Decision intelligence
Orchestrator
AI-powered prompt orchestration and decision intelligence center with multi-agent analysis, KPIs, and executive-ready briefings.
See it in the platformResources
Books, papers, and tools for getting enterprise AI past the pilot — and into the customer’s hands.
Everything I've published on agentic AI, decision intelligence, and the executive work of turning AI investment into decisions a business can act on. The published library leans to the AI practice; the customer experience half of the work shows up in the frameworks and tools below, and in the engagements on the About page.
Latest: The Decision Loop — Generate, Assess, Score, Prioritize · Previous paper
New · Coming soon
The Agentic Enterprise
From automation to autonomous decision intelligence.
The enterprise no longer competes on what it automates. It competes on how it decides.
A field manual for leaders building enterprises where AI is no longer a feature inside a workflow, but the decision-making fabric that spans the whole business — sensing, reasoning, prioritizing, and acting continuously. The bottleneck is no longer information. It is the decision-making process itself.
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01
Continuous Decisioning
From periodic planning → always-on intelligence
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02
Autonomous Orchestration
From human coordination → agent-driven execution
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03
Dynamic Prioritization
From static ranking → adaptive intelligence
- Intro · The End of Automation as We Know It
- 01 · What Is Agentic AI?
- 02 · The Evolution of Enterprise AI
- 03 · Defining the Agentic Enterprise
- 04 · Agentic Architecture Blueprint
- 05 · The Role of Decision Intelligence
- 06 · The Agent Stack
- 07 · Data, Memory, and Feedback Loops — in progress
- Next · Operating model, strategy, implementation — in progress
Foreword by Bruce Cleveland, bestselling author of Traversing the Traction Gap and Market Engineering: Because Markets Don't Build Themselves.
Written for CEOs, COOs, and boards · CIOs, CTOs, and CDAOs · strategy and transformation leads · product and engineering leaders
Published · First edition 2024
Illuminating Pathways
Navigating the enterprise ecosystem through decision intelligence and generative AI, focused on prioritization.
“Illuminating Pathways” is required reading for any business executive who wants their company to remain viable. Bruce Cleveland · from the foreword
Business intelligence gave executives hindsight — charts and reports about what already happened, with a human left to find the opportunity buried inside them. Decision intelligence is built the other way around. This book is the working playbook for that shift: how generative AI assesses, scores, ranks, and prioritizes strategy initiatives, what infrastructure it takes to run, and where the ethical and implementation traps sit.
It is written to be jumped around in. Read it cover to cover, or go straight to the chapter, industry, or scorecard you need — with a glossary at the back for the terminology.
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SECTION 1
The Why
What decision intelligence changes, and where generative AI actually helps
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SECTION 2
The How
Foundations, infrastructure, applied decision-making, and industry-by-industry use
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SECTION 3
Realizing Generative AI
Starting the journey, readying the organization, and measuring whether it worked
- 01 · The Significance of Decision Intelligence in Enterprise Strategy
- 02 · Unleashing the Power of Generative AI in Decision Intelligence
- 03 · Assessing Strategy Initiatives with Generative AI
- 04 · Scoring and Ranking Strategy Initiatives
- 05 · Prioritization Strategies Enhanced by Generative AI
- 06 · Ethical Considerations in Generative AI-Driven Decision Intelligence
- 07 · Overcoming Implementation Challenges
- 08 · Imagining the Future: Decision Intelligence and Generative AI
- 09 · The Foundations of Enterprise Decision Intelligence
- 10 · Applying Generative AI in Business Decision-Making
- 11 · Building an Effective Generative AI Infrastructure
- 12 · Overcoming Challenges and Maximizing the Potential
- 13 · Future Directions and Emerging Trends
- 14 · Applying Generative AI Across Industries
- 15 · Starting Your Journey with Generative AI
- 16 · Preparing Your Organization for Deployment
- 17 · Readiness Assessments, Checklists, Scorecards, and KPIs
- 18 · Final
- Glossary of terms
Foreword by Bruce Cleveland — technology executive, venture capitalist, and author — on the arrival of decision intelligence as a software category built on a genuinely new stack.
Case studies across insurance, healthcare, pharmaceutical, ESG, financial services, CPG, and retail.
Published · First edition 2024
The Rise of Decision Intelligence
A retrospective and prospective view.
Enter Decision Intelligence — the next generation of decision-making frameworks designed to harness the power of artificial intelligence to optimize choices, strategies, and outcomes. From the introduction
Two views of the same discipline. Looking back: how enterprises got from 1960s batch reporting through data warehousing, OLAP, and the maturing of business intelligence — and why those systems, however sophisticated, could still only describe what had already happened. Looking forward: what changes when AI stops supporting decisions and starts driving them.
The turn between the two is traced to four developments running at once — the exponential growth of data across channels, cloud computing and big data, the demand for real-time decisions, and the proliferation of AI and machine learning. Decision intelligence is what gets built on top of that: proactive, context-aware, and answerable to an outcome rather than a dashboard.
