David Pidsley is a British decision intelligence (DI) leader bridging business and technology insights in analytics and AI. 

“The data-driven dogma is dead. You’ve got to make a decision. Evolve a decision-centric vision.”

He joined Gartner in 2020 and has 20 years experience advising on business technology transformations. Before that, he was Chief Technology Officer (CTO) for Analytics at system integrator DXC Technology, having previously held three Head of Data, Analytics and BI roles and was founder of content intelligence company Cause Analytics, and earlier headed marketing strategy for UNIT4.

Business technology is a path to a more agile, innovative and productive future. The teams I’ve been asked to advise have pioneered profitable projects by bridging the gap between Business: clients, teams, shareholders, and Technology: products, computing and cognition. In over twenty years advising businesses from startup to market impact, I’ve learnt we succeed by being the bridge.

AI agents are the top technology trend for C-suite executives. Data and analytics leaders must plan for their impact on analytics, business intelligence and data science processes, people and platforms. 

🏆 I am honored today to have received Gartner's 'Research of the Month' award, presented by Afraz Jaffri, Senior Director of Analytics & AI, Business & Technology Insights, awarded for research excellence for my publication: "How AI Agents Will Impact Analytics and Data Science" (24 October 2025) Gartner clients can login now to read it in full: https://lnkd.in/e5aW3Nrx [ID G00838757]

🙏 I am grateful to my Gartner colleagues and coauthors for their support, especially Neil Chandler, Kjell Carlsson, Rita Sallam, Yogesh Bhatt, Diarmuid Curran, Andrés García-Rodeja, Anirudh Ganeshan, Georgia O'Callaghan, Leinar Ramos.

#Data #Analytics #GartnerDA #AIAgents #AgenticAI #BusinessIntelligence #DataScience #AgenticAnalytics

David Pidsley

18 years advising business technology transformations. British Decision Intelligence Advisor bridging technology and business using AI. Business technology is a path to a more agile, innovative and productive future. The teams I’ve been asked to advise have pioneered profitable projects by bridging the gap between Business: clients, teams, shareholders, and Technology: products, computing and cognition. A recent email from a client read, “David has enough professional willpower to transform our whole business with technology alone, but he takes an extraordinary interest in listening to our people and learning from new data.” In over seventeen years advising businesses from startup to market impact, I’ve learnt we succeed by being the bridge.

Gartner

AI Industry Analyst @ Gartner | Higher Ed Board Member

Managing VP, Business Analytics & Data Science, Gartner

Gartner VP analyst covering AI, data science and advanced analytics

Chief of Research, Data and Analytics

Senior Director Agentic AI, Generative AI, AI Governance, GraphRAG and Analytics

Analytics and AI Industry Research | Senior Data Scientist

Data, Analytics & AI Strategy | Knowledge Engineering | GenAI | Agentic AI | Data and Analytics as a Product | DSML & AI Agent Platforms

Senior Principal Analyst @ Gartner | Analytics & Artificial Intelligence

Director Analyst, Analytics & AI for Technical Professionals at Gartner

VP Analyst | AI at Gartner

Decision Intelligence Leader | Gartner

Sabrina Schultz

Gartner’s Market Guide for Decision Intelligence Platforms (published 18 July 2024) is still in the top twenty most read market guides (as of the week ending 7 November 2025). #DecisionIntelligence

Enterprise AI teams are struggle with agents that cannot reliably access and act on core enterprise systems and data, lack deep understanding of domain‑specific language and context, and sit on top of weak retrieval and grounding over internal knowledge, which leads to brittle answers and hallucinations.

At the same time, organizations have poor observability, control and evaluation of AI behaviour, with limited guardrails, benchmarking and testing and they find it hard to compose multiple agents and tools into robust workflows, so initiatives stall in “pilot purgatory” instead of scaling into production.

Part of the solution to these challenges is integrating domain‑aware models with enterprise systems and adding strong retrieval, monitoring, guardrails and orchestration for multi‑agent workflows.

