Organizing for Innovation, Part V: The Leadership Challenge

Organizations of autonomous teams require a different set of behaviors than organizations that are run like a machine. People in a self-directed team form their own appreciations for what should be done, prioritize what will be done, and self-determine how it will be done. They are unencumbered by hierarchy, expected to communicate with anybody in the organization they need. They are unencumbered by role definition, as people simply do whatever work is required even when that means acquiring new skills or knowledge. They are unencumbered by organization, as teams form task-forces to solve for problems that are beyond the scope of any single defined team.

This all sounds great. Who wouldn't want to work this way?

The majority of people working in enterprise IT today, that's who.

Enterprise tech labor is highly codified (bounded responsibilities) and stratified (seniority). Aside from the fact that this serves the interests of a multitude of non-tech corporate functions such as human resources, vendor management and finance, it also provides a great deal of comfort to the individual. The employee knows very precisely what is expected of them to earn a salary increase or advance their career, while the contractor knows what they are obliged to do to satisfy the terms of their contract. Over time, jobs become working annuities that require little servicing (such as skill acquisition or excessively long working hours) for comfortable levels of compensation with job security (by e.g., being the only ones familiar with a technology, service or function).

The autonomous team environment is anathema to this. For starters, the autonomous team operates with a lot of ambiguity. New appreciations change the team's priority constantly, meaning there is no deterministic plan. Plus, people adapt themselves to the work that needs to be done as opposed to working in strict swim-lanes. An autonomous team environment implicitly subordinates the traditional goals of the individual: the success of the team is success for the individual. An autonomous team breaks down when its members put individual achievement over team accomplishment.

Of course, working this way is a question of both skill and will. Whether labor can re-orient itself from machine to autonomy is a debate well beyond the scope of any blog post, but suffice to say that some - few, several or most - will not be able to make the transition from doing what they are told to do by management, to figuring out for themselves what needs to be done and doing it. In addition, whether labor willingly chooses to re-orient itself is another matter entirely. Over-exposure to enterprise change programs - or more likely, over-exposure to failed enterprise change programs - won't inspire enthusiasm for yet another one. There is also the suspension of disbelief people need to make to give devolved authority and autonomous team structures a try.

This brings us to the leadership challenge that creating an organization of autonomous teams poses. Obviously, there are institutional barriers that have to be overcome. HR has to be comfortable with ambiguous roles and titles. Vendor contracts for supply of specialist labor have to be replaced with contracts tied to business outcomes. Finance needs an alternative to predictive planning and financial budgeting.

However, the challenge to leadership goes beyond mechanical processes and structural changes. In an organization of autonomous teams the nature of leadership changes from giving commands to guiding actions. The leader does not direct people's performance to achieve a goal (organization-as-machine), but instead projects a goal that people direct themselves toward achieving (organization-as-brain). The leader in the autonomous organization makes use of:

• Concrete statements of expectations: leaders have to make expectations crystal clear. Saying "all enterprise software is deployed into production every other week" is a clear expectation. Saying "we will be a continuous deployment organization" is a woolly statement: "continuous" will be interpreted relative to the current skills and learned helplessness of the people responsible for enterprise software today. The latter statement also misses the point: how the organization functions (continuous deployment) is less important than describing what it achieves (new software released across the board every other week.) The prescriptive implications of that statement deny the people in the organization the opportunity to figure out for themselves how best to achieve the goal. The leader communicates the expectation; it is up to the people in the organization to figure out how to achieve it.
• Rewards and recognition: obviously, what gets measured is what gets managed. If the goal is to increase frequency of deployments, reward the teams that find ways to make reliable production releases weekly, then twice weekly, then daily, then multiple times per day. Story-telling is important as well, especially as an organization adopts new behavioral norms and its business partners develop new expectations of it. For example, in its early days, FedEx executives were fond of repeating the story of the junior employee who rented a helicopter on his personal American Express card to fly him to a mountain location to repair snow-damaged phone lines so that they could service remote customers. When you're building a reputation for absolutely, positively getting packages delivered overnight, stories like this go a very long way.
• Acknowledging and solving constraints: every team, not to mention the organization as a whole, will be short of the capacity, capability and capital to achieve every organizational goal. While constraints force institutional responses such as prioritization and innovation, they are also opportunities for a systemic change. It is the leader's obligation to work with constrained teams to identify the actions they can take today so that they will be less constrained in the future. For example, if there is a capability constraint, what can the team do now to develop skills and knowledge so that it can do more for itself? If a financial constraint, how can the team build a stronger business case or define a set of experiments to explore potential value? In the face of constraints, the leader's responsibility is to develop an organization's ability to learn how to learn.

At the foundation of the organization of autonomous teams is Theory Y. The aspirational leader of an organization of autonomous teams has to believe that people are motivated, responsible, and do not need close supervision. If you do not fundamentally believe this, do not bother pursuing an organization of autonomous teams. Once you revert to command-and-control (Theory X), you have lost the mantle of leadership in a devolved organization.

In the next installment in this series, we will put all of these concepts together to visualize what the autonomous organization looks like at scale.

Organizing for Innovation Part IV: Autonomy at Scale

It is easy to understand how a small organization of autonomous teams can function. When there are only a few teams, there is a small community, and it is simple for people to communicate with one another in both formal and informal ways.

It is not difficult to see how a large organization of largely independent teams can scale. For nearly 60 years, Gore-Tex has shown that devolved authority can work just fine at scale.

Autonomy scales at Gore-Tex because there is very little overlap between outerwear and dental floss, and subsequently less coordination. What happens when the business becomes complex, with lots of interdependencies among lots of teams?

Dependencies by themselves do not make it difficult to understand how devolved authority can work on a small scale. If there are 4 teams, there are a maximum of 6 communication pathways (n x (n - 1) / 2). Even if there are transitive dependencies, the small size of the community - 4 tech leads, 4 product owners, etc. - makes cross-functional communications relatively easy. But a technology organization of tens of thousands of people will have hundreds of teams - and therefore an extraordinarily large number of communication pathways. Translating a small number of enterprise goals into hundreds of millions of synapses sounds like opacity at best, chaos at worst.

There are four things that an organization of autonomous teams needs if it is to scale.

The first is an implicit hierarchy, but one of purpose rather than control.¹ Traditionally, hierarchy is meant to control activity througyh supervisory responsibility and assigned decision rights: the subordinates in one division take direction from superiors in the same division. If, per the initial blog post in this series, decision rights are devolved, "span of control" does not exist in the organization of autonomous teams.

Hierarchy also influences communications. If those "higher up" in the hierarchy use the things produced by those "further down", there is an obvious pattern of communications between producers and consumers. In manufacturing, there might be different teams independently assembling subsystems such as brakes or drivetrains from individual component parts. Each of their subsystems might then be consumed by teams on the line installing them into more comprehensive systems (such as the powertrain), and, ultimately, the finished vehicle itself.

