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Agentic Intelligence: Strategy at the Speed of Data
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Make Your Data Work Together—and Work Fast
Most organizations aren’t held back by a lack of information, but by slow, scattered data that’s impossible to use when decisions are on the line. In Agentic Intelligence, Manish Sood and Venkat Venkatraman show how companies can break free from outdated systems, unify their data, and operate with the speed today’s business environment demands.
This practical guide explains how real-time data and AI agents can improve decision-making, customer experience, and day-to-day operations. Through straightforward explanations and relatable examples, the authors show what it takes to retire outdated systems, bring critical information together, and give teams a clearer view of what’s happening across an organization.
You’ll
Why unified, real-time data is now an essential source of competitive advantage How agentic systems accelerate decisions and improve customer relationships Ways to eliminate data silos and streamline operations How leaders can use real-time intelligence to drive clarity, trust, and speed
If you want to strengthen your organization and stay ahead in the Intelligence Age, this book offers the roadmap.
Most organizations aren’t held back by a lack of information, but by slow, scattered data that’s impossible to use when decisions are on the line. In Agentic Intelligence, Manish Sood and Venkat Venkatraman show how companies can break free from outdated systems, unify their data, and operate with the speed today’s business environment demands.
This practical guide explains how real-time data and AI agents can improve decision-making, customer experience, and day-to-day operations. Through straightforward explanations and relatable examples, the authors show what it takes to retire outdated systems, bring critical information together, and give teams a clearer view of what’s happening across an organization.
You’ll
Why unified, real-time data is now an essential source of competitive advantage How agentic systems accelerate decisions and improve customer relationships Ways to eliminate data silos and streamline operations How leaders can use real-time intelligence to drive clarity, trust, and speed
If you want to strengthen your organization and stay ahead in the Intelligence Age, this book offers the roadmap.
275 pages, Kindle Edition
Expected publication September 15, 2026
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Review of advance copy received from NetGalley
The Company Knows Plenty. The Problem Is Who Gets to Act.
“Agentic Intelligence” argues that the real enterprise-AI bottleneck is not machine capability but the difficult passage from distributed knowledge to legitimate authority.
By Demetris Papadimitropoulos | August 27th, 2026
Despite all the talk of artificial intelligence, the most revealing object in Manish Sood and Venkat Venkatraman’s “Agentic Intelligence: Strategy at the Speed of Data” is a shoebox. In the speculative opening, a seventy-five-year-old patient named Henry arrives at AirMed Health carrying his medical history in a battered Florsheim box because the institutions responsible for his care have never assembled it for him. The system possesses more information about Henry than he could remember, yet he remains the courier charged with making its fragments intelligible. The trouble is not ignorance. It is that nobody has built a place where what is known can become shared enough to act on.
Enterprise AI books often hurry toward one of two promises: startling machine capability, or executive preparedness before the rival down the street gets there first. “Agentic Intelligence” contains its share of urgency, but Sood and Venkatraman are after a less glamorous failure. Sood, founder and CEO of the enterprise-data company Reltio, and Venkatraman, a longtime scholar of digital strategy, care less about what a model can do in isolation than about what an organization must become before an autonomous system should be allowed to do very much at all. Their term is “coagency”: humans and machines working from enough common context to correct one another and act together. What begins as a book about enterprise data keeps drifting toward an older question: how does scattered knowledge become legitimate action?
Much of the book is built for the conference room. Ten Rules sit inside three sections: Strategic Intent, Invest to Monetize, and Implement to Win. The sequence moves from connected data and velocity through trust, investment, workforce capability, continuous learning, shared context, and governance. One can practically hear the slide deck advancing. Yet the arrangement has an argument inside it. The first section asks what an intelligent organization can see; the second what must be built before seeing becomes useful; the third what changes once humans and machines begin to learn and act together. The book starts with information and ends with authority.
Even its most technical-looking chapters drift toward the institutional. Rule 2 begins with Formula 1 telemetry and the glory of milliseconds, then distinguishes ordinary processing delays from “cognitive latency”: time lost because humans hesitate, distrust information, wait for approval, or do not know who is entitled to act. A technologically fast company can therefore remain organizationally slow. Speed, in this account, is less a temperament than a problem of decision rights. Plenty of institutions do not suffer from an intelligence shortage. They suffer from an authorization shortage.
