The SaaS Apocalypse & Industrial AI — From Wrappers to Real Intelligence | 202X Vision

Host: Vishwendra Verma, Founder, GrowthSutra

Expert Panelists:

Dr. Sudhin Baraokar — Global AI & Quantum Engineer Advisor, Co-founder at Deep Tech Startups; ex-SBI, Barclays, IBM, GE
Dharmender Kapoor (DK) — AI, Digital Transformation & Business Advisor; Former CEO, MindSprint and Birlasoft

Comprehensive QnA basis the Expert Panel Discussion from Growth Sutra's March 2026 LinkedIn Live Session

Opening Context

Global AI spending is projected to hit $2.52 trillion by 2026 (Gartner), yet companies have little measurable to show for it. What were once called AI investments are now described as expensive subscriptions dressed up in LLM interfaces. The market is penalizing this: $285 billion wiped from software valuations in early 2026, software stocks losing over 20% of their value — a "SaaS apocalypse." 82% of companies report cutting SaaS and IT vendors unable to demonstrate returns (Forbes).

The dialouge was centered around three discussion pillars :

(1) What is the SaaS apocalypse and why are processes breaking?

(2) Where does real opportunity live in industrial complexity?

(3) What is the concrete playbook for builders?

1. The SaaS Apocalypse

Q: What exactly is an AI wrapper, what is an AI-native model, and why is AI fundamentally not a SaaS product?

Dr. Sudhin Baraokar: An AI wrapper is a thin interface layer connected to frontier foundation models — GPT, Gemini, Grok — via API calls. It uses the LLM's power, returns a JSON, adds a UI, and presents that as a product. An AI-native model is the next step: organizations building Small Language Models (SLMs) tailored to their own use cases, internal data, and existing frameworks — using LLMs for broad scale and SLMs for deep-dive analysis.

The core reason AI is not a SaaS product is architectural. SaaS operates at level one: business rules and intent. AI operates at level two (reasoning) and level three (decisioning) — both completely model-based and updated continuously, sometimes over the air. That is a fundamentally different paradigm.

Q: You have lived through ERP, cloud, and digital transformation disruptions. What does history tell us about who survives AI disruption and who doesn't?

Dharmender Kapoor: Every prior wave — ERP, SaaS — began by wrapping existing systems to improve usability and access. That approach delivered limited innovation and eventually collapsed under its own weight, like fitting a car engine to a bullock cart. With AI, a third dimension enters: native intelligence. Wrappers are not just insufficient — AI exposes their inadequacy.

The key difference now is that value lies in judgment plus learning loops: systems that continuously learn from history, adapt to new signals, and inform future decisions. Those who survive will be the ones who re-architect the core, own the data and decision loops, and collapse process latency. The casualties will be those focused only on interfaces and aggregation without depth.

Q: What specifically breaks when you apply the wrapper philosophy to process-rich industrial environments like manufacturing plants, energy grids, or chemical facilities?

Dr. Sudhin Baraokar: Large industrial conglomerates operate at extraordinary complexity — over 10,000 function points in large automakers, 15,000+ in large industrial conglomerates, 2,000 live production databases, 5,000+ source systems. The wrapper model was never designed for this. The AI opportunity here is actually 100x what banking has offered, because these organizations sit on massive untapped data richness — sensor data, IoT data, plant flow data — that can now be integrated into reasoning and decision models.

Dharmender Kapoor: The industrial environment is not a software problem — it is a continuous systems problem involving time sensitivity and safety. Four elements break under the wrapper approach:

Real-time reality gap — Wrappers produce lagged intelligence; business, data, and technology contexts all change continuously, requiring real-time decision-making.
Data fragmentation vs. system coherence — Wrappers pull data from ERP, SCADA, MES systems but do not understand the contextual coupling and relationships between them. AI sees data; it misses relationships.
Stateless intelligence — Wrappers produce intelligence with no memory of system state, whereas effective industrial AI requires stateful, cumulative understanding.
Exception management — Rather than just "human-in-the-loop," industrial AI must record exceptions, learn from them, and prevent them from recurring — building progressively better decision intelligence.