This is called a "hierarchy of purpose." The hierarchy is constructed largely around degrees of granularity. Brakes and drivetrains are smaller assemblies that form larger subsystems that contribute to the finished product. A provider of cloud-based infrastructure such as Amazon can organize in the same way: the "finished product" of a cloud instance consists of more "primitive" components of virtual storage, server and network. Each of those subsystems in turn consists of more finely grained primitive components of network protocol communication, CPU, load balancing, and so forth.

Creating complex higher-order offerings as composites of lower-level capabilities is the “platform effect” of innovation.

It's worth mentioning that enterprise program management has long tried the same structure. It chokes on itself when transitive dependencies that extend several layers deep expose the difference between a boundary in work (a team has exclusive responsibility for producing something used by many other teams) and a boundary in fact (the output is high-touch service, support and maintenance, making those boundaries porous). A primitive component must be consumable in a friction-free manner.

This is a logical transition to the second thing the organization of autonomous teams requires, which is practical patterns of communications. The platform effect scales effectively because consumption patterns are the de-facto communication patterns within the organization. In a hierarchy of purpose, the organization does not have hundreds of millions of communication pathways, because the number of consumers is limited. A cloud instance may consume a load-balancing primitive, but it does so through a more coarsely grained "network" intermediary.

The decision classes introduced in the initial post in this series act as a control system for each individual team. The wider the divergence of type of consumer, the more difficult it is to create patterns of demand and prioritize to form a product strategy. The nested team organization limits the divergence of consumer demand, which creates a narrower range of appreciations, which make cohesiveness of strategy and execution at an individual product level far easier to perform.²

The autonomous-team collective maintains enterprise cohesiveness by virtue of its communication patterns. To understand how, we have to revisit the devolved decision classes. Appreciations (what should be done) act as the organizational system for managerial decisions (what can be done); managerial decisions function as the instrumentation system for appreciations. Managerial decisions, in turn, act as the organizational system for technical decisions (how will it be done); technical decisions, in turn, act as the instrumentation system for managerial decisions.

Scope	Nature	Organizes	Instruments
Appreciations	What should be done?	Managerial
Managerial	What can be done?	Technical	Appreciations
Technical	How will it be done?		Managerial

How this works effectively in practice becomes clear when we look at the characteristics of a single autonomous team that we saw in the last post. The transmission of minimum critical specifications through a hierarchy of purpose limits the noise and confusion. The reception of minimum critical specifications from multiple consumers are interpreted as appreciations through double-loop learning. The relationship of control systems and instrumentation systems of the different decision classes categorizes the information appropriately.

The third thing required for an organization of autonomous teams to function at scale is the ability to handle extraordinary patterns of communication. As there is no hierarchy of control, the organization needs protocols that allow it to adapt itself to a changing problem space, as well as to resolve inter-team conflicts.

A dynamic problem space is addressed through task-forces, which may be short- or long-lived, depending on the nature of the need or opportunity. Task forces are formed organically from members of affected teams to solve for problems that are existential to any one team. For example, suppose three teams conclude that they need a new class of capability that is outside the boundaries of all of them. They could elect to form a "task force" in the form of a long-lived team, staffed by reassigning members of their respective teams to this new one on a full-time basis. Not all task-forces are long-lived and full-time, of course: a task force to address a simple challenge might require each person commit only 1 day each week for a month, allowing them to otherwise remain focused on their line responsibilities.

Another other exceptional form of communication are inter-team conflicts. For example, team A elects not to prioritize something really important to team B, and team B lacks the resources or capability to do it for itself. Without patterns for extraordinary circumstances, it would end with a very grumpy team B. Inter-team conflict is handled through mediation and adjudication protocols. At the first stage of mediation, an acceptable 3rd party - that is, someone who is not a stakeholder in the conflict - is asked to mediate a decision. If the conflict is still not resolved following the initial mediation, a committee of 3rd parties are formed to arbitrate a decision. This provides a fair hearing among peers without the need to resort to authority given through a hierarchy of control.

Finally, the organization of autonomous teams needs mechanisms for aligning the strategic goals of the organization with team and individual execution. Appreciations provide connective tissue among strategic, team and individual goals and objectives, but they still need to be reinforced by executive management. Intermediary (layered) objectives have a tendency to change the interpretation of organizational goals, and therefore the goals of each individual.

Alignment is achieved by telegraphing executive intent throughout the organization. There are techniques popularized by a number of firms - OKRs, V2MOM, and the Big Bets Spreadsheet - and probably many others. Whatever the mechanism, their purpose is to communicate unambiguous goals to give each individual a clear means of reconciling a decision - why, what and how - with an outcome that advances the organizational goals. This creates direct line-of-sight between effort and result - and therefore tactical action with strategic outcome - and allows the organization of autonomous teams to function without an excessive number of people in low-value supervisory roles.

With the right set of characteristics, then, an organization of autonomous teams can reach scale, even in complex environments. But it clearly has a vastly different operating model to the traditional control style imposed over the machine-like organization. Is the labor force equipped for this? Is leadership? We will look at these questions in the next post.

¹ Susman, Gerald. Autonomy at Work: A Sociotechnical Analysis of Participative Management Prager Publishers, 1976.

² There is no guarantee of control, of course. An individual team can still thrash or produce unwanted product.

Organizing for Innovation, Part III: A New Metaphor

Before looking at autonomy at scale, we need a different understanding of what an organization is. This matters because the way we perceive an organization will determine our interpretation of what "good" and "bad" look like.

Some years ago, I wrote that management thinking is still dominated by the "Freds": Frederick the Great, who's Prussian military structure became the model for the modern organization; and Frederick Taylor, a pioneer of scientific management. Frederick the Great organized his military to function like a machine. Frederick Taylor optimized performance down to the task level. The organization-as-machine metaphor has dominated management thinking for well over a century.

A machine is optimized to require the least amount of labor to produce the highest volume of throughput possible with minimal waste. Machines are designed such that each of its component parts performs specific tasks in a consistent and repetitive fashion. When organized into an orchestrated flow of execution, a machine will yield a high volume of consistent output.

Machines are made for optimal performance within a limited range of environmental variation; they are not adaptive to their environments. If something changes in the internal or external environments, the machine will perform at a less-than-optimal state. An exceptional condition to those the machine was designed to operate in will cause an error in execution. Exceptions are subsequently a source of inefficiency to a machine: the machine's purpose is the consistent and repetitive execution of tasks; exceptions inhibit that execution. An exception must therefore be contained so that the machine returns to efficient execution as quickly as possible.

