The Future of Automation (Led by AI) — And What Still Gets in the Way
Based on a Startup Grind talk by Ishan Varshney and Prakhar Sinha
Title from their deck: “The Future of Automation Transformations Led by AI and What Stands in the Way.”
The “Oh-No” Audit: Why RAG Exists
A surprise compliance audit lands on your desk. Answers are scattered across PDFs, wikis, and “final_final_v7” folders. You need facts, fast, and you’ll be judged on accuracy—not creativity.
Retrieval-Augmented Generation (RAG) is built for this moment. It:
Grounds responses in your real sources (and cites them).
Pulls the latest versions when your content changes.
Cuts down hallucinations and human error.
Bridges static model training with dynamic real-world knowledge.
Under the hood: A retriever scans your knowledge base (and optional web sources), ranks relevant passages, and the model generates a concise answer with the receipts. If your AI can’t point to where it found the claim, it’s not helping—it’s guessing.
What’s New in RAG (And Why You Should Care)
Hybrid retrieval: Blends semantic + keyword search to boost recall and precision.
Context optimization: Re-rankers and dynamic chunking improve what the model “sees,” so you get fewer misses and better answers.
Enterprise adoption: Finance, healthcare, and customer support are already shipping this, often inside copilots and assistants.
Multimodal RAG: Text today, code/images/doc layouts tomorrow. Welcome to richer answers.
Data Extraction: From Grunt Work to Agentic AI
Remember when “automation” meant a frazzled analyst copy-pasting rows at 11:58 p.m.? Same goal, different century.
Then: Manual eyes-on-paper → early OCR (e.g., Tesseract) → lots of brittle scripts.
Now: AI-powered, agentic extraction that understands structure—headers, nested tables, weird PDFs—and maps values into predictable JSON your systems can trust.
Why it matters:
Speed & scale: Chews through mountains of invoices, receipts, payroll, inventory.
Consistency: Models don’t have “off days.”
Sanity: Keeps people on judgment work instead of cell-hunting in spreadsheets.
Traditional vs. Agentic Extraction (The Real Tradeoffs)
Traditional: Slow, error-prone, brittle when formats shift.
Agentic AI: Faster, more consistent, schema-aware, and easier to scale—with a crucial caveat: you must govern it.
The Unsexy (But Critical) Challenges
Data privacy & security: Use isolated/tenant setups and contracts that prohibit model training on your data. Minimize/strip PII by default.
Trust & explainability: Stakeholders need to see why a value was extracted. Keep confidence scores, lineage, and audit trails.
Human-in-the-loop: Route low-confidence or high-impact fields (payroll, benefits, clinical) for review. AI at scale, humans on the knife-edge decisions.
Bottom line: In regulated domains, “pretty good” accuracy is not good enough. Aim for 95–98%+ where it counts—and prove it.
Connectors vs. Custom Pipelines (Choose Like an Adult)
End-user connectors (Drive, Slack, CRM) are great when your data is already neat and non-sensitive. But if you’ve got legacy databases, mixed formats, or policy constraints, you’ll want:
a cleaning/redaction layer,
an agentic extraction layer to normalize concepts (e.g., “Percent/Proportion/Fraction” →
percentage_value), anda RAG layer that answers with citations against versioned sources.
The Near Future (Grocery Store Edition)
In the picture Ishan and Prakhar painted: invoices, inventory, and payroll flow into structured stores automatically; RAG answers policy and ops questions with citations; staff do less drudgery and more real work. Burnout down. Accuracy up. Business better. That scales from grocers to banks to clinics.
A Pragmatic Rollout Checklist
Inventory sources (PDFs, scans, exports) and label sensitivity.
Define target JSON (fields, types, validators, confidence thresholds).
Bench two or three extraction tools on your files; measure OCR quality and structure fidelity.
Add the agentic layer (synonym mapping, schema normalization, auto-routing to human review).
Stand up RAG on a versioned knowledge base; require citations.
Governance (isolation, contracts, PII hygiene, logs, retention).
Metrics (accuracy, coverage, human review rate, cycle time saved).
Final Word
If your AI can’t say what it pulled, where it pulled it from, and why it trusted it—don’t deploy it. Auditors won’t buy “vibes.” Neither should you.