The prose is built to make such concepts travel. Sood and Venkatraman favor declarative oppositions: data at rest versus data in motion, collecting versus connecting, insight versus action, automation versus coagency. They also have a knack for naming an experience just before it becomes obvious enough to seem as if it always had a name: “attention capital,” “agentic literacy,” “governance in motion.” The language is lucid and usable, occasionally too pleased with its usability. A proposition may be explained, illustrated, renamed, converted into leadership questions, then escorted to the exit beneath a closing maxim. The book is concise sentence by sentence and expansive chapter by chapter.
Recurring metaphors keep the abstractions from floating away. Filing cabinets give way to nervous systems. A caravan crossing a desert carries the burden of investment horizons. An orchestra stands in for agents listening and acting inside common constraints. The strongest image arrives late, when traditional data governance is compared with managing a library and agentic governance with managing highway traffic. Static records can be cataloged after the fact; highway-speed decisions require boundaries while the cars are moving. The image clarifies the late-book concern: not whether autonomous systems will sometimes be wrong, but whether an institution can remain answerable while wrongness travels faster than its customary procedures.
Intelligence, in Sood and Venkatraman’s strongest formulation, belongs less to the machine than to the arrangement connecting machine, data, and organization. Marco Iansiti and Karim R. Lakhani’s “Competing in the Age of AI” reimagined the architecture of the firm; Paul R. Daugherty and H. James Wilson’s “Human + Machine” argued for work built around complementary human and machine abilities. Sood and Venkatraman extend that lineage into a moment when software does not merely analyze or recommend. It can initiate, reroute, approve, escalate, and learn. The asset is not the brilliant model by itself but the shared operating environment in which many actors can see enough of the same situation to coordinate without becoming mutually unintelligible or dangerously obedient.
Precision arrives in Rule 9, which divides shared context into shared perception, shared interpretation, and shared objectives. Humans and machines need sufficiently similar facts; the human must be able to follow the machine’s reading of those facts; both must be working toward compatible ideas of success. That goes beyond the tired warning that AI needs good data. A loan officer and a credit agent can have pristine information and still fail as partners if one cannot understand why the other is recommending rejection. Intelligence, in this account, is not accumulated accuracy. It is coordinated interpretation.
Action exposes the difference between knowing and being able to do anything with what is known. In Rule 6, Rolls-Royce’s predictive engine systems separate the two neatly: detecting that a component is likely to fail creates potential value, but value materializes only when maintenance, scheduling, parts, and personnel change course before failure occurs. The action is not an appendix to the insight. It is the economic event. That distinction moves the authors from analytics toward delegated agency. A company does not become intelligent because it produces excellent predictions any more than a person becomes decisive because she owns an excellent weather app.
Placing governance last is the book’s smartest structural choice. By Rule 10, the authors have spent nine chapters progressively empowering machine actors: connecting information, accelerating its flow, opening access, establishing trust, training human collaborators, creating learning loops, aligning context. Only then do they ask the question all this authority has made unavoidable: who constrains agency while it is executing? CrowdStrike and Zillow become cautionary cases, while commercial aviation supplies the better counterexample: autonomy can be substantial if authority is bounded, behavior observable, escalation clear, and intervention possible. Governance is not the department that arrives after innovation to explain why something cannot be done. It is part of the machinery that lets action occur without turning every mistake into a chase scene.
AirMed, the fictional healthcare organization that opens the book, offers one future; EverLight Studios, the fictional entertainment giant near the end, offers its inverse. EverLight has beloved intellectual property, talented employees, global reach, institutional memory, and enormous resources. What it lacks is the ability to make those resources know one another. Localization disagrees with legal; marketing works from superseded information; a film stalls because no one can establish whether a franchise character has been cleared. No malevolent superintelligence arrives. Everyone merely keeps possessing the wrong fragment at the wrong moment until fragmentation itself begins making decisions.
Destiny creeps into the rhetoric here. Sood and Venkatraman increasingly write as though every company will eventually have to rebuild itself as an intelligence company, with the real choice being whether transformation occurs early from strength or late under duress. The urgency gives the book propulsion but also makes its future unnecessarily narrow. Industries and risk regimes will require very different degrees of speed and autonomy. The authors are strongest when they argue that these capabilities are becoming useful; they become less exact when a powerful tendency hardens into fate. Business books often prefer the future when it comes with only two exits.