If a lot of things change in the environment, the machine will completely break down. The machine is not designed to intelligently adapt, it is designed to single-mindedly execute.

The organization managed as a machine may achieve optimal performance, but at the cost of adaptability. We need a different metaphor for the organization: without one, we're doomed to end up with what we've already got. If we see an organization as a machine, we will define it in terms of efficiency: what gets measured is what gets managed, and when we apply old-style thinking we come to rely on old-style managing. If the organizational goal is innovation, we need the organization to have the characteristics described in the previous post in this series: it must be attuned to its environment, and it must be highly adaptable to it.

An more appropriate metaphor for the adaptive organization is to think about the organization as a brain. The brain is a highly adaptable organ:

• While different regions specialize in different activities, control and execution are not localized - regions are closely independent and capable of acting on behalf of each other when necessary
• Memory is distributed, not localized
• Robust connectivity allows for simultaneous processing and awareness of what is going on elsewhere
• Cross-connectivity creates redundancy that allows the brain to operate in a probabilistic rather than a deterministic manner, allows room to accommodate random error, and creates excess capacity that allows new functions to develop¹

The brain is designed to facilitate the process of self-organization where internal structure and function can evolve along with changing circumstances; machines do not do this.

The manager in the organization-as-brain is concerned with redundancy of capability at atomic and coarse levels; recording, curation and accessibility of institutional memory; and unencumbered communications (no strict hierarchy) that are highly contextualized (explain why you want what you want). These are anathema to the manager who's goal is efficiency of execution. Redundancy is paying for something twice. Memory only matters to the most senior people as it affects decisions with long-term ramifications; in the organization-as-machine, those decisions are exclusively their purview. Open communications creates a lot of noise that interferes with orders from management. Having to explain why somebody needs something wastes time.

Traditional organizational thinking inhibits the conditions that create innovation. To have any reasonable expectation of innovation, we need to have the right expectations of how the organization functions if we are to manage in a way that fosters innovation.

Or course, a business is not managed by metaphor. But the way an organization is understood determines the way it is managed. A mindset rooted in learning rather than efficiency provides a set of "first principles" against which measurements and management decisions can be reconciled. It is therefore important to have the right mind-set about the nature of organization to understand how the organization of autonomous teams can exist at scale.

With these goals in mind, the characteristics of an organization of autonomous teams at scale become easier to understand. We'll look at those in the next post.

¹ Morgan, Gareth. Images of Organization. Sage Publications, 1986.

Organizing for Innovation, Part II

Last month we defined autonomy by the classes of decisions that are devolved to the team level, specifically that the smallest organizational unit - a team - has the ability to decide what it should do, can do, and will do. Looking at it this way makes clear the sharp differences between autocratic and autonomous management philosophies. It also helps us to understand that there need to be very special conditions for autonomy to succeed, even on a small scale.

It seems plausible that autonomy can work among a small group of people having a natural predisposition to collaborate and low asymmetry in their depth of skills and knowledge. But there has to be more to it than just a handful of similarly talented and like-minded people working together. If there isn't, than successful autonomous teams are largely an accident of hiring, and not a replicable phenomenon.

According to Morgan, there are four things that characterize an autonomous team.

One is redundancy of functions. Team members have the skills to be able to perform each other's jobs and substitute for one another when necessary. They are called "redundant" functions because each team member has skills they are not using for the work they are doing at any point in time (e.g., coding a new feature doesn't require a change to the build script). A team of poly-skilled people is itself an organization that is flexible enough to reorganize down to its most atomic level - the individual contributor. It adapts naturally because "[t]he nature of one's job is set by the changing pattern of demands with which one is dealing."¹

By comparison, a team of specialists can be an autonomous unit when the external environment is stable, but it cannot sustain autonomy in the face of changing conditions because specialists lack the ability to adapt. When a specialized skill becomes unnecessary, the specialist becomes redundant along with it. A revolving door of members destroys the cohesiveness of a team.

The lack of individual adaptability also creates apathy within each member of the team. Problems such as poor quality or long time-to-market are seen as "someone else's problem" to solve because specialists working on the line don't know, or don't care, or don't have the authority to solve them. As a result, "[a] degree of passivity and neglect is thus built into the system."²

The team of specialists therefore lacks the capacity to self-organize because its members cannot change their job to reflect the changing patterns of demand, and because each member is invested in their skillset more than the team itself. Fixing problems within a team of specialists must be initiated and controlled by higher authority that exists outside the team. In dynamic external conditions, a team of specialists is doomed because the whole will always be less than the sum of its parts, while a team of generalists will acquire the skills and knowledge it needs to solve whatever the problem at hand may be.

Another characteristic of autonomous teams is requisite variety. A team's internal capabilities must mirror the breadth and depth of the environment within which it functions if it is to deal with challenges and opportunities posed by the environment. That skill variety must exist within the team itself so that it can be directly applied where and when it is needed.

A team lacking diversity of function must depend on others so that it can respond to environmental challenges. That dependency impairs a team's ability to self-organize and act, and therefore erodes its autonomy. For example, a team that develops an appreciation for something it should do will be inhibited from doing it if it has to negotiate with other teams for skills it does not have itself.³

Satisfying requisite variety is where technology platforms enable autonomous teams. While it is true that it is people and not assets who innovate, the assets can enable or prohibit such innovation. Teams that can consume components produced by others in a self-service manner do not suffer a dependency. The more comprehensive the components available for consumption, the greater the requisite variety a consuming team can possess, the larger and more complex the environment a single team can engage.

There is more to requisite variety than just skills and capabilities. It also makes a case for human diversity within a team. The appreciations a team develops are richer and more nuanced when they are recognized and crystalized through the diversity of its participants. Another way to look at it is, a homogeneous team will develop homogeneous solutions, and through a lack of human diversity will be structurally blinded to both opportunity and threat. By way of example, I once worked with a bank that was slow to realize that the average age of their employee matched the average age of their customer, that the average had been steadily rising for many years and was now well above the national population average. Year-on-year growth of assets under management looked spectacularly good, primarily because wealth distribution overwhelmingly favored the baby boomer generation. Unfortunately, it completely masked a dearth of new customer acquisition. Along the way, they had become generationally tone deaf, failing to develop experiences and products that appealed to younger generations and subsequently grow their customer base.

The next characteristic of autonomous teams is minimum critical specification. Vague charters and ambiguous boundaries create the capacity for self-organization because they build-in the expectation that teams are responsible for self-definition. A team cannot rely on management edicts that tell them what to do and how to do it. A team must instead define itself through practice and inquiry. General guidelines give a team an abstraction that they must constantly solve for, bringing them face-to-face with the appreciations, or "why" they do or do not do something.

