Skip to main content
Paul Welty, PhD AI, WORK, AND STAYING HUMAN

· artificial-intelligence

Bookmark: The roadmap to AI ROI for enterprises

Discover how to maximize AI ROI with strategic metrics that drive productivity, efficiency, and customer satisfaction for your enterprise.

The article “The Roadmap to AI ROI for Enterprises” examines the increasing expectations businesses have for artificial intelligence (AI) return on investment (ROI) and the metrics used to measure it. The piece explores how at least 30% of generative AI initiatives might be discontinued post the concept proof phase, yet a significant proportion of leaders deploying AI report ROI in operational efficiencies, productivity, and customer satisfaction. The article discusses various AI ROI metrics, emphasizing productivity, operational efficiency, and customer satisfaction, alongside financial measures like revenue. Examples include enhanced code development for engineers and reduced recruitment times through AI in HR. It emphasizes the strategic importance of defining ROI metrics and integrating AI into core operations, with AI acting not just as technology but as a strategic instrument. The discussion also covers the timeline expectations for ROI from AI deployments, suggesting initial returns might be visible within three to six months and greater impacts as data accumulates and AI technology matures. A core argument is that without proven ROI, AI investments risk being deemed as costly ventures without value, underscoring the need for consistent evaluation and alignment of AI outcomes with business-critical objectives The Roadmap to AI ROI for Enterprises

The agent-shaped org chart

Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.

AI as staff, not software

Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren't the same product, the same company, or the same industry.

Knowledge work was never work

Knowledge work was always coordination between humans who couldn't share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.

The work of being available now

A book on AI, judgment, and staying human at work.

The practice of work in progress

Practical essays on how work actually gets done.

How do I get my dev team to adopt AI?

A stub on helping mixed-interest development teams find their own useful ways into AI.

Want to learn about agents? Talk to someone who ran an agency.

I spent 20 years running consulting engagements at Fortune 500 companies. Turns out that's the best preparation for running a fleet of AI agents ... because the problems are identical.

Your AI agents need a water cooler

We run a twelve-session AI fleet that coordinates through an IRC breakroom. A friend asked: why are you making AI agents act like humans? The answer turned out to be more interesting than the question.

Article analysis: Computer use (beta)

Explore the capabilities and limitations of Claude 3.5 Sonnet's computer use features, and learn how to optimize performance effectively.

Bookmark: From proof of concept to production: Embracing systems thinking

Transform your AI strategy with a systems-thinking approach, ensuring seamless transition from proof of concept to impactful production deployment.

Bookmark: Workers who use AI are more productive at work—But less happy, research finds

AI boosts workplace productivity but may diminish creativity and job satisfaction. Explore the paradox of efficiency versus fulfillment in this insightful...