AI agents and GenAI solutions will augment and automate an increasing share of analytics. To unlock adoption and value, data and analytics leaders must deploy new AI protocols, strengthen semantic layers, guardrail human biases, implement interpretability and start shifting AI governance responsibilities. Looking towards 2026, Gartner predicts that:

🔵 Agentic analytics is changing how users consume and analyze data. 
🔵 AI protocols trigger a new analytics architecture. 
🔵 Human bias and AI sycophancy become a major AI risk.
🔵 New AI interpretability drives performance and adoption.
🔵 AI governance joins design and engineering.

I'm pleased to have coauthored Gartner's latest "Predicts 2026: AI Agents, MCP and Governance Are Transforming Analytics" published 19 November 2025 (ID G00841144) led by my colleague Kjell Carlsson, with Edgar Macari, Joe Antelmi, Diarmuid Curran, Anirudh Ganeshan, Andrés García-Rodeja, Lauren Kornutick, Alissa Lugo, David Pidsley.

Gartner clients can login and access it in full here: https://lnkd.in/eu45gJJU

#AIAgent #AgenticAnalytics #GenAI #AgenticAI #DecisonIntelligence #SemanticLayer #AIGovernance

Agentic analytics has the potential to improve analytics effectiveness and accelerate adoption. #Data and #analytics leaders should assess adoption readiness to identify areas where they can achieve reliable, impactful results. 

I'm pleased to have contributed to Gartner's latest research, "How D&A Leaders Can Prepare Their Organization for Agentic Analytics" published 11 November 2025 by Jeroen Cornelissen and Afraz Jaffri which clients can login and read here: https://lnkd.in/eMJm_ASs #AgenticAnalytics

A survey of 800 senior business leaders found nearly every company (97%) experimenting with AI, but only 20% making real-time decisions, just 22% having a defined AI strategy, and those with one being 3X as likely to see measurable ROI. Source: Supply Chain’s Big Bet on AI For Geopolitical Resilience (by Economist Impact)

Since last year, I have been researching how AI agents impact data and analytics within large organizations based on how I see each client evolving into an autonomous business.

I have come to believe that rapid AI agent adoption will overwhelm data and analytics operations built by and for humans.

Data and analytics leaders must adapt their strategic vision, stakeholder objectives, operating models and technology architecture to meet machine demand for insights to enable an autonomous business transformation.

1️⃣ Revise their strategy
2️⃣ Modernize their operating model
3️⃣ Adopt composable architecture

Gartner clients can now login to read:

AI Agent Adoption Transforms Data and Analytics Strategy and Operations
https://lnkd.in/e5zHy2vr

Are you working on agentic data and analytics? What do you think of autonomous business as the next wave? Tell me in the comments.

These new business technology insights were published (23 October | G00832111) with contributions from my colleagues: Melissa Davis, Don Scheibenreif and others.

Not yet a Gartner client? Checkout these insights about how Autonomous Business Is Coming, Powered by AI: https://lnkd.in/emF3DESi

#AgenticDA #AgenticAnalytics #AIAgent #AutonomousBusiness #MachineCustomer

GenAI provides new opportunities for the misuse of data, prompts or analysis to make and justify poor decisions.

Chief Scientist and Cofounder Quantellia | Cofounder opendi.org . 37 years Machine Learning deliveries, 15 years delivering Decision Intelligence. Strong opinions, weakly held.

What David Pidsley says here about the danger of ungoverned LLM-only decisions is not widely understood but absolutely true.  

AI's past has largely focused on "one tool at a time" (one-hidden-layer networks fail XOR, so throw away neural networks and switch to logic.  Expert systems don't widely succeed, so throw all symbolic approaches away and switch to deep learning.  GenAI does amazing things, so rename "AI" to mean just genAI and ignore 70 years of history...and so forth...).  

The truth: smart people know that AI's future is a "toolbox", of which LLMs are just the latest shiny object.  Smart practitioners know the strengths and weaknesses of each tool and - as Gartner calls it - use a "composite" approach (which, btw, also includes operations research, behavioral economics, econometrics, business intelligence, randomized control trials, and more: disciplines not traditionally called "AI" but which absolutely belong in the same toolbox).  (PS Looking for a tech job?  Say this^)

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