2. Where Real Opportunity Lives in Industrial Complexity

Q: Where is the real opportunity hiding inside process-rich industrial environments, and what kinds of AI models actually work here?

Dr. Sudhin Baraokar: The highest-value use cases include:

Predictive/Prescriptive Maintenance — AI not only predicts failures but provides nudges and recommendations (prescriptive maintenance), avoiding costly emergency fixes and unplanned downtime. This is now mission-critical infrastructure.
Supply Chain Resilience — COVID exposed catastrophic fragility. Autonomic robots, autonomic guided vehicles, and over-the-air model updates (e.g., Ford building a car in 35 seconds with continuously learning robots) represent what's possible.
R&D and Smart Materials — AI enables smarter material design, hydrogen ecosystems, and next-generation manufacturing processes.
Quality Assurance and Design — Computer vision, digital twins, and Fourier transform signal analysis (e.g., detecting rattling parts by frequency across door components) open entirely new frontiers.

The framing matters: this is AIX — AI for Everything — not AI for technologists alone. Every function, every event, every process in an organization becomes a node in a neural network that can be analyzed, reasoned upon, and decided upon.

Q: What has shifted in the last 18 months that makes this the right moment for AI-first founders and IT companies to move decisively into industrial sectors?

Dharmender Kapoor: Four shifts are happening simultaneously:

Legacy systems — Models can now translate legacy code (e.g., COBOL to Java) rapidly, but translation is not transformation. Real depth is still required, opening space for specialists.
Sales cycles — Knowledge is now abundant for everyone; differentiation comes only through contextual depth. Vendors who understand the specific decisions a client needs to make will win. Generic horizontal proposals are obsolete — you must arrive with context already built into your proof of concept.
Domain expertise — Previously, "domain appreciation" sufficed. Now real domain expertise is required because business context must be married with technology before client engagement, not discovered from zero during the engagement.
Buyer behavior — Buyers everywhere are becoming more sophisticated. They must now own their first mile (what they need), last mile (how decisions become actions), and middle path (readiness for AI enablement). Buyers who do not know what they need will not get what their business actually requires.

Q: What is the AI readiness assessment framework you recommend, and how long does it take?

Dharmender Kapoor: Traditional readiness looked at systems, databases, and infrastructure as a static portfolio. The new element is the decision. The framework uses a 2×2 matrix of decision complexity vs. current system suitability, with approximately 35 parameters covering process quality, data completeness, data quality, technology portfolio, and decision criticality.

For a mid-tier company, this takes 8–10 weeks — studying systems, decision processes, and aligning business and technology stakeholders. The output is a prioritized roadmap of which decisions are ready to be AI-enabled today and what must be built to enable others. Critically, it is not a one-time exercise: data and context drift continuously, so readiness must be re-assessed over time.

Q: What is a realistic cost to start building AI in a mid-size manufacturing company?

Dr. Sudhin Baraokar: The cost to start is far lower than perceived. LLMs and cloud infrastructure are available as services; SLMs cost roughly one-tenth to one-twentieth of LLMs to build and run. Vibe coding tools and cloud APIs lower the barrier further. A full SLM pilot — including a neural network OS for an automotive use case — has been built for under $1,000. Entry-level experimentation can start at $20 and scale from there. The real cost that grows with scale is data consumption, not initial infrastructure.

Audience Q&A

Q (Nishant Kanda, United Airlines): What should be the core parameters for AI readiness from a decision-maker's lens, and how is security addressed?

Dharmender Kapoor: The 35-parameter AI readiness framework begins with decisions, not systems. One useful starting point: process quality audit. Most companies discover that the processes they believe they run diverge significantly from those they actually run — exceptions accumulate silently for years while systems remain static. AI readiness means surfacing these gaps and defining which decisions can be made probabilistically rather than sequentially.

Dr. Sudhin Baraokar: Security in AI implementations is actually stronger than in legacy IT, because guardrails can be built from Day 0. Explainability tools (e.g., SHAP — Shapley Additive Explanations) allow models to be audited by regulators, with bias and outlier analysis baked in. Responsible AI frameworks such as India's DPDP 2023 and GDPR can be embedded directly into model governance. The key is establishing company security policies, risk management policies, and stakeholder agreements with the CRO and legal team before deployment begins.