Telling a team precisely what to do robs it of the capacity for self-determination and self-organization because it locks them into a swim lane. A team that is precisely chartered is institutionally specialized. We saw earlier that a team loses adaptability when its individual members lack redundancy of function. In a similar fashion, an organization loses adaptability when individual teams lack minimum critical specification, because teams themselves are stripped of their capacity to adapt based on what they see on the line.

Finally, a team must be capable of learning how to learn. This is also known as double-loop learning. Single-loop learning is the ability to detect and correct deviations from the norm, responding to threats to contain and minimize the impact of exceptions. In double-loop learning, a team is able to analyze a situation in its totality and question the relevance of the things that it does as well as the need to do things it is not doing. Single-loop learning is concerned with staying on-course. Double-loop learning is concerned with determining whether a team is doing what it should be doing in the first place. A well-functioning Agile retrospective is an example of double-loop learning.

Both minimum critical specification and learning how to learn point to the need for abstract thinkers, people who can understand a situation and adjust accordingly. Large ex-growth enterprises are operating companies, not developing companies. Operating companies need efficient execution, so they are populated with concrete thinkers, people who are conditioned through incentives and rewards and professional certifications to keep the ship sailing "steady as she goes". Abstract thinkers are constantly questioning why and looking for the right course of action based on all available information. To the concrete thinker, an exception is a problem to be contained. To the abstract thinker an exception is an opportunity to learn.

These four characteristics - redundancy of function, requisite variety, minimum critical specification and learning how to learn - make it possible for a team to self-organize, self-direct and self-regulate in response to changing external conditions. It is not difficult to see how these form the core characteristics of autonomy. It is also not difficult to see how their respective antitheses - specialization, dependency, precise chartering and single-loop learning - are the defining characteristics of the sclerotic organization.

Combined, these characteristics create what Susman⁴ calls "learning cells" within an enterprise. While they form the basis of the autonomous team, a cell does not simply multiply to form a more complex organization. We’ll next look at what it takes to scale the learning organization.

¹ Morgan, Gareth. Images of Organization Sage Publications, 1986.

² Ibid

³ If "does not have" is institutionalized as "can not have" through shared services - for example, because of an acute shortage of supply, or because those functions are used as control mechanisms to "protect production" - then the pretense of "autonomy" is a veneer over the management anti-pattern of "responsibility without authority".

⁴ Susman, Gerald. Autonomy at Work: A Sociotechnical Analysis of Participative Management Prager Publishers, 1976.

Organizing for Innovation, Part I

Innovation happens through people, not assets. Assets can be an impediment to innovation: software that is brittle, monolithic, poorly encapsulated, or high-maintenance inhibits creative new uses of it. But assets don't innovate by themselves. Innovation happens through the people you have.

We saw last month that innovation is stifled where management's prevailing goal is control. If we want innovation borne of individual creativity, the reasonable thing to do is to look at organizational structures of autonomy and devolved decision-making. Unfortunately, as we saw two months ago, there are no formulas for devolving decision rights. We also saw there are few reference implementations, and no objective measures that show autonomous structures outperform command-and-control styles. Deciding to devolve requires unflinching conviction that it is the right thing to do, and the intestinal fortitude to muddle through what doesn't work to figure out what does. Because there are no half-measures of devolution, the stakes are high: by choosing to do this, you are betting your career and possibly the entire business on its success.

To better understand devolved decision making, it helps to understand the classes of decisions that define autonomy. According to Susman, there are three:

Scope	Nature	Environment	Artifact	Hierarchy
Institutional	What should be done?	Accommodate or defend against what it cannot understand or cope with	Appreciations	Board
Managerial	What can be done?	Decisions are uncertain and highly reactive	Strategic plans	Senior management
Technical	How will it be done?	"Supervisors of risk": decision making is fluid and creative	Implementation plans	Middle management

Source: Susman, Gerald. Autonomy at Work: A Sociotechnical Analysis of Participative Management

It is conceptually easy to understand how devolution works in small companies because the distance between decision makers and decision executors isn't very great. Start-ups don’t have large boards and employees take direction directly from the founder, who is less concerned with precision execution than finding things that drive usage and growth. Senior technology leaders who decide on the “how” are also the people who implement the “how”. There isn't much distance between the Chief Executive and the Chief Cook and Bottle Washer.

The larger the organization, the more polarized the control over each decision class. Appreciations - why should we do something - are the provenance of the board, who are few in number and very far removed from the insides of the company and the ecosystem in which it functions day-to-day. Questions of “what” are held tightly by management, providing a means of co-opting the board in assessing how well management executed, not necessarily on the success it achieved in exploiting the appreciations the board set forth. Held to performance targets from management, and saddled with lowest-common-denominator rented labor (thank you procurement departments everywhere for dehumanizing the secondary labor force for nearly two decades now), questions of “how” are similarly held tightly by technical managers.

The more disenfranchised the line - as in, the greater the extent to which individual employees are only permitted to do exactly what they're told to do - the harder it is for anyone to fathom a devolved model, let alone function within one.

The gulf between "stay in your lane" and "chart your own course" makes clear that there is much more to devolving authority than investing small teams with the responsibility of figuring out what they should do, can do, and will do. In part II, we'll look at the organizational characteristics of a self-directed team, one that functions in a genuinely autonomous manner. After that, we'll look at autonomy at scale: what needs to be in place for autonomous teams to function cohesively in a complex corporate ecosystem.

Innovation Versus Control

Firms in industries ranging from financial services to retail pharmacy to fast food aspire to be "platform companies." In the minds of their chief executives, the emergence of Amazon and the evident superiority of platform economics make this necessary for their continued survival. It is also a good story to tell Wall Street as it allows a firm to create the aura of being the technology leader in their space while trafficking in the success of companies like Amazon.

"Platform" is conceptually conveyed as a technological phenomenon. But it stands to reason that the defining characteristic of the platform organization is neither the technology assets that they produce (e.g., friction-free consumable primitives), nor how they produce them (e.g., lean and agile process). The benefits of a platform are only yielded if the creativity and imagination of the rank and file can be unleashed through those assets, to experiment, learn, and implement quickly. This means devolving decision rights far down into the organization, a.k.a. autonomous teams.

I've been brushing up on organizational behavior theory, and during my research I came across this paragraph. There is a lot of wisdom condensed into these two sentences:

In general, the longer the time period required for the consequences of strategic decisions to be realized and evaluated, the less flexible are resources for commitment to alternative objectives. Furthermore, (1) the longer the time period in which strategic decisions operate as constraints on the decisions made by technical-level personnel and (2) the lower the complexity of the tasks required to carry out operational plans, the more likely that operational planning will take place at a higher level.

