The End of Agile as We Know It: Are Human Organisations and AI Systems Becoming the Next Management Divide?

For twenty years, organisations have chased agility.

They reorganised teams, introduced Scrum, adopted product operating models, invested in continuous delivery, and created transformation offices dedicated to becoming more responsive.

Yet the central promise of Agile remains unfinished.

Many organisations became agile at the team level while remaining fundamentally traditional at the enterprise level. They changed ceremonies but not structures. They changed delivery methods but not decision systems. They empowered teams while retaining hierarchical control over priorities, funding, and accountability.

The next question is more disruptive:

What if Agile was never meant to improve the existing organisation? What if it was meant to replace it?

This is the question explored by Karl A L Smith through a body of work spanning enterprise transformation, information architecture, user experience, organisational design, and emerging approaches to artificial intelligence.

His work presents two different but connected visions for the future organisation.

One asks what happens when organisations become fully human-centred adaptive systems.

The other asks what happens when artificial intelligence becomes an organisational counterparty capable of participating in decision-making.

The tension between these approaches may define the next era of management.

Agile’s Unfinished Revolution

The original Agile movement challenged the assumption that complex work could be predicted and controlled through detailed upfront planning.

Its message was clear:

• listen to customers,
• shorten feedback loops,
• empower teams,
• adapt continuously.

But enterprise adoption often stopped halfway.

Organisations introduced agile practices while preserving industrial-era assumptions:

• employees remained organised around fixed roles,
• managers remained responsible for allocating work,
• budgets remained disconnected from outcomes,
• departments remained the primary organisational units.

The result was a contradiction:

Agile teams operating inside non-agile enterprises.

The next evolution requires a more radical shift.

Instead of making teams agile, organisations must become adaptive systems.

The Rise of Work Orchestration

A conventional enterprise is built around a hierarchy of people.

A future adaptive enterprise may be built around a hierarchy of outcomes.

This distinction is fundamental.

In traditional organisations:

“People own roles, and roles own work.”

In work-orchestrated organisations:

“Work creates outcomes, and capability flows toward the most valuable work.”

The Customer Agility Framework (CAF), associated with Smith’s work, represents this broader interpretation of agility: connecting executive insight, strategic priorities, operational decisions, and production outcomes through continuous feedback.

The organisation becomes less like a machine with fixed components and more like a living system continuously reallocating capability.

The All me Experiment: Can Humans Organise Without Traditional Management?

The All me ecosystem represents a particularly interesting experiment because it appears to remove one of the most common assumptions of modern organisations: that effective coordination requires conventional employment structures.

Instead, it uses concepts such as:

• retained contributors,
• shared work backlogs,
• collective prioritisation,
• self-selection of work,
• outcome-based contribution.

The proposition is provocative:

Perhaps people do not need managers to allocate work. Perhaps they need visibility of important work, alignment around purpose, and mechanisms for trust and accountability.

This is not a small change.

It challenges the foundation of the modern employment relationship.

The traditional organisation asks:

“How do we manage people?”

The adaptive organisation asks:

“How do we enable valuable work to find the right capability?”

The Counterargument: Self-Organisation Has Limits

The criticism is obvious.

Human systems are not automatically efficient.

Without effective governance, self-organising systems can create:

• competing priorities,
• neglected essential work,
• decision paralysis,
• popularity-driven choices,
• unclear accountability.

The absence of hierarchy does not guarantee freedom. It can simply create invisible hierarchies.

A mature adaptive organisation therefore requires stronger mechanisms, not weaker ones:

• transparent priorities,
• clear outcomes,
• measurement,
• feedback loops,
• reputation systems,
• decision discipline.

The challenge is not removing management.

It is redesigning management.

AI Changes the Question

While All me appears to explore the limits of human organisational intelligence without AI, Smith’s work in AI Organisational Design introduces a different possibility.

Here, AI is not positioned simply as automation.

It is described as a counterparty.

That distinction matters.

A replacement model says:

“AI does what humans currently do.”

