Gartner ThinkCast

Most AI initiatives don't fail because the technology is wrong; they fail because the humans around it stop thinking as carefully as they used to.
In this episode of ThinkCast, recorded from a live session at Gartner CIO Leadership Forum, Gartner Distinguished VP Analyst Mandi Bishop breaks down why so‑called "soft skills" have quietly become the hardest — and most essential — capability to scale in the age of AI. From practical workforce exercises to Mandi's own racing experience, hear how over‑reliance, complacency and automation bias creep in — and how CIOs can rebuild critical thinking, judgment and contextual awareness to turn AI tools into true performance accelerators.



You'll learn:
Why automation bias is one of the biggest hidden risks in AI adoption
How soft skills like skepticism and judgment directly impact AI outcomes
Why hands‑on techniques, like hallucination hunts, can build real AI literacy
How better prompting reinforces human thinking instead of replacing it
What CIOs can do now to operationalize soft skills across the enterprise



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AI can answer faster, personalize at scale, and automate entire customer journeys, but it can't make someone feel seen.
In this episode of ThinkCast, recorded from a live session at Gartner CIO Leadership Forum, Gartner Distinguished VP Analyst Don Scheibenreif explores why customer experience breaks down when efficiency overtakes empathy — and what CIOs can do to keep humanity at the center of AI‑driven design. Through real‑world stories from travel, retail, financial services and the public sector, Don shows how "moments of humanity" shape trust, loyalty and long‑term growth.

 
You'll learn:
What happens when organizations over‑automate customer relationships
How to recognize and design for moments that truly matter
Where empathy fits into AI‑driven efficiency efforts
Why CX challenges often stem from design decisions, not technology limits
How CIOs can ensure AI enhances experiences without dehumanizing them
 
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In this episode of ThinkCast, we explore one of the most persistent — and evolving — challenges with AI: hallucinations.
Gartner Chief of Research Chris Howard breaks down what hallucinations really are, why they happen and what they reveal about how machines think. From multiagent systems to physics-informed neural networks, discover how the next wave of AI innovation is moving beyond prediction and toward precision, all while relying on AI-ready data.


You'll learn:
Why hallucinations happen and what they say about how AI works
How multiagent systems and PINNs are changing how machines reason
When to use probabilistic neural networks vs. deterministic tools
Why not every business problem needs AI
How hallucinations might actually help reframe your thinking


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Most organizations don't have an AI problem — they have a human readiness problem.
Despite massive investment and accelerating adoption, most AI initiatives are falling short of real business impact. The reason isn't immature technology. It's that workforces, roles and leaders haven't been prepared for what AI actually changes.
In this episode, Gartner VP Analyst Alicia Mullery explains why the gap between AI innovation and AI outcomes continues to widen. In a preview of her recent webinar on workforce readiness, you'll hear what's really happening: not mass layoffs, but hiring restraint, role redesign, and a strategic remix of talent — all of which demand far more change management than most organizations anticipate.
 
You'll learn:
Why most AI initiatives stall at productivity gains instead of delivering financial  ROI
The three types of AI value and how confusion between them derails results
What's actually happening to jobs as AI adoption accelerates
The essential skills every employee needs in an AI‑dominant future
Why change management is the missing linchpin for sustained AI impact
 
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See how Gartner is the world authority on AI

How should software engineering evolve for the age of AI? As organizations rush to weave GenAI and agentic AI into every product and workflow, engineering teams face a new reality: you can't scale AI without redesigning how software gets built.
 
In this episode, Gartner experts Manjunath Bhat, Akis Sklavounakis and Shameen Pillai break down what that redesign actually looks like. They'll explore the team structures that help scale GenAI to repeatable delivery, the platform patterns that reduce cognitive load and drive value, and the API strategies that AI implementation can't function without.
 
You'll learn:
Why GenAI efforts fail without the right team structures
How platform engineering reduces complexity and accelerates delivery
Why APIs are the backbone of GenAI and agentic AI
The five‑dimension API maturity model and where to focus first
The tools and next steps to scale AI safely and effectively
 
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