-- Gerald I. Susman, Autonomy at Work

The first sentence neatly captures why things like Agile and Continuous Delivery and Lean Startup are so appealing. We reach critical mass of feedback on a strategic imperative - and therefore judgment on the wisdom of that imperative - more quickly with lots of frequent deliveries of small but business-valuable things than we do with infrequent, large deliveries of comprehensive business solutions. The sooner we reach the inflection point where a body of feedback confirms or contradicts a strategic decision, the more quickly we can move on to the next phase of our strategy, or change course. This separates the sclerotic laggards from the adaptive innovators. In addition, the presence of continuous market intelligence serves to separate the agile and adaptive from the strategic flailers.

This is intuitively obvious, but seeing it in black and white serves as a means test for the fulfillment of business strategy: is a firm asserting, confirming, or just guessing at what the market will buy?

The second part of the paragraph helps us to better understand the organizational dynamics within a small and innovative company versus those within a large integrated program team or an enterprise.

The first part is simple enough: the longer it takes to realize a strategic imperative... Longer is bad, check; already established in the first sentence. The second part is where it becomes interesting: and, the simpler the tasks required to deliver that strategic imperative... This statement is an indictment of the labor carrying out those tasks and the management defining them.

All together, the second sentence tells us that a long-lived initiative expected to be fulfilled through simple tasks relegates executives to the role of supervisor.

This is a damning statement in a number of ways.

The moniker "executive" is highly relative, potentially to a point of meaninglessness. The greater the degree to which technical execution is decomposed into simple tasks, the higher up the responsibility for operational planning. The higher up the responsibility for operational planning, the less meaningful the title of the person doing that planning. C-levels engaged in day-to-day prioritization and resource allocation are not executives. They are mid-level managers who have benefited from title inflation. It also means that the scope of executive decision-making - strategy - is concentrated in just a few hands. This renders quite a few people executives in title only, and deprives a company of its next generation of leadership by stifling their formation.

Anyone touting the potential for innovation from a delivery team engaged in task execution is living in a world of make-believe. Innovation stems from the combination of autonomy and complexity: give a team the freedom to solve a complex problem any way they see fit, and they are likely to come up with something novel. A system based on completion of simple tasks deprives a team of any complexity to sink their teeth into. Additionally, a system of rudimentary task completion is inherently a control system, which has zero tolerance for independent thought or action that is off-plan. Innovation is scarce where control is the priority.

Enterprise-y Agile processes function as systems of control, not innovation. Any system that adjusts the work to suit the labor instead of adjusting the labor to suit the work will require a high degree of centralized control. Enterprise Agile processes tolerate, and even advocate, decomposing work into tasks and assigning them to specialist labor. This values the control of labor over the creativity of labor. Per the previous point, technical-level employees are systemically disenfranchised. A system based on control through tasks offers no leeway for devolved decision rights; the only right an individual has is to complete the tasks they've been told to complete. This makes enterprise Agile processes more prone to suppressing than unleashing innovation.

The dynamics of small teams in small companies are not directly transferable to small teams in large enterprises. Small teams in small companies have high degrees of overlapping responsibility, little tolerance for specialization, light processes, and engage in high-bandwidth, omni-directional communication. Large organizations codify things such as roles and responsibilities, career development, processes, and work (e.g., technology) guidelines, and engage in low-bandwidth, hierarchical communications. In small companies, trust is largely based on the expectation that everybody will do whatever it takes to achieve a common outcome; in large companies, trust is largely based on the expectation that specialized people respond to precise requests with precise responses. Team dynamics are functions of HR structures, organizational values and systems, communication patterns, and ingrained behavior patterns, all of which are highly resistant and even subersive to change when they have decades to develop within a company. The executive in a legacy enterprise who says they want to transform the company into a "start-up" betrays their naïveté of the magnitude - and unlikeliness - of that task.

The paragraph at the beginning of this post captures what many in the tech biz have experienced for decades. Since the 1990s, enterprise IT has been a story of scale. As it scaled, it became more prominent on the income statement, and was forced to place a premium on control. Occasionally, it basks in the reflected glory of innovative consumer technology firms, or gets elevated by a CEO as a source of untapped potential. Unfortunately, enterprise IT has never been able to reconcile an expectation for innovation with the fact an over-emphasis on control gives everybody in management a demotion, suppresses innovation, and stifles attempts at organizational renewal, all while holding a company back from fulfilling its strategic potential because it takes such a long time to get anything done.

The most interesting thing about that paragraph? It was first published in 1976. Industrial, not tech firms, were the prominent companies of the time. The lessons remain the same.

You say you want a devolution...

"This isn't to say that alternative approaches to management are dead, or that they have no future. It is to say that in the absence of serious upheaval - the destabilization / disruption of established organizations, or the formation of countervailing power to the trends above - the alternatives to the Freds will thrive only on the margins (in pockets within organizations) and in the emerging (e.g., equity-funded tech start-up firms)."

-- Me, September 2013

I wrote that nearly 5 years ago. That previous summer I cracked the spine on some management books I had last read a quarter of a century earlier. When I first read those books in the 1980s, there certainly did seem to be a management revolution afoot. In the late 1970s, large industrial firms in the US were plagued with quality and performance problems, a rank-and-file that was fully aware of but apathetic to them, and management that was clueless about what to do. The epitome of the industrialized era in western nations turned out to be a company that would systemically disappoint both customer and investor alike. The long dominant organization-as-machine model was commonly perceived to have matriculated to a state of intellectual bankruptcy. Out went command-and-control, in came employee empowerment and team autonomy. Meet the new boss!

Yet when I read these management books anew in the early 2010s, it was clear that the revolution had been stopped dead in its tracks somewhere along the way. Same as the old boss!

I have had reason to re-visit this recently, this time in the context of enterprise technology platforms. A company that develops recomposable, atomic components that can be consumed in a self-service manner by other developers can help to yield more coarsely-grained solutions more quickly. Making those coarsely grained solutions recomposable components as well should enable an organization to create with both greater ambition and speed.

The objective of a platform is not to build both big and small things more quickly or to build more efficiently, but to create more effectively. A platform should allow for a greater number of experiments and more comprehensive feedback. Employees closest to an opportunity - current and potential consumers, technology, competitors, people and capital - are the ones best positioned to pursue that opportunity through exploring, learning, and adjusting. In an emerging area of business or tech, a local team muddling through stands a better chance of success than a distant management imposing its will over a market. In practice, muddling through experiments and feedback requires some degree of authority devolved to the team level, so that a team can decide and act for themselves.

The notion of authority devolved to the team level brings up the question of the autonomous organization yet again. Plus ça change...

The same old idea comes with the same old questions. What does an organization of autonomous teams look like? Can it work? How does it scale?