A counterparty model says:

“AI contributes capabilities humans do not naturally possess.”

In this model, AI may contribute through:

• probabilistic analysis,
• pattern recognition,
• scenario modelling,
• decision support.

Humans retain responsibility for:

• values,
• purpose,
• ethics,
• judgement.

The future organisation may therefore involve a relationship between two forms of intelligence.

The Real Management Question: Who Gets to Decide?

The emerging debate is not really about whether AI is good or bad.

Nor is it about whether humans are superior to machines.

The deeper question is:

Where should decision authority reside?

Should organisations rely primarily on:

• human emergence,
• machine optimisation,
• or a carefully designed combination of both?

The human-first model argues that intelligence is already distributed throughout the organisation and must be unlocked.

The AI-augmented model argues that organisations can become more capable by adding new forms of intelligence.

Both challenge the traditional command-and-control enterprise.

Beyond Agile: The Adaptive Enterprise

The next generation of organisations may not look like today’s companies.

They may not be defined primarily by:

• departments,
• job titles,
• reporting structures.

They may instead be defined by:

• flows of work,
• networks of capability,
• continuous learning,
• human and machine intelligence working together.

The legacy question was:

“How do we make people execute strategy?”

The emerging question is:

“How do we create systems that continuously discover and execute the right strategy?”

Agile began this journey by changing how teams work.

The next transformation may change what an organisation is.

The future may belong neither to humans alone nor machines alone.

It may belong to organisations designed intentionally around both.

References

Agile and Enterprise Agility

Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, W., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R. C., Mellor, S., Schwaber, K., Sutherland, J., & Thomas, D. (2001).
Manifesto for Agile Software Development.
https://agilemanifesto.org/

Rigby, D. K., Sutherland, J., & Takeuchi, H. (2016).
Embracing Agile.
Harvard Business Review, 94(5), 40–50.

Denning, S. (2018).
The Age of Agile: How Smart Companies Are Transforming the Way Work Gets Done.
AMACOM.

Organisational Design and Adaptive Systems

Snowden, D. J., & Boone, M. E. (2007).
A Leader’s Framework for Decision Making.
Harvard Business Review, 85(11), 68–76.

Senge, P. M. (1990).
The Fifth Discipline: The Art & Practice of the Learning Organization.
Doubleday.

Hamel, G., & Zanini, M. (2020).
Humanocracy: Creating Organizations as Amazing as the People Inside Them.
Harvard Business Review Press.

Information Architecture and Knowledge Systems

Davenport, T. H., & Prusak, L. (1998).
Working Knowledge: How Organizations Manage What They Know.
Harvard Business School Press.

Rosenfeld, L., Morville, P., & Arango, J. (2015).
Information Architecture: For the Web and Beyond (4th ed.).
O’Reilly Media.

Decision Science and Probabilistic Governance

Pearl, J. (1988).
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
Morgan Kaufmann.

Pearl, J. (2009).
Causality: Models, Reasoning, and Inference (2nd ed.).
Cambridge University Press.

Kahneman, D. (2011).
Thinking, Fast and Slow.
Farrar, Straus and Giroux.

Human–AI Collaboration

Davenport, T. H., & Kirby, J. (2016).
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.
Harper Business.

Russell, S. (2019).
Human Compatible: Artificial Intelligence and the Problem of Control.
Viking.

Amershi, S., Weld, D., Vorvoreanu, M., et al. (2019).
Guidelines for Human-AI Interaction.
Proceedings of the CHI Conference on Human Factors in Computing Systems.

Primary Sources

Smith, K. A. L.
All me ecosystem research and organisational design materials.
https://all-me.mobi/

Smith, K. A. L.
AI Organisational Design and Customer Agility Framework materials.
https://organisational-design.com/ and https://customeragilityframework.com/

Further Reading

Agile Evolution and Scaling

Schwaber, K., & Sutherland, J.
The Scrum Guide.
https://scrumguides.org/

Larman, C., & Vodde, B. (2016).
Large-Scale Scrum: More with LeSS.
Addison-Wesley.