Inside coagency sits the book’s neatest evasion. Sood and Venkatraman are excellent on what happens when humans and machines possess different information, work on different clocks, or cannot understand one another’s reasoning. They are less searching about what happens when everyone sees the same reality and still wants different things. Rule 9 names shared objectives as a prerequisite for collaboration, but “shared” is doing suspiciously cheerful work there. Who chooses the objective? Who receives the gains from efficiency? Who absorbs the risk when autonomy fails? Employee and employer can read the same productivity numbers and disagree over higher pay, fewer workers, or more output.
Machines do not dissolve those conflicts by becoming legible. Nor does better architecture turn an economic disagreement into a data-quality problem. The book’s humans sometimes arrive as members of one coordinated enterprise before they are considered as people occupying unequal positions within it. Its preferred account of workplace change sends machines toward repetitive execution and humans toward judgment and supervision. Sometimes it will. It may also remove entry-level work, concentrate authority, intensify monitoring, or distribute gains differently from the rhetoric of partnership. Coordination can settle misunderstandings. It cannot decide what an organization ought to optimize, or for whom.
Ironically, the book supplies its own corrective. Rule 2 celebrates low latency and speed; Rule 10 insists on boundaries, escalation, reversibility, observability, and restraint. Read together, they imply a better principle than either states outright: the goal is not to eliminate friction but to distinguish useless friction from protective friction. A delayed approval because nobody knows who has authority is waste. A second opinion before an irreversible high-stakes decision may be wisdom. A checkpoint can be bureaucratic theater, or it can be the moment in which the organization remembers that efficiency is not the only value worth optimizing. The later Rules improve the earlier ones by sanding down their absolutism.
Today’s bottleneck is already visible: impressive AI pilots remain easier to build than organizations capable of absorbing them. Questions of agent identity, permissions, and oversight are moving from speculative panels into routine enterprise practice. That makes Sood and Venkatraman’s least glamorous proposition plausible for reasons larger than the current enthusiasm for agents: model availability may cease to be the principal constraint long before organizational readiness does. The book is more diagnostic than prophetic. Its vocabulary belongs to the moment, but the condition is ancient. Institutions have always struggled to turn partial knowledge into coordinated action. Autonomous systems merely shorten the interval between misunderstanding and consequence.
Rather than pretending to stand outside the commercial ecosystem it describes, the book makes its entanglement explicit. Sood’s concluding “Reltio as Customer Zero” explains how his company reconciles operational information while presenting Reltio’s “Context Intelligence” as a layer that can give agents current enterprise context. Candor is preferable to coyness, but the fit deserves scrutiny: the future the book prescribes is also one in which the kind of infrastructure Sood’s company sells becomes indispensable. That overlap does not discredit the diagnosis. It asks the reader to separate a persuasive general problem from the commercial solution most congenial to one of its authors, exactly the sort of contestability the book recommends for intelligent systems.
Outside the book’s own terminology, its practical appeal is easy to locate. Ethan Mollick’s “Co-Intelligence” is a useful neighbor because it asks how individuals might work productively with AI; “Agentic Intelligence” enlarges the unit of analysis until collaboration becomes institutional. The machine must know not merely what one worker knows but what the organization means by a customer, contract, permission, risk, and exception. The employee, in turn, must understand enough of the machine’s context to challenge it without treating it as either oracle or malfunctioning intern. Mutual legibility becomes a capability rather than a mood.
Practical but intellectually ambitious, “Agentic Intelligence” earns 84/100 and 4/5 stars. Its intellectual synthesis outruns its prose, and its final third deepens what might otherwise have remained an argument for enterprise data plumbing. It does not interrogate power as rigorously as architecture, and some of its ten Rules could profitably share a room. But it gives business readers more than another basket of use cases: a vocabulary for recognizing why technologically advanced organizations can remain institutionally unintelligent.
Only the shoebox, in the end, is a better emblem than the gleaming autonomous agent. It contains the problem in miniature: the facts are present, the institutions are present, the people are present, yet the burden of making them cohere has fallen on the least powerful participant in the system. Sood and Venkatraman imagine an architecture capable of connecting signals, preserving context, learning from correction, and acting before knowledge goes stale. The vision persuades so long as one remembers what their final Rule implies: intelligence is not proved by how quickly the system moves. It is proved by whether, once everything is connected and moving, someone can still see where the road leads – and has the authority to touch the brakes.
