Human + AI Impact (H+AIII) is a practical podcast about redesigning work for the AI era—moving beyond counting AI activity and toward tracking where value actually moves.
Each episode unpacks the patterns, metrics, and operating decisions that make Human+AI collaboration work. Expect sharp stories, field-tested frameworks and “ready to pilot” ideas—from LLMs to agentic systems where humans do the judging and AI does the heavy lifting.
When Productivity Doesn't Switch Off: The Hidden Cost of AI Flow (with Anthony Cannon)
AI is making work faster, more creative but harder to stop.
In this episode of Human+AI Impact, Dr. Sabrina Anjara is joined by Anthony Cannon to explore a less visible side of AI productivity: the rise of “always-on” work. As AI collapses the gap between idea and execution, it creates powerful flow states that can drive output, but also extend effort beyond sustainable limits.
They unpack what this new pattern of work feels like in practice, why traditional signs of burnout don’t apply, and how leaders need to rethink productivity design. Sabrina proposes stopping rules, flow boundaries, and new signals of over-engagement. Because the next challenge in AI-enabled work isn’t just performance, it’s knowing when to stop.
10 Mar 2026
From Access to Fluency: Why Silence is Not Adoption (With Rachel Earley)
Most organizations measure AI adoption by access: licenses issued, seats activated, usage frequency. But access is not fluency.
In this episode of Human+AI Impact, Dr. Sabrina Anjara and Rachel Earley unpack what real AI fluency looks like in practice. Drawing on Human+AI Impact research, they explore why high-fluency users iterate rather than accept first drafts, why polished outputs reduce scrutiny and why the loudest users may actually be the most advanced.
If your AI rollout feels quiet, that may not be success. Fluency shows up in behaviour — and behaviour is shaped by design.
25 Feb 2026
Responsible Agentic AI: Turning Governance into a Performance Multiplier (with Patrick Connolly)
Is Responsible AI slowing teams down—or is it just stuck in the wrong place? In this episode, Dr. Sabrina Anjara talks with Patrick Connolly about why governance must evolve for agentic AI systems. They explore how shifting governance into pipelines and runtime operations transforms it from a compliance bottleneck into a performance enabler. From AgentOps and real-time controls to trust as a measurable throughput signal, this episode shows how responsible AI becomes the infrastructure that lets organizations scale faster—and safer.
18 Feb 2026
Rethinking What We Measure in the Age of AI (with Darragh Miller)
AI is generating more ideas, options, and outputs than ever before—so why are so many organizations capturing less value? In this episode, Dr. Sabrina Anjara speaks with Darragh Miller about the AI surplus paradox and why traditional KPIs fail in a world of recursive, learning systems. They unpack how static dashboards hide end-to-end value, why measurement must become a living system, and how leaders can reconcile competing CEO, CFO, CIO, and CHRO value lenses. A deep dive into why measurement is now strategic—and moral.
11 Feb 2026
Quality Over Speed: Rethinking Productivity with AI (with Dr. Manaswi Saha)
AI can make work faster—but does it make it better? In this episode, Dr. Sabrina Anjara is joined by Dr. Manaswi Saha to challenge the idea that speed equals productivity in knowledge work. They explore why “just add human oversight” often fails, how misdesigned collaboration patterns quietly scale risk, and what it really means to measure human–AI collaboration quality. From agency and cognitive effort to joint utility and trust over time, this conversation reframes productivity as something you design—not something you accelerate blindly.
9 Feb 2026
Measuring Productivity in an AI world (with Fergus Gleeson)
AI is everywhere—but productivity gains are harder to find. In this episode, Dr. Sabrina Anjara sits down with Fergus Gleeson to unpack why many organizations are stuck in “productivity theater”: celebrating time saved, tickets closed, and bots deployed while outcomes stay flat. Together, they explore why productivity in the AI era must be measured at the level of the human+AI workflow, not individual effort; why decision latency beats busywork as a metric; and why reinvesting saved time—with clear ownership—is the real unlock for value. A practical reset of the AI productivity scoreboard.
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