Kniberg, H., & Ivarsson, A. (2012).
Scaling Agile @ Spotify.
Spotify Engineering Culture.

Scaled Agile, Inc.
Scaled Agile Framework (SAFe).
https://scaledagile.com/

Complexity and Emergence

Holland, J. H. (1995).
Hidden Order: How Adaptation Builds Complexity.
Addison-Wesley.

Stacey, R. D. (1995).
The Science of Complexity: An Alternative Perspective for Strategic Change Processes.
Strategic Management Journal, 16(6), 477–495.

Wheatley, M. J. (2006).
Leadership and the New Science: Discovering Order in a Chaotic World (3rd ed.).
Berrett-Koehler.

Organisational Learning and Distributed Cognition

Argyris, C., & Schön, D. A. (1978).
Organizational Learning: A Theory of Action Perspective.
Addison-Wesley.

Hutchins, E. (1995).
Cognition in the Wild.
MIT Press.

Weick, K. E. (1995).
Sensemaking in Organizations.
SAGE Publications.

Knowledge, Taxonomy, and Search

Wurman, R. S. (1997).
Information Architects.
Graphis.

Woods, E. (2004).
The Corporate Taxonomy: Creating a Knowledge Map for Business.
KM World.

AI, Intelligence, and the Future of Work

Brynjolfsson, E., & McAfee, A. (2014).
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies.
W. W. Norton.

Ford, M. (2015).
Rise of the Robots: Technology and the Threat of a Jobless Future.
Basic Books.

Mitchell, M. (2019).
Artificial Intelligence: A Guide for Thinking Humans.
Farrar, Straus and Giroux.

Self-Managing Organisations

Laloux, F. (2014).
Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness.
Nelson Parker.

Bernstein, E., Bunch, J., Canner, N., & Lee, M. (2016).
Beyond the Holacracy Hype.
Harvard Business Review, 94(7–8), 38–49.

Note on the Article’s Intellectual Position

The article does not argue that AI and human-centred organisational design are competing alternatives. Rather, it examines two complementary responses to the same challenge:

How should organisations govern complexity when both human and machine intelligence are available?

One approach explores the augmentation of organisational intelligence through AI-enabled decision support.

The other explores the potential of human adaptive systems through new models of work orchestration and organisational design.

The future organisation may ultimately combine both: human judgement and purpose, adaptive structures, and machine intelligence applied where it creates genuine advantage.

About Karl A L Smith

Karl A L Smith is a technology and organisational design practitioner whose work spans information architecture, enterprise transformation, agile operating models, and decision systems.

His career has focused on designing ways for complex organisations to structure knowledge, coordinate activity, and improve decision-making. Early in his career, he worked on large-scale taxonomy and information classification challenges for UK Government, before the establishment of the Government Digital Service (GDS), and later developed faceted search technologies designed to help organisations navigate complex information environments.

Smith has contributed to enterprise transformation initiatives across major organisations, including work associated with financial services environments such as NatWest and HSBC, where elements of his thinking around enterprise agility, work orchestration, and outcome-focused operating models were applied. He also contributed to the development of Wipro Digital, where aspects of these approaches were explored in a startup environment.

His work has evolved into the Customer Agility Framework (CAF), an enterprise operating model designed to connect executive-level insight through to production outcomes by aligning strategy, decision-making, work orchestration, and delivery.

Alongside human-centred organisational design, Smith has explored the role of artificial intelligence in enterprise decision-making. Through AI Organisational Design, he examines the relationship between human judgement and machine intelligence, including the use of probabilistic approaches to support complex decisions.

Smith is also the founder of the All me ecosystem, which explores an alternative human-centred organisational model based on principles including work orchestration, collective prioritisation, retained contribution, and adaptive coordination. The initiative deliberately explores organisational design without relying on AI automation, providing a contrasting perspective on how human systems can organise and create value.

Across his work, Smith’s central theme is the design of systems that help humans and organisations manage increasing complexity through better information structures, improved decision processes, and more adaptive ways of working.