Generative AI for Enterprise: Where to Start (Without Wasting Money)
Most enterprise AI pilots fail not because the technology doesn't work — but because companies start in the wrong place. Here's a practical framework for getting your first generative AI project right.
The AI Pilot Graveyard
Across Europe and North America, enterprise AI pilot programmes are being quietly shelved. The technology worked — the demo was impressive — but the business impact never materialised. The initiative ends up in what Gartner euphemistically calls the "trough of disillusionment."
This happens with predictable regularity for predictable reasons: the wrong use case was chosen, the data wasn't ready, the process wasn't well-defined, or the organisation wasn't prepared to change how it worked to accommodate the AI output.
Getting generative AI right in an enterprise context requires a framework, not just a model.
The Four Criteria for a Good First Use Case
The best first AI use cases share four characteristics:
High frequency: The task happens many times per day or week. AI delivers the most ROI where it can eliminate repetitive cognitive load — not one-off complex decisions.
Well-defined inputs and outputs: Someone can clearly specify what information goes in and what a good output looks like. Vague tasks with subjective success criteria make evaluation impossible.
Recoverable mistakes: The cost of an AI error is low — a human can catch and correct it before it causes harm. Do not start with AI making autonomous decisions in high-stakes irreversible situations.
Measurable baseline: You know how long the task currently takes and how much it costs. Without a baseline, you cannot measure ROI — and without ROI, you cannot fund expansion.
Good Starting Points by Department
Legal: Contract review and summarisation. LLMs are excellent at extracting key terms, flagging unusual clauses, and producing concise summaries. The human still approves — but does so in minutes instead of hours.
HR: Policy Q&A. Build a RAG-powered chatbot over your HR documentation with Elephandroid. Employees get instant answers to policy questions; HR teams spend less time on repetitive queries.
Sales: Meeting summarisation and follow-up drafting. After every customer call, AI produces a structured summary and draft follow-up email. Saves 20-30 minutes per call.
Finance: Expense categorisation and anomaly detection. AI classifies expenses, flags policy violations, and identifies unusual patterns — reducing manual review time significantly. Faturacim handles this natively through WhatsApp.
Operations: Report generation from structured data. AI converts operational metrics into narrative reports — weekly updates, exception reports, management summaries.
The Build vs Buy Decision
For most organisations, building a custom LLM from scratch is unnecessary and uneconomical. The right question is whether to: use an off-the-shelf AI product (fast, limited customisation), build on top of a foundation model API (flexible, requires technical capability), or commission a custom implementation from an AI consulting firm (highest customisation, fastest time-to-value for non-technical organisations).
The decision depends on how unique your requirements are and what internal AI engineering capability you have. For domain-specific use cases — specialist compliance with GreenPact, niche manufacturing processes with Punch, proprietary knowledge bases with Elephandroid — purpose-built solutions consistently outperform generic products.
Our Approach at CF Innovation Labs
We start every engagement with a use case discovery session — mapping your operational processes against the four criteria above, identifying the highest-value starting point, and designing an implementation that delivers measurable results within 90 days.
If you are ready to move beyond the AI hype and into production, book a discovery call. We'll tell you honestly what's achievable and what isn't.
Ready to explore AI for your organisation?