is too busy to integrate this"
- "Can you train our people on how to use it?"
- "What about security/compliance/governance?"
Not for: Consumer AI apps, internal tools, or companies selling to SMBs who can self-serve.
Answering common questions
Is this really free?
Yes. Brief is sharing Madhu's insights to help B2B AI startups succeed with enterprise sales. No catch, no spam.
How long will it take to read?
The resource kit is 12 pages. Most founders read it in 25-30 minutes, then reference specific sections (readiness rubric, ROI script, implementation checklist) as needed.
What happens after I download?
You'll get instant access to the PDF. Brief may occasionally share similar insights and founder stories. Unsubscribe anytime.
Who is Madhu Chamarty?
Madhu is a repeat founder and enterprise AI builder with extensive experience building AI products for large organizations. He advises startups and VCs on enterprise go-to-market strategies.
What is the "lagging edge" of AI?
The lagging edge refers to enterprises that are hesitant about AI adoption and need significant support: training, integration help, security documentation, and change management. This is where the real opportunity is for B2B startups.
Ready when you are
Bridge the Gap Between Innovation and Adoption
Get Madhu's tactical resource kit: readiness scoring, ROI scripts, implementation checklists, objection handling, and more to win enterprise deals.