Q (Vijay Sabarwal, Ex President - OCCL): Would human bias affect AI implementation, and how is neutral decision-making maintained?

Dharmender Kapoor: Human bias is real and well-documented — a US bank example showed AI offering loans preferentially to certain demographics because human loan officers historically had the same bias, which was encoded in the training data. Two types of drift must be continuously monitored: data drift (new biases emerging as data accumulates) and contextual/logic drift (business expansion into new geographies or products changing the decision environment). Regular data and logic audits, timed by measurable quality degradation in model outputs, are essential. Agents must be refined continuously, not treated as one-time implementations.

Q (Rajiv Upadhyay, Founder, Gravitas Academy): Most AI programs invest in process redesign and upskilling, but few audit leadership belief systems. What does genuine leadership readiness look like when the real bottleneck is conviction, not capability?

Dharmender Kapoor: Leadership readiness precedes everything else. The recommended framework is a SNACK analysis — Stakeholders, Needs, Alterables, Knowledge (context), and constraints (time, money, talent). This must be mapped and presented to the leadership team before any AI program begins, because technology can enable everything, but if the business leadership does not own the data, context, and decision processes, last-mile execution will always fail. 100% commitment from the top is not optional — it is the prerequisite.

Q (Ravi Shankar, Zoworks.ai): What are the product opportunities — not just services — in manufacturing and construction?

Dr. Sudhin Baraokar: Manufacturing offers a full spectrum from mid-market to large enterprise: predictive and prescriptive maintenance, autonomic systems integrated with existing automation layers, quality inspection, and neural network operating systems. Construction opportunities include scheduling optimization, project timeline acceleration, smart material estimation, and what-if analysis for capacity scaling. The principle: do not use AI to replace Excel — use AI for massive, autonomic innovation.

Dharmender Kapoor: For product companies, the 2×2 decision complexity vs. system capability matrix is equally applicable. If your product solves problems in the top-right quadrant — high decision complexity, low current system capability — there is no reason opportunities won't materialize. Clients will adopt because they can simplify decisions, improve productivity, reduce costs, and increase throughput.

Q: Should industrial companies consider build vs. buy for AI-native models? Why partner with AI-first startups rather than defaulting to large IT vendors?

Dr. Sudhin Baraokar: Partner with the entire ecosystem. Large organizations under-leverage their existing partner networks. The model is "run the organization + change the organization" in parallel — mission-critical systems maintained while deep tech accelerates transformation. India's particular strength is application-layer digital transformation, not model building. Use global models (LLMs), customize them for Indian industrial contexts, and build SLMs for depth. That is India's competitive play.

Dharmender Kapoor: AI-first startups have a structural advantage that mirrors what born-digital companies had over incumbents during e-commerce disruption. Large players have deep pockets but carry legacy baggage — existing clients, revenue models to cannibalize, slow organizational movement. AI-native companies move fast, solve one specific problem deeply for one or two clients, and then scale that solution across an ecosystem. That is the window. The new-age companies that emerge from this cycle will be as significant as the digital natives of the previous cycle.

Final Takeaways

Vishwendra Verma's Closing Checkpoints for Builders:

Define the outcome before you define the model — clarity on business impact precedes architecture decisions.
Your architecture and data are a product — treat them accordingly.
Do not go to production until you can explain every output — explainability is a prerequisite, not a retrofit.
Pilot scope and production scope are not the same problem — design for both distinctly.
Price for outcomes, not for access — the SaaS subscription model is the problem, not the solution.

Key Success Principles: Decision-backward readiness assessment, SLM + LLM hybrid architecture, SNACK leadership alignment, domain-context differentiation, continuous drift monitoring, and ecosystem partnership over build-alone approaches.

Watch The Replay

This Q&A is based on GrowthSutra's "The SaaS Apocalypse & Industrial AI — From Wrappers to Real Intelligence | 202X Vision" session.

The full replay is available on GrowthSutra's LinkedIn and YouTube channel .

The next session in the 202X Vision series is scheduled on 23rd April 2026 — "AI Rewiring Enterprise Procurement: Who Really Wins — Buyers, Sellers, or the Platforms?"

Register here.