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RETROSPECTIVE
How BI got here
From mainframe reporting to warehouses, OLAP, and the limits of descriptive insight
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THE TURN
Four forces
Data growth, cloud and big data, real-time demand, and the arrival of AI and ML
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PROSPECTIVE
What comes next
AI that drives decisions across business, government, healthcare, and beyond
- 01 · The Evolution of Business Intelligence
- 02 · Challenges with Traditional Business Intelligence
- 03 · The Emergence of Decision Intelligence
- 04 · Key Drivers Behind the Rise of Decision Intelligence
- 05 · The Role of AI in Decision Intelligence
- 06 · Decision Intelligence in Action: Use Cases Across Industries
- 07 · Transitioning from Business Intelligence to Decision Intelligence
- 08 · The Future of Decision Intelligence
- 09 · Embracing the New Era of Decision Intelligence
Opens with The Dawn of a New Decision-Making Era — decision-making traced from intuition and experience to systems that analyze, interpret, and recommend at a scale beyond human capacity.
Written for leaders making the transition in business, government, healthcare, and beyond.
Published · First edition 2024
The Future of Generative AI
Creating tomorrow: the power of generative AI unleashed.
Unlike traditional AI, which typically processes and analyzes data to provide insights, generative AI creates — whether it’s text, images, music, designs, or even entirely new ideas. From the introduction
The distinction the book is built on: systems that analyze versus systems that produce. It traces generative AI’s evolving capabilities, its impact sector by sector, the ethical questions that arrive with it, and what the road ahead actually looks like.
The lineage runs long. Turing’s 1950 test and the rule-based systems that followed; neural networks resurfacing in the 1980s against the limits of the hardware; the 1990s shift to machine learning, unsupervised techniques, and deep learning; then the decade that made generation itself possible — variational autoencoders in 2013, Goodfellow’s generative adversarial networks in 2014, and the transformer architecture in 2017 that made large-scale models practical.
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ORIGINS
How it was built
From the Turing Test and symbolic reasoning to neural networks and the machine-learning turn
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BREAKTHROUGHS
The decade that mattered
VAEs in 2013, GANs in 2014, and the transformer architecture in 2017
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WHAT’S NEXT
The next generation
Contextual understanding, real-time adaptation, multimodal generation, collaborative AI
- 01 · Revolutionizing Industries and Shaping Tomorrow
- 02 · The Evolution of Generative AI
- 03 · Key Features of Next-Generation Generative AI
- 04 · Industry Sector Applications of Generative AI
- 05 · Societal Implications and Ethical Considerations
- 06 · The Decision Loop in Innovation and Enterprise
- 07 · Conclusion — Shaping Tomorrow with Generative AI
- Final · The Promise and Perils of an AI-Enabled Future
Opens with What Is Generative AI? — separating the systems that classify and predict from the ones that produce, and the architectures behind the difference: GANs, VAEs, and transformer models like GPT.
Chapter 4 alone runs more than a hundred pages of sector applications — insurance, high tech, healthcare, financial services, manufacturing, industrial, ESG, pharmaceuticals, retail, and oil and energy.
The platform behind the advice
Advisory grounded in software I build
Prioriti AI is the enterprise decision-intelligence platform I founded — agentic analysis and generative prioritization for executive decisions. Three capabilities leadership teams reach for first. The same instinct runs through the design practice: prototype the thing, put it in front of people, and let the evidence rank it.
Prioritization
Score and Rank
Advanced scoring and ranking with AI-powered insights to drive objective prioritization decisions across initiatives.
See it in the platform
Portfolio
Portfolio Management
Manage and optimize your strategic portfolio with intelligent prioritization, resource allocation, and initiative tracking.
See it in the platformEvery capability in the platform — workspace, dashboards, risk, predictive and competitive analysis — is at prioriti.ai/product.html.
The other practice
From the customer experience side of the work
The published library above leans to AI, because that is what has been written down. The customer experience design and innovation practice is twenty years older and shows up as engagements, methods, and prototypes rather than books — so here is where to look at it.
01
Enterprise engagements →
Customer experience design and innovation work delivered for enterprise organizations across financial services, retail, technology, telecoms, automotive, healthcare, agriculture, and higher education. Each one opens to the detail.
02
The methods →
Journey research and mapping, service blueprinting, experience measurement, concept prototyping and testing — the working methods behind CX strategy, journey reinvention, and innovation programs.
03
The CX Design Sprint →
The experience-led shape of the Strategy & Design Sprint: current-state journey research, the experience the business intends to deliver, a redesigned service blueprint, and prototypes customers have actually seen.
The library
Everything else
Books, whitepapers, articles, tools, and talks across both practices — AI strategy and customer experience design. Filter by type or search the collection.
Let's talk
What decision are you trying to get right?
If your team is weighing where to invest in AI — or trying to move from pilots to something that actually runs the business — a short conversation is usually the fastest way to find out whether I can help.