Before we ask, "can an organization of autonomous teams work?", we have to ask, "what does autonomy at team level mean?" Does it mean the authority and responsibility for what they do and when they get it done? Does it include design and architecture? Can they act on things that are nominally the responsibility of other teams? Do they get to pick and choose the people on their team and the providers they source people from? Do they have to secure their own funding? Who do they answer to? How are they measured?

It may mean all of these things, or it may mean just a few. Autonomy is in the eye of the beholder. To some, just having operational autonomy - authority over what, when and how a team fulfills delivery goals - is sufficient. To others, operational autonomy without owning the P&L and balance sheet - everything from capital to compensation levels - is merely responsibility without authority under the guise of self-direction.

Every firm that has gone down this path has come face to face with the same questions and challenges. Every firm of any scale that has achieved any degree of success has ended up with some hybrid implementation: some things are decentralized, some things centralized; some for a short period of time, others for a longer period of time, and some permanently. For example, we want teams to be responsible for the production operations of their creations, but we must first incubate an ops capability; once we are comfortable that ops has completed its gestation period it will be broken up and absorbed into the line teams. However, to alleviate administrative burden and to avoid violating labor laws we will have a centralized HR function, but we do want ideas to compete for funding, so we will have utility and risk capital allocation processes.

One question, many different answers, and answers that change at different points in time as circumstances require or allow.

When there are many different answers to a single question, it is the wrong question to ask. Looking for specificity where there is none will only sow seeds of confusion and ultimately doubt. And, while there is plenty to be learned from the experiences of others, self-reported testimony must be taken with a grain of salt, and the success of others comes with no guarantee of portability.

A better question to ask is, how convinced are you that team autonomy is a solution to whatever challenges you face? You need to be overwhelmingly convinced that it is, because you need a high tolerance for the ambiguity, uncertainty, and constant adjustments and experiments you will have to run to find and maintain the right balance - that is, construct the right hybrid - for your set of circumstances. You also have to be comfortable without a lot of hard evidence that it solves whatever you had hoped that it would. Even had you not devolved a greater degree of decision-making to the team level, that product might have been a success, that innovation might have emerged, those employees might still have joined your firm. Can't prove the counterfactual.

If you are convinced, and decide to add your name to the list of those that have elected to crack this nut, the operationalizing questions are much different. The one that you will ask again and again and again is the obvious: how do we strike the balance: what do we think that hybrid should be today? what do we think it could possibly be? how do we go about figuring that out?

In addition, given the cyclical love-hate relationship with devolved authority, you must also ask: what makes it more likely, and what makes it less likely, that it will have staying power in your organization?

And you may ask yourself, how did I get here?

It quickly became clear that the problem was not to explain why the market was in decline. it was to explain why the market had ever been so large in the first place.

— John Kay, Merry Christmas, whether or not you celebrate it with a sherry

Managers become interested in innovation when their company’s fortunes start to wane. Innovation is a hoped-for remedy to arrest the decline, spark new growth, and convince nervous investors that management is up to the task.

I have written previously that executives looking for business innovation should not start by looking at technology, but at socio-economic changes that can be exploited or responded to in part or in whole by a technological solution. For example, what makes the sharing economy possible is a willingness for people to monetize their vehicles, home, and spare time because real wages have been stagnant for over a decade and homeowners are underwater on their property mortgages. Similarly, what makes robo-investing viable is a change in investor attitude which once eschewed “average” returns but not embraces them in favor of trying to beat the market. In each case, the stage is set for change by socio-economic factors, not apps and algorithms.

But before figuring out what to try and do next, John Kay makes the point that executives should look at the historical context of their own businesses to understand how it got to where it is - or once was, if past its peak of glory - in the first place. A product or market that was simply “of its time” - and regardless how long, whose time has come and gone - will not benefit from incremental innovation and promises only to consume a lot of investor capital in pursuit of "radical reinvention."

Management that understands the socio-economic factors that gave rise to the opportunity for the business in the first place will recognize the change in conditions, monetize the decline if the change is permanent, and respect investor capital by trafficking in facts. As unflashy as it may be, sometimes the best strategy is not a capital intensive boondoggle in pursuit of a product revival, but periodic marketing campaigns that appeal to consumer nostalgia.

Looking for disruption? Don't look to technology

The chattering classes would have us believe that technology disrupts. It does not. Socio-economic conditions change to create an incongruity that is ripe for exploitation. By way of example, the technology to enable the sharing economy existed for years, but monetizing everything from spare time to the spare bedroom only became appealing when mortgages went underwater, wages stagnated, and the labor participation rate dropped. That computer technology was at the center of this disruption should be no surprise given the rise of the Information Age several decades ago. It certainly wasn't going to be steam engines.

Rather than understanding the disruption phenomenon through the lens of change that has already happened, it is worth looking at the change that has not happened but should have given the availability of technology to bring it about.

For at least 40 years now, we‘ve been told that technology will revolutionize education. Kids can learn from home in immersive media-rich environments, receive continuous feedback on their work intertwined with their lessons, learn at their own pace with tutors and resources delivered to reinforce or accelerate their learning, and so forth. And it could: plenty of technologies exist that make it practical for students to learn advanced subjects in virtual environments, tapping into tutors for private study and multi-media libraries on-demand to experience subjects as never before.

But the revolution hasn’t happened. Kids today are still transported en masse to large brick buildings to get talked at for hours on end, just as they have for decades. If better ways of educating the masses are in their second, third, even fourth generation, why are we still closer to the one room schoolhouse than "I know kung-fu"?

We are because entrenched interests create stationary socio-economic inertia that is difficult to overcome. Consider:

1. K-12 education is free daycare: with real wages stagnant, high levels of single-parent households, and record levels of household debt-to-income, most families do not have the luxury of having a stay-at-home parent.
2. Education is publicly regulated, publicly provided, and publicly financed: power dynamics of education are political, not commercial, because politicians define education standards, schools are funded by tax revenues, union dues finance political action committees, and teachers (and bus drivers, and school administrators) vote.
3. Education is big business. Tax reform that targets university endowments has elicited quite a cry from what are arguably hedge funds that happen to be associated with universities (the top 4 US universities have combined endowments over $103,000,000,000). There is also $1,200,000,000,000 in student loans, a debt market that, like all fixed income markets, has an insatiable appetite for growth.
4. School sports is big business at the high school and collegiate levels. To wit: it is a little bit shocking that the highest paid public employee is a professional entertainer, rather than a professional administrator or legislator. Of course, sports is big money to the institutions with limited compensation - scholarships pale in comparison to the television revenues - to the athletes.

The status quo is not without its defenses. This is important to understand because these defenses are a bulwark against disruption. One defense is that community schooling develops social interaction skills. Another is that team activities like sports and music extend the educational experience beyond fact mastery. These justifications are increasingly without merit. There are no social benefits to bullying, peer pressure, and substance abuse among teenagers; clearly, we can create healthier environments for children to come to terms with diversity, open dialogue and complex social interactions than the toxic environment that is the modern education system. And with so many schools cutting back on arts programs and non-revenue sports (every sport but football and basketball), families increasingly have to go private (meaning, pay out of pocket) for their children to be able to participate in them.