“Agentic Intelligence” argues that the real enterprise-AI bottleneck is not machine capability but the difficult passage from distributed knowledge to legitimate authority.
By Demetris Papadimitropoulos | August 27th, 2026
Despite all the talk of artificial intelligence, the most revealing object in Manish Sood and Venkat Venkatraman’s “Agentic Intelligence: Strategy at the Speed of Data” is a shoebox. In the speculative opening, a seventy-five-year-old patient named Henry arrives at AirMed Health carrying his medical history in a battered Florsheim box because the institutions responsible for his care have never assembled it for him. The system possesses more information about Henry than he could remember, yet he remains the courier charged with making its fragments intelligible. The trouble is not ignorance. It is that nobody has built a place where what is known can become shared enough to act on.
Enterprise AI books often hurry toward one of two promises: startling machine capability, or executive preparedness before the rival down the street gets there first. “Agentic Intelligence” contains its share of urgency, but Sood and Venkatraman are after a less glamorous failure. Sood, founder and CEO of the enterprise-data company Reltio, and Venkatraman, a longtime scholar of digital strategy, care less about what a model can do in isolation than about what an organization must become before an autonomous system should be allowed to do very much at all. Their term is “coagency”: humans and machines working from enough common context to correct one another and act together. What begins as a book about enterprise data keeps drifting toward an older question: how does scattered knowledge become legitimate action?
Much of the book is built for the conference room. Ten Rules sit inside three sections: Strategic Intent, Invest to Monetize, and Implement to Win. The sequence moves from connected data and velocity through trust, investment, workforce capability, continuous learning, shared context, and governance. One can practically hear the slide deck advancing. Yet the arrangement has an argument inside it. The first section asks what an intelligent organization can see; the second what must be built before seeing becomes useful; the third what changes once humans and machines begin to learn and act together. The book starts with information and ends with authority.
Even its most technical-looking chapters drift toward the institutional. Rule 2 begins with Formula 1 telemetry and the glory of milliseconds, then distinguishes ordinary processing delays from “cognitive latency”: time lost because humans hesitate, distrust information, wait for approval, or do not know who is entitled to act. A technologically fast company can therefore remain organizationally slow. Speed, in this account, is less a temperament than a problem of decision rights. Plenty of institutions do not suffer from an intelligence shortage. They suffer from an authorization shortage.
The prose is built to make such concepts travel. Sood and Venkatraman favor declarative oppositions: data at rest versus data in motion, collecting versus connecting, insight versus action, automation versus coagency. They also have a knack for naming an experience just before it becomes obvious enough to seem as if it always had a name: “attention capital,” “agentic literacy,” “governance in motion.” The language is lucid and usable, occasionally too pleased with its usability. A proposition may be explained, illustrated, renamed, converted into leadership questions, then escorted to the exit beneath a closing maxim. The book is concise sentence by sentence and expansive chapter by chapter.
Recurring metaphors keep the abstractions from floating away. Filing cabinets give way to nervous systems. A caravan crossing a desert carries the burden of investment horizons. An orchestra stands in for agents listening and acting inside common constraints. The strongest image arrives late, when traditional data governance is compared with managing a library and agentic governance with managing highway traffic. Static records can be cataloged after the fact; highway-speed decisions require boundaries while the cars are moving. The image clarifies the late-book concern: not whether autonomous systems will sometimes be wrong, but whether an institution can remain answerable while wrongness travels faster than its customary procedures.
Intelligence, in Sood and Venkatraman’s strongest formulation, belongs less to the machine than to the arrangement connecting machine, data, and organization. Marco Iansiti and Karim R. Lakhani’s “Competing in the Age of AI” reimagined the architecture of the firm; Paul R. Daugherty and H. James Wilson’s “Human + Machine” argued for work built around complementary human and machine abilities. Sood and Venkatraman extend that lineage into a moment when software does not merely analyze or recommend. It can initiate, reroute, approve, escalate, and learn. The asset is not the brilliant model by itself but the shared operating environment in which many actors can see enough of the same situation to coordinate without becoming mutually unintelligible or dangerously obedient.
