Favorite reading
Technology creates value when people can use it, teams can sustain it and leaders can trust it. These readings connect data and AI strategy with operating models, organizational change and the practical work of implementation. Selected for chief data, technology and AI leaders, and for the operators and investors working alongside them.
Start with mine
What held up across banks, insurers, a central bank and a global firm, and what it means for anyone trying to get data and AI into production. 40 lessons in 8 themes, with practical actions, evidence checks and implementation sources.
Ten to start with
Reading paths
The full list
Charles Conn and Robert McLean · Wiley
The clearest modern account of the McKinsey method: define the problem precisely, break it into a logic tree, prune to the issues that matter, plan the analysis, then synthesize and communicate. It is the approach I have used throughout my career to take apart problems that look too big to start.
Bring to the discussion Can we state the problem in one sentence that a decision maker would sign, and does our issue tree cover it without overlap?
Paid book; publisher excerpt available
Barbara Minto
Where MECE comes from. Minto, who developed it at McKinsey, shows how to lead with the answer, group supporting ideas so they are mutually exclusive and collectively exhaustive, and order them so a reader can follow and challenge the logic. It changes how you think as much as how you write.
Bring to the discussion If an executive reads only the first paragraph of our recommendation, do they have the answer and the reason?
Paid book; McKinsey Alumni profile of the author
Ethan M. Rasiel; Ethan M. Rasiel and Paul N. Friga · McGraw-Hill
Two short, practical companions on hypothesis-driven work: form an early answer, test it with the analysis that matters, apply the 80/20 rule, and structure findings MECE. The second book turns the techniques into tools a team can apply on Monday.
Bring to the discussion What is our working hypothesis, and which single analysis would prove it wrong fastest?
Paid books; library catalog record
Eric Lamarre, Kate Smaje, Robert Levin, Alex Singla and Alexander Sukharevsky · McKinsey & Company
Connects technology and AI investment with the organizational capabilities needed to turn it into business value. A useful anchor for executive alignment before discussing platform choices.
Bring to the discussion Which business domain will we transform, and which capabilities must change for the value to reach the business?
Paid book; public author interviews and excerpts
RJL companion: The Data and AI Operating Model
McKinsey & Company
A concise foundation for changing behavior through understanding, reinforcement, skills and leadership example. Use it to test whether the adoption plan extends beyond communications and training.
Bring to the discussion Do our incentives, managers and everyday processes reinforce the behavior we are asking people to adopt?
Public article
RJL companion: The Data and AI Operating Model
Darrell Rigby, Sarah Elk and Steve Berez · Bain & Company
Explores how agile delivery interacts with leadership, budgeting, structure and technology. Useful when stable product teams are being introduced into an organization built around annual projects.
Bring to the discussion Which planning and funding rules still force teams to behave like temporary projects?
Paid book; public overview and excerpt
RJL companion: The Data and AI Operating Model
Dana Maor, Hans-Werner Kaas, Kurt Strovink and Ramesh Srinivasan · McKinsey & Company
Focuses on the human work of leadership: self-awareness, purpose and connection with others. Complements technical transformation plans that depend on trust and influence across functions.
Bring to the discussion What must the leadership team change in its own behavior before asking the organization to change?
Paid book; public interviews and excerpts
RJL companion: The Data and AI Operating Model
John P. Kotter · Kotter
A foundational guide to mobilizing large-scale change, building a coalition and sustaining momentum. Pair its original eight-stage approach with Kotter’s current explanation of the evolved accelerators.
Bring to the discussion Who belongs in the guiding coalition, and what visible early outcome will build commitment?
Paid book; public framework overview
RJL companion: The Data and AI Operating Model
Karen Ball · Prosci; foreword by Jeffrey M. Hiatt
Provides a practical lens for individual adoption and sustained use. Useful for diagnosing whether a team’s barrier is awareness, willingness, knowledge, capability or reinforcement.