The way the status quo in education has been defended is akin to how the status quo has been defended in wealth management. As passive investing emerged as a threat to active management, defenders of active management argued that passive investment can at best yield "average" returns (that is, returns that match the market) - and who wants to be "average?" That sentiment, twined with selective data flattering to returns on active investing (such as Peter Lynch's aggregate performance and selective years from selected funds), kept money in active for decades despite a preponderance of evidence showing superior performance of passive over time. Stationary inertia is not only quite powerful, it is vigorously defended.

It isn't difficult to imagine just about all primary, secondary, and 100 and 200 level university courses delivered digitally: there just isn't a lot of room for variation in teaching Principals of Financial Accounting I, and how many ways can we dissect James Joyce that machine learning can’t match? And, although there would still be a need need for physics, chemistry and medical labs, it would not be necessary at the basic levels: while there is quite nothing like playing with chemicals, a lot of STEM experiments can be modeled in virtual reality, allowing a student to live like Wile E. Coyote without having to depend on cartoon physics to survive the experience.

Technology may have influenced education, but it hasn’t transformed it. At best, technology has been co-opted to reinforce the classroom model. The technology exists today to make primary through associate degree education a utility. If unleashed, technology would allow for much more advanced and exploratory work at the boundaries of research. But something has to threaten the easy money in education before that happens. Technology cannot do that by itself.

The Would-Be Innovator's Dilemma

It is appealing to think of your trading partners and competitors seeing your company as an industry leading innovator. More soberly, you know it's only a matter of time before the fundamental economics of your business shift not only out of your favor, but out of what you've always known them to be.

Unfortunately, as we saw last month, innovation is an increasingly expensive game. You aren't the pied piper who will lead industry change, nor can you afford an escalating arms race developing weaponized technology. How do you play the innovation game to win if you don't have infinitely deep pockets to finance voyages of discovery and if you don't have the clout or the charm to convince others to finance your vision?

Do You have the Financial Profile of an Innovator?

It is unrealistic to ask a debt-laden utility to suddenly find its creative mojo. Because it must extract maximum cash flows to service its capital structure, it is organized for maximum operating efficiency. Given that efficient utilities are intolerant to irregular operations, it goes without saying that they are similarly allergic to excessive bouts of creative thinking.

Companies that loaded up on debt in recent years have taken themselves out of the innovation game. Innovation is risk, and equity - not debt - is risk capital. A company that is serious about innovating cannot be beholden to investors demanding predictability. It must have sufficient high-risk capital to engage in high-risk investing.

Changing capital structure doesn't imply foregoing operational discipline. Ambitious innovation - that is, not the incremental kind - is expensive. The bigger your war chest, the longer you can stay in the game. Not to mention that having positive cash flows from operations means not having to beg investors for cash infusions just to keep the dream alive. Retained earnings are investment capital with the lowest cost of capital a firm can get. The operative word is "retained": operational efficiency doesn't fuel innovation if cash is pledged to investors. In exchange for foregoing distributions, you have to convince investors that they are wagering on value through innovation.

Before you go hunting for innovation, you must first thoroughly understand the financial incongruities in your business model that benefit you so that you can be prepared for them to disappear and, more importantly, set the terms for you and your industry peers for sacrificing them. Technology drives out inefficiency. For example, software firms have had to move away from a lucrative license revenue model (pay up front) to a metered cloud-based subscription service (pay as you go). When you go looking for innovation, expect that you will open Pandora’s box and unleash the commercial forces that conspire to contract those incongruities. More importantly, prepare your own company for the evaporation of easy money and prepare it to compete on different commercial terms - before someone else forces them on you.

Know Your Business, Know Your Commercial Ecosystems

It is alarming how much business knowledge erosion has already occurred. Some because of attrition, some because of acquisition, and some because expensive knowledge workers were swapped out for cheaper labor paid to simply turn the crank. Whatever the reason, you can't have much hope of ambitiously innovating if you don’t have people who know why your customers derive value from you for the things that you do.

That knowledge only covers the current lay of the landscape. You have to have people who understand customer and supplier needs as well as the needs of those who could be but are not doing business with you. You may have deep insights and lots of data about a universe of companies you work with today, but that does you no good if you’re not winning the business of people who will do business with you tomorrow. You can find that out through experimentation, but experimentation without context is just guessing. Context is tribal knowledge. It's hard to win the business of a new generation of buyer if none are members of your tribe (i.e., you don't employ anyone of that generation).

If the definition of value and the foundational economics are are being blurred in your industry, you probably can’t project your idea of the future all alone. That means selling your ideas on partners and convincing them to move in concert. That requires a thorough understanding of your trading partner’s businesses so you can explain why the innovation that is good for your business is also good for theirs.

Build Versus Buy Versus Co-Opt

We tend to think about innovation as something we have to go out and make. Big pharma showed that M&A can be an effective substitute for R&D. One is not a shortcut to the other as each is defined by a complex set of competencies and capabilities. Making requires competency in experience empathy, product management, design, analysis, engineering, analytics and many other skills. Acquiring requires competency in valuation, negotiation, financial engineering, integration (which itself extends to things like corporate culture) and rationalization. Do not go in pursuit of either making or buying in a big way until you build confidence that you have capability to execute competently in a small way.

Of course, the business landscape is littered with expensive technology boondoggles gone awry, and M&A is more likely to be value destructive than value generative. Sometimes the best use of capital is co-opting an emerging threat to the prevailing economics. Whether banks were ever quaking in their boots at unregulated peer-to-peer lenders stealing their most lucrative borrowers, the banks certainly did a good job fueling P2P lending growth to a point of dependency. The tightening of the credit cycle caused banks to pull back their buying, creating a crisis with peer-to-peer lenders that resulted in those would-be disruptors filing for banking licenses themselves - becoming the very thing they set out to disrupt in the first place. Instead of competing by creating a competitive marketplace or buying an emerging competitor, the established banks effectively greenmailed the threat.

Restructure to Innovate, then Innovate or Die

If the easy money of the idea economy has long past, and if “big ideas” are the only ones that will move the needle, then innovation is not incremental but wholesale in scope. This makes it a serious investing activity that encompasses the enterprise, not a lab in the business or a side R&D function. That requires the appropriate capital structure and investors (innovation is risk), the right knowledge (invest in what you know), and the acumen to choose when to act through making, acquiring or co-opting. Leading innovation doesn’t have to mean creating “the next big thing”, but it always means being prepared to exploit it.