Precision arrives in Rule 9, which divides shared context into shared perception, shared interpretation, and shared objectives. Humans and machines need sufficiently similar facts; the human must be able to follow the machine’s reading of those facts; both must be working toward compatible ideas of success. That goes beyond the tired warning that AI needs good data. A loan officer and a credit agent can have pristine information and still fail as partners if one cannot understand why the other is recommending rejection. Intelligence, in this account, is not accumulated accuracy. It is coordinated interpretation.
Action exposes the difference between knowing and being able to do anything with what is known. In Rule 6, Rolls-Royce’s predictive engine systems separate the two neatly: detecting that a component is likely to fail creates potential value, but value materializes only when maintenance, scheduling, parts, and personnel change course before failure occurs. The action is not an appendix to the insight. It is the economic event. That distinction moves the authors from analytics toward delegated agency. A company does not become intelligent because it produces excellent predictions any more than a person becomes decisive because she owns an excellent weather app.
Placing governance last is the book’s smartest structural choice. By Rule 10, the authors have spent nine chapters progressively empowering machine actors: connecting information, accelerating its flow, opening access, establishing trust, training human collaborators, creating learning loops, aligning context. Only then do they ask the question all this authority has made unavoidable: who constrains agency while it is executing? CrowdStrike and Zillow become cautionary cases, while commercial aviation supplies the better counterexample: autonomy can be substantial if authority is bounded, behavior observable, escalation clear, and intervention possible. Governance is not the department that arrives after innovation to explain why something cannot be done. It is part of the machinery that lets action occur without turning every mistake into a chase scene.
AirMed, the fictional healthcare organization that opens the book, offers one future; EverLight Studios, the fictional entertainment giant near the end, offers its inverse. EverLight has beloved intellectual property, talented employees, global reach, institutional memory, and enormous resources. What it lacks is the ability to make those resources know one another. Localization disagrees with legal; marketing works from superseded information; a film stalls because no one can establish whether a franchise character has been cleared. No malevolent superintelligence arrives. Everyone merely keeps possessing the wrong fragment at the wrong moment until fragmentation itself begins making decisions.
Destiny creeps into the rhetoric here. Sood and Venkatraman increasingly write as though every company will eventually have to rebuild itself as an intelligence company, with the real choice being whether transformation occurs early from strength or late under duress. The urgency gives the book propulsion but also makes its future unnecessarily narrow. Industries and risk regimes will require very different degrees of speed and autonomy. The authors are strongest when they argue that these capabilities are becoming useful; they become less exact when a powerful tendency hardens into fate. Business books often prefer the future when it comes with only two exits.
Inside coagency sits the book’s neatest evasion. Sood and Venkatraman are excellent on what happens when humans and machines possess different information, work on different clocks, or cannot understand one another’s reasoning. They are less searching about what happens when everyone sees the same reality and still wants different things. Rule 9 names shared objectives as a prerequisite for collaboration, but “shared” is doing suspiciously cheerful work there. Who chooses the objective? Who receives the gains from efficiency? Who absorbs the risk when autonomy fails? Employee and employer can read the same productivity numbers and disagree over higher pay, fewer workers, or more output.
Machines do not dissolve those conflicts by becoming legible. Nor does better architecture turn an economic disagreement into a data-quality problem. The book’s humans sometimes arrive as members of one coordinated enterprise before they are considered as people occupying unequal positions within it. Its preferred account of workplace change sends machines toward repetitive execution and humans toward judgment and supervision. Sometimes it will. It may also remove entry-level work, concentrate authority, intensify monitoring, or distribute gains differently from the rhetoric of partnership. Coordination can settle misunderstandings. It cannot decide what an organization ought to optimize, or for whom.
Ironically, the book supplies its own corrective. Rule 2 celebrates low latency and speed; Rule 10 insists on boundaries, escalation, reversibility, observability, and restraint. Read together, they imply a better principle than either states outright: the goal is not to eliminate friction but to distinguish useless friction from protective friction. A delayed approval because nobody knows who has authority is waste. A second opinion before an irreversible high-stakes decision may be wisdom. A checkpoint can be bureaucratic theater, or it can be the moment in which the organization remembers that efficiency is not the only value worth optimizing. The later Rules improve the earlier ones by sanding down their absolutism.