Bring to the discussion Where is adoption breaking down for this role, and what specific intervention addresses that barrier?
Paid book; public ADKAR model overview
RJL companion: The Data and AI Operating Model
Matthew Skelton and Manuel Pais · IT Revolution
Offers a vocabulary for team responsibilities, interaction and cognitive load. Use it to clarify platform services and temporary enabling support without turning every specialty into a permanent team.
Bring to the discussion Which dependencies or cognitive demands prevent a delivery team from owning an outcome end to end?
Paid book; public concepts and overview
RJL companion: The Data and AI Operating Model
Patrick Litre and Kevin Murphy · Bain & Company
Examines the emotional swings that can distort transformation decisions. Useful for planning leadership interventions and recognizing predictable trouble before momentum is lost.
Bring to the discussion How will we recognize and respond when initial excitement turns into uncertainty or fatigue?
Public article
RJL companion: The Data and AI Operating Model
Boston Consulting Group
Brings strategy, leadership, talent, governance and technology into one transformation discussion. A useful assessment prompt before treating delivery problems as a tooling issue.
Bring to the discussion Which organizational conditions are missing from our plan, and who is accountable for closing them?
Public article
RJL companion: The Data and AI Operating Model
McKinsey & Company; insights from Brooke Weddle
Connects AI adoption to workflow redesign, leadership, culture and operating models. A focused companion when teams have deployed tools but cannot show meaningful business improvement.
Bring to the discussion What changed in the workflow after deployment, and how are we measuring actual use and resulting value?
Public article
RJL companion: The Data and AI Operating Model
Alex Singla, Alexander Sukharevsky, Kate Smaje, Eric Lamarre and Robert Levin · McKinsey & Company
Examines the management capabilities behind repeated AI value creation. Use it to connect experimentation with prioritization, operating discipline and the capacity to scale across business domains.
Bring to the discussion What management capability is limiting our ability to repeat and scale successful AI work?
Public article
RJL companion: The Data and AI Operating Model
Boston Consulting Group
Examines employee AI use and the organizational conditions around it. Useful for considering leadership support, training and redesigned work together rather than measuring access alone.
Bring to the discussion Which roles have usable tools, manager support and practical training for their actual work?
Public article and report resources
RJL companion: The Data and AI Operating Model
Boston Consulting Group
Frames AI as a redesign of work and organizational responsibilities. Useful for coordinating business, people and technology leaders before simply adding agents to existing processes.
Bring to the discussion Which decisions, responsibilities and skills must change when AI becomes part of the workflow?
Public article
RJL companion: The Data and AI Operating Model
Accenture
Links enterprise reinvention with a digital core, talent and responsible AI. Useful for discussing investment across capabilities instead of treating AI as a collection of isolated pilots.
Bring to the discussion Are our investments strengthening the common capabilities needed by several business priorities?
Public report landing page
RJL companion: The Federated Data Platform
Accenture
Examines how AI changes tasks, workforce capabilities and the employee experience. Useful for planning skills development and work redesign alongside technology delivery.
Bring to the discussion What will employees do differently, and how will we prepare them to perform that work well?
Public overview and report resources
RJL companion: The Data and AI Operating Model
Deloitte
Provides a current executive perspective on scaling, workflow reimagination, AI fluency and agentic readiness. Use it to challenge whether an AI program is ready for sustained enterprise operation.
Bring to the discussion Which readiness gap would stop us from safely scaling a promising use case?
Public findings; report download on publisher site
RJL companion: The Data and AI Operating Model
PwC
Explores changing job requirements, skills and workforce outcomes around AI exposure. Useful for considering career pathways and human judgment in the design of AI-enabled work.
Bring to the discussion How will role expectations, entry-level development and investment in human skills change?
Public overview and report downloads
RJL companion: The Data and AI Operating Model
EY
Shares EY’s organizational lessons on sponsorship, funding, literacy, communication and governance. Useful as a concrete discussion prompt for coordinating an enterprise AI transition.
