70% of enterprise AI pilots never reach production
That number from McKinsey should give every CTO pause. Companies spend $500K on an AI proof-of-concept, it works in a demo, and then it sits in a repository because nobody figured out how to integrate it into actual business workflows.
The problem isn't the technology. GPT-4, Claude, Gemini — the models are good enough. The problem is that companies start with "we need an AI strategy" instead of "we have a specific problem that AI might solve."
Where GenAI delivers measurable ROI
After building AI systems for financial services, legal tech, and government clients, we see clear patterns in what works.
Document processing and analysis
Any business drowning in documents is a good candidate. Legal contract review, insurance claims processing, regulatory compliance checking. A human reviewer reads 30-50 pages per hour. An AI system processes 500 pages per hour with accuracy that matches senior reviewers for routine classifications.
Our Psika.ai system for legal precedent research reduced lawyer research time from 4 hours to 20 minutes per case. That's not a theoretical improvement. It's measured across thousands of queries with real law firms.
Customer service augmentation
Not chatbots that frustrate users, but AI that helps human agents respond faster. The agent sees a customer question, the AI suggests a response based on knowledge base articles and previous successful resolutions, the agent reviews and sends. Response time drops 40%, consistency improves, new agents ramp up in days instead of weeks.
Internal knowledge management
Every enterprise has critical knowledge trapped in Confluence pages, Slack threads, and departing employees' heads. RAG (retrieval-augmented generation) systems that index internal docs and answer employee questions in natural language deliver immediate value. The ROI is difficult to measure precisely but the productivity gains are obvious.
Code review and development acceleration
AI code review catches logic errors, security issues, and style inconsistencies that human reviewers miss when tired. We use AI review as a first pass — it handles the systematic checks, humans focus on architectural and business logic review. Reviews that took 2 hours now take 30 minutes.
Where GenAI wastes money
Generic chatbots. If your AI chatbot just wraps GPT with your website content, you've built a worse search engine.
Autonomous decision-making. For any decision with regulatory, financial, or safety implications, AI should prepare options for human decision-makers. Full automation sounds impressive in board presentations but creates liability.
Marketing content generation. Generating 100 blog posts with AI is easy. Generating 100 blog posts that customers want to read is hard. AI-generated content performs poorly in search (Google knows) and fails to build brand trust.
How to structure an AI project for success
Start with a workflow, not a technology. Identify a specific process where humans spend hours on tasks that follow patterns. Map the workflow. Then ask: which steps benefit from AI?
Measure the baseline first. Before building anything, measure current performance. How long does the task take? What's the error rate? Track these numbers so you can prove ROI after deployment.
Build a thin integration layer. Don't lock into one model provider. Build your system so the AI model is a replaceable component. When a better model arrives (and it will), you swap one module, not your entire system.
Plan for humans in the loop. Every AI output should be reviewable by a human. Confidence scores determine which outputs need review. High-confidence outputs pass through; low-confidence outputs get flagged. This approach delivers 95%+ accuracy while maintaining speed.
Frequently asked questions
What's a realistic budget for an enterprise AI project? Pilot: $100K-200K over 3 months. Production deployment: $300K-600K over 6 months. This includes infrastructure, model costs, and integration work.
Which model should we use? For most enterprise use cases, the choice between GPT-4, Claude, and Gemini matters less than your prompt engineering and data pipeline. Pick one, build well, and swap later if needed.
How do we handle data privacy with AI models? Use API endpoints (not consumer products), implement data processing agreements with providers, and consider self-hosted models for sensitive data. Azure OpenAI Service and AWS Bedrock keep your data within your cloud boundary.
Ready to find where AI delivers real value in your business? Talk to our AI consulting team.