Today’s bottleneck is already visible: impressive AI pilots remain easier to build than organizations capable of absorbing them. Questions of agent identity, permissions, and oversight are moving from speculative panels into routine enterprise practice. That makes Sood and Venkatraman’s least glamorous proposition plausible for reasons larger than the current enthusiasm for agents: model availability may cease to be the principal constraint long before organizational readiness does. The book is more diagnostic than prophetic. Its vocabulary belongs to the moment, but the condition is ancient. Institutions have always struggled to turn partial knowledge into coordinated action. Autonomous systems merely shorten the interval between misunderstanding and consequence.
Rather than pretending to stand outside the commercial ecosystem it describes, the book makes its entanglement explicit. Sood’s concluding “Reltio as Customer Zero” explains how his company reconciles operational information while presenting Reltio’s “Context Intelligence” as a layer that can give agents current enterprise context. Candor is preferable to coyness, but the fit deserves scrutiny: the future the book prescribes is also one in which the kind of infrastructure Sood’s company sells becomes indispensable. That overlap does not discredit the diagnosis. It asks the reader to separate a persuasive general problem from the commercial solution most congenial to one of its authors, exactly the sort of contestability the book recommends for intelligent systems.
Outside the book’s own terminology, its practical appeal is easy to locate. Ethan Mollick’s “Co-Intelligence” is a useful neighbor because it asks how individuals might work productively with AI; “Agentic Intelligence” enlarges the unit of analysis until collaboration becomes institutional. The machine must know not merely what one worker knows but what the organization means by a customer, contract, permission, risk, and exception. The employee, in turn, must understand enough of the machine’s context to challenge it without treating it as either oracle or malfunctioning intern. Mutual legibility becomes a capability rather than a mood.
Practical but intellectually ambitious, “Agentic Intelligence” earns 84/100 and 4/5 stars. Its intellectual synthesis outruns its prose, and its final third deepens what might otherwise have remained an argument for enterprise data plumbing. It does not interrogate power as rigorously as architecture, and some of its ten Rules could profitably share a room. But it gives business readers more than another basket of use cases: a vocabulary for recognizing why technologically advanced organizations can remain institutionally unintelligent.
Only the shoebox, in the end, is a better emblem than the gleaming autonomous agent. It contains the problem in miniature: the facts are present, the institutions are present, the people are present, yet the burden of making them cohere has fallen on the least powerful participant in the system. Sood and Venkatraman imagine an architecture capable of connecting signals, preserving context, learning from correction, and acting before knowledge goes stale. The vision persuades so long as one remembers what their final Rule implies: intelligence is not proved by how quickly the system moves. It is proved by whether, once everything is connected and moving, someone can still see where the road leads – and has the authority to touch the brakes.
Review of advance copy received from NetGalley
From the questions that keep the author awake at night to the birth of Reltio, Agentic Intelligence demonstrates a highly futuristic way of thinking about the age of intelligence. The imagined Airmed scenarios, along with relatable examples such as the shoebox and medication shortages, effectively illustrate the challenges of data silos and the turning point between simply having more data and using smarter data.
Studies such as Nurse Helena and Mercury Insurance highlight the difficulties of integration and the widening gap between the industrial age and the intelligence age. The discussion also raises the challenge of AI hallucinations, particularly through examples such as the digital Heart-o-meter and the smarter workplace.
I found the exploration of hallucinations and their relationship with context and prompting especially interesting. Examples of Aadhaar's intelligence dataset nodes and Estonia's identity distribution model further demonstrate the importance of effective data architecture, and how much potentially valuable intelligence still remains unused.
Concepts such as cognitive latency, medium-dependent data speed, and cross-functional data are made easier to understand through examples like robotaxis and fleeting agents. However, despite the promise of coagency, I felt that the idea of responsible data sharing remains insufficiently defined. Behavioral and systemic trust, therefore, still require significant development. The phantom-braking example is particularly effective in distinguishing between AI hallucination and distorted versions of reality.
The discussion of transformative investment also feels somewhat aspirational because intelligent infrastructure remains fragmented across multiple layers of data silos. Coagency and scalable agentic literacy offer an important perspective on how humans may work alongside AI in real time, yet we are still not fully accustomed to understanding what AI agents actually "see" or how they interpret their environment.
I especially appreciated the reference to Murphy's Law and Amazon's well-known screening failure involving historical datasets. It also raises broader concerns about patterns of digital colonization and gender bias embedded within technological systems.