Bring to the discussion Have we funded adoption and governance with the same seriousness as the technical platform?
Public PDF
RJL companion: The Data and AI Operating Model
University of Melbourne, in collaboration with KPMG
Examines AI use, trust and attitudes among people across countries. Useful for grounding adoption plans in employee understanding and concerns rather than assuming access creates trust.
Bring to the discussion What would make employees confident enough to use AI appropriately and report concerns?
Public overview and report download
RJL companion: Context Before Query
EDM Association (formerly EDM Council)
Structures the capabilities and evidence behind a sustainable data program. Use the public overview to organize priorities, and authorized framework access for a formal assessment.
Bring to the discussion Which capability gaps are evidenced, and which investment will improve business outcomes?
Public overview; full framework for member firms and authorized partners
RJL companion: Data Products in Practice
EDM Association (formerly EDM Council)
Connects sensitive-data management in cloud environments with ownership, access and automated controls. Useful for testing whether platform protections are repeatable and supported by evidence.
Bring to the discussion Which sensitive-data controls are enforced automatically, and what evidence shows they work?
Public overview; free framework license for internal use under publisher terms
RJL companion: The Federated Data Platform
DAMA International
A broad reference for the disciplines behind dependable data, including governance, quality and master data. Use selected chapters to establish shared language and avoid gaps between specialties.
Bring to the discussion Which data-management disciplines need explicit ownership in our operating model?
Paid book; public overview
RJL companion: Data Products in Practice
DAMA International
An executive companion to the broader data-management body of knowledge. Useful for introducing business leaders to why data discipline matters before diving into detailed implementation practices.
Bring to the discussion Can the sponsor explain the business need for data management without relying on technical terminology?
Public overview; purchase link on publisher site
RJL companion: Data Products in Practice
Zhamak Dehghani · MartinFowler.com
Explains domain ownership, data as a product, self-service infrastructure and federated governance. Useful for separating the organizational principles from claims made by particular technology vendors.
Bring to the discussion Can domains own products sustainably, and what shared services make that responsibility practical?
Public article
RJL companion: The Federated Data Platform
DORA
Defines the current delivery metrics and their use for improvement. Useful for evaluating flow and stability alongside business outcomes; interpret the measures within a team’s service context.
Bring to the discussion What do delivery speed and instability reveal about our ability to improve a live service?
Public guide
RJL companion: The Data and AI Operating Model
National Institute of Standards and Technology (NIST)
Offers a structured approach to governing, mapping, measuring and managing AI risks. Useful for translating responsible-AI commitments into owners, assessment work and lifecycle evidence.
Bring to the discussion Which risks require assessment before release, and who owns the residual-risk decision?
Public framework and resources
RJL companion: Context Before Query
NIST
Extends the AI RMF with generative-AI considerations and suggested actions. Useful for choosing concrete risk work appropriate to a use case, without treating every suggestion as a legal requirement.
Bring to the discussion Which generative-AI risks are material to this workflow, and how will we test and monitor them?
Public PDF
RJL companion: Context Before Query
OWASP Cheat Sheet Series
Provides implementation guidance for retrieval-augmented generation security. Useful for examining retrieval authorization, untrusted content and other controls around enterprise knowledge systems.
Bring to the discussion Does retrieval enforce the user’s access before content reaches the model, and how do we handle untrusted documents?
Public guide
RJL companion: Context Before Query
Nothing matches.
My top picks, and ones others have recommended to me, for CIOs, CTOs, CDOs, CDAOs and the leaders working alongside them. The collection is an editorial selection, not a popularity ranking or a shared recommendation issued by the named organizations. Original works belong to their named authors and publishers. RJL Solutions provides original reading notes and links to official sources. Inclusion does not imply affiliation or endorsement. Access labels reflect the official pages reviewed on October 10, 2026; publisher terms govern use.
Next step
We will work through what is blocking it, what evidence is missing and whether there is a useful next step.