LLMs for Business: A Decision-Maker's Guide

Key Takeaways

Identifying High-ROI Use Cases

The highest-ROI AI use cases share common characteristics: high frequency (many repetitions per week), significant human time cost (tasks that take 30+ minutes), relatively standardized inputs and outputs (consistent enough to prompt reliably), and verifiable outputs (you can check whether the AI did it correctly). Document workflows, customer support, and content creation usually fit this profile. The most transformative applications are Agent-based: autonomous systems that call tools, retrieve information via RAG, and complete multi-step tasks with minimal human intervention — turning a Foundation Model into a business process rather than a chat window.

Use case evaluation matrix: Score each candidate use case on frequency, time savings per instance, quality verifiability, technical feasibility, and risk if the AI is wrong. Multiply these scores to get a ROI priority ranking. Start with the highest-scoring, lowest-risk use cases — early wins build organizational confidence for more ambitious applications.

Build vs Buy: The Right Framework

Buy first. Most business AI use cases are well-served by existing products — Copilot for Office productivity, Intercom/Zendesk AI for customer support, GitHub Copilot for engineering. Building custom AI systems requires significant engineering investment, ongoing maintenance, and careful quality management. The build case is strongest when: the use case is proprietary to your business, existing products don't cover it, or you need tight data governance.

When building: evaluate whether you're building 'on top of AI' (API integration) or 'AI infrastructure' (model training, fine-tuning, serving). Most businesses should only do the former. Training your own models is appropriate only for very large-scale, proprietary applications where competitive advantage from customization outweighs the significant investment.

AI Governance and Risk Management

AI governance starts with accountability: who is responsible for each AI system's outputs? AI-generated content that is published, acted upon, or used to make decisions is ultimately the responsibility of the organization, not the AI vendor. Establish clear accountability, review processes, and escalation paths for AI outputs in high-stakes contexts. Pay particular attention to Hallucination risk — models can produce confident, fluent, but factually wrong outputs. Use Grounding via RAG and require human review wherever factual accuracy is legally or financially material.

Key governance documents: an AI use policy defining where AI is permitted and prohibited, a data classification policy determining what data can be processed by external AI systems, a quality review process for AI outputs in high-stakes applications, and an incident response procedure for AI errors that reach customers or create liability.

Change Management for AI Adoption

Technology rarely fails from technical reasons — it fails because organizations don't adopt it. AI is no different. The resistance to AI tools often reflects legitimate concerns (job security, quality accountability, the learning curve of new workflows) that require explicit address, not dismissal.

Effective AI change management: involve potential users early in use case selection and tool evaluation, address job security concerns directly and honestly, invest in training that focuses on how AI augments skills rather than replacing them, celebrate early adopters, and measure and communicate productivity improvements that result from AI adoption. Organizations where management uses AI tools themselves see dramatically higher team adoption.