Overall, Agentic Intelligence helped me better understand the importance of coagency, the sources of AI hallucinations, and the gap between human and AI perception. Yet these gaps remain deeply ingrained. Embedding agentic AI could undoubtedly be revolutionary in terms of time and efficiency, but we still need controlled environments to understand and explore its error patterns because ignoring those errors could have devastating consequences.
Studies such as Nurse Helena and Mercury Insurance highlight the difficulties of integration and the widening gap between the industrial age and the intelligence age. The discussion also raises the challenge of AI hallucinations, particularly through examples such as the digital Heart-o-meter and the smarter workplace.
I found the exploration of hallucinations and their relationship with context and prompting especially interesting. Examples of Aadhaar's intelligence dataset nodes and Estonia's identity distribution model further demonstrate the importance of effective data architecture, and how much potentially valuable intelligence still remains unused.
Concepts such as cognitive latency, medium-dependent data speed, and cross-functional data are made easier to understand through examples like robotaxis and fleeting agents. However, despite the promise of coagency, I felt that the idea of responsible data sharing remains insufficiently defined. Behavioral and systemic trust, therefore, still require significant development. The phantom-braking example is particularly effective in distinguishing between AI hallucination and distorted versions of reality.
The discussion of transformative investment also feels somewhat aspirational because intelligent infrastructure remains fragmented across multiple layers of data silos. Coagency and scalable agentic literacy offer an important perspective on how humans may work alongside AI in real time, yet we are still not fully accustomed to understanding what AI agents actually "see" or how they interpret their environment.
I especially appreciated the reference to Murphy's Law and Amazon's well-known screening failure involving historical datasets. It also raises broader concerns about patterns of digital colonization and gender bias embedded within technological systems.
Overall, Agentic Intelligence helped me better understand the importance of coagency, the sources of AI hallucinations, and the gap between human and AI perception. Yet these gaps remain deeply ingrained. Embedding agentic AI could undoubtedly be revolutionary in terms of time and efficiency, but we still need controlled environments to understand and explore its error patterns because ignoring those errors could have devastating consequences.
Review of advance copy received from NetGalley
In an era where every organization is scrambling to plug artificial intelligence into its operations, this timely, visionary guide cuts through the surrounding hype to answer the question that actually matters: how do you get your business ready for autonomous systems to safely and effectively act on your behalf? It presents a grounded, sharp paradigm shift for modern leadership, demonstrating that true digital advantage isn't just about using smarter tools, but about unifying your data foundation so machines and humans can execute together seamlessly.
Here are three critical insights that completely redefine how to approach the next wave of enterprise technology:
Unified real-time data is the ultimate prerequisite: Autonomous AI systems are only as effective as the underlying information they process. Eliminating data silos and establishing a single, trusted, real-time operating picture across departments is essential before delegating high-stakes decisions to machines.
hift from AI tools to human-machine co-agents: The true strategic leap happens when organizations stop viewing artificial intelligence as an isolated helper and start integrating autonomous agents directly into daily decision-making workflows alongside human teams.
Speed of execution requires structural governance: Accelerating organizational speed isn't about moving fast and breaking things; it requires building a reliable coordination fabric where data governance, clear rules, and operational visibility allow intelligent systems to act with clarity, trust, and speed.
This is a masterclass in modern digital strategy. It offers an essential, highly pragmatic roadmap for executives and leaders determined to build an agile, future-proof organization.
Here are three critical insights that completely redefine how to approach the next wave of enterprise technology:
Unified real-time data is the ultimate prerequisite: Autonomous AI systems are only as effective as the underlying information they process. Eliminating data silos and establishing a single, trusted, real-time operating picture across departments is essential before delegating high-stakes decisions to machines.
hift from AI tools to human-machine co-agents: The true strategic leap happens when organizations stop viewing artificial intelligence as an isolated helper and start integrating autonomous agents directly into daily decision-making workflows alongside human teams.
Speed of execution requires structural governance: Accelerating organizational speed isn't about moving fast and breaking things; it requires building a reliable coordination fabric where data governance, clear rules, and operational visibility allow intelligent systems to act with clarity, trust, and speed.
This is a masterclass in modern digital strategy. It offers an essential, highly pragmatic roadmap for executives and leaders determined to build an agile, future-proof organization.
Displaying 1 - 3 of 3 reviews