Innovation Exhaustion

"We've tried nothin' and we're all out of ideas."

-- Ned Flanders' mom, "The Simpsons", season 8 episode 8: Hurricane Neddy

We're constantly being told by the popular business press that we live in an "ideas economy," where survival is a function of disruption because consumer behaviors and emerging technologies are conspiring to obsolete the economics of established businesses. There are plenty of examples - music publishing, mass-market retailing, local transportation - where new entrants have left a wake of creative destruction in their path.

Management consultants love to trot this stuff out, because fear of the unknown (who will destroy your company?) twined with tantalizing prospects of runaway riches (you could be the next air-b-n-amazon-uber-twit-book!) make for eager and pliable clients.

What those management consultants don't tell you is, it's expensive being in the ideas business. R&D isn't cheap: there is far more demand for engineering labor than there are engineers to be hired. And, a lot of R&D is terminal: you have to try a lot of things before you find something that pays for itself. Costly research that tells you only what not to do is cold comfort when you're trying to figure out what it is you should be doing.

It also appears that the economics of the "ideas economy" have been slowly eroding for a very long time. According to this paper, the number of people working in research has grown at a much faster rate than economic growth. Consider semiconductors: "The number of researchers required to double chip density today is more than 18 times larger than the number required in the 1970s." Inflation has risen only 6.5 times since 1970. Yowza.

Of course, that could mean there are too many slackers in research jobs, or that we have more eggheads than our economies can afford. But the real culprit seems to be a scarcity of ideas: they're just getting harder to find. As Izabella Kaminska wrote in the FT, "if research productivity is declining it stands to reason it is being offset by increased research effort. This essentially implies that it is getting harder to find new ideas as research progresses."

A big reason for this is economic maturation. There were far more impressive productivity gains in the early stages of the industrial revolution and microcomputer revolution than there were later in their respective lifecycles: the once factories were mechanized and all the back-office accounting computerized, the big and easy gains were made.

But even Amazon is showing signs of innovation fatigue. In the last 6 years, sales are up 5x, but employee headcount is up 10x. Liabilities are growing as fast as cash, suggesting free cash flow isn't improving with time. And, Amazon's growth rate is far lower than what Wal-Mart's was at a similar point in its history. If growth has slowed, capital intensity is up, and total labor spend is up, either platform monopoly economics aren't what we think they're supposed to be or they will take a very, very long time to materialize. This isn't to say Amazon isn't going to grow, or be a threat to traditional retailers and other industries, but it is to say that even Amazon is showing evidence of idea exhaustion.

What about the major disruption that appears to be on the horizon, like distributed ledger technology?

Blockchain could eliminate redundancies across companies, reduce fees for simple transfers, and usher in all kinds of innovation. It can, but the economics won't materialize as rapidly as ideas of yore. As long as the network is stubbornly difficult to secure and access, trust will remain with the institutions using the network, not the network itself. As long as trust remains with institutions and not the network, the institutions will have no choice but to maintain their own ledgers for a long time. That means that companies in ecosystems that adopt distributed ledger technology will find opportunities for innovation and gain some efficiencies, but will not be able to exploit its full potential for quite some time. Innovation and productivity from disruptive ideas, while still present, will fall short of potential.

This is the storyline with all emerging disruptive technologies. We may get autonomous long-haul trucks but we will still require drivers in the cab, we have shared ledgers but a lot of that data will remain duplicated throughout consuming organizations, we allow initial coin offerings but we regulate them as securities. There are economic benefits, sure, but the economic windfall they promise is just out of reach. That revolutionary new economy is delayed at the airport.

The chattering classes are telling us that we live in an "ideas economy" a full half-century after it was ripe to traffic in ideas. A more appropriate term might be the "ambition economy", because to reap the benefits of the possible requires a significant break away from the known and familiar. That's more than innovation driven by a single firm; it requires moving ecosystems of consumers, suppliers and regulators.

For those caught in the crossfire - firms that don't much like the prospect of winning a participant trophy in a costly innovation arms race, and don't have the gravitas to lead ecosystem change - what alternative do you have? We'll look at the options next month.

Partners

"Greed and patience don't live together very well."

-- Keith Jackson, ESPN 30 for 30, Who Killed the USFL?

Businesses rely on a network of suppliers to operate and grow, including providers of components, back-office operations, distribution, marketing, retail, information technology and even office supplies. They do this for a variety of reasons, ranging from areas of specialty (assembling large finished goods is different from manufacturing small, precision components), depth of expertise (some companies are better at selling things than making things), accessibility of labor (difficult to hire in a location where there are too many jobs chasing too few employees), and appeal to the people with the skill set (a wholesaler doesn't offer that much career growth for an attorney).

All relationships, whether personal or commercial, are based on need. A buyer looking for widgets will go to another supplier if they can't get the widgets they're looking for. Similarly, a seller may choose not to sell widgets to a cheapskate buyer, and will find other customers instead.

Although rationally the statement of cash flows should triumph in commercial relationships, we're very often asked to extend our balance sheets to help someone else. In a commercial context, this is the point at which terms like "supplier" and "vendor", "client" and "customer" are ditched in favor of the aspirationally higher ground implied by the word "partner". Rather than evaluating transactionally (this relationship comes at a high cost to me), we evaluate strategically (this relationship is important to me).

Choosing to underwrite a shortcoming in a relationship is to make a leap of faith that there will be tangible or intangible rewards for doing so. The executive who keeps changing the specifications but always gives a glowing recommendation, the company that provides your firm with the annual revenue if not the timely cash flows. A partner puts up with deficiencies because they get much more out of the relationship.

Partnership, then, encompasses more than just a relationship of need, but a relationship worth it to both parties to make sacrifices to sustain. When we partner, we each agree to ebbs and flows in the relationship - "in sickness and in health" - and that we will not merely tolerate, but accommodate. A seller that has to roll somebody off a team because they can't travel; a buyer that has to reduce the amount they spend. In these situations, a partner sets aside the short-term impairment for the long-term benefits of continuity and consistency.

Of course, there are more benefits than merely convenience. Each partner changes independently, and those changes keep the partnership relevant and fresh. In the process, each learns continuously from the other, evolves what they do and matures how they do it. Strong partnerships make stronger individuals.

Partnership implies equivalency. Yet the commercial world is full of alleged "partnerships" that are superior-subordinate, making them inherently unbalanced. Companies stay in condescending or even abusive relationships because they're afraid of the uncertainty of the alternative. Sellers do this because suckling at the teat of easy revenue is far easier than hustling new business. Buyers do this because they feel held hostage by a supplier. Even though it comes at a high commercial cost (squeezed margins) and high human cost (second class status and compromised careers for those involved), such business "partnerships" can last for a long time.

Egalitarian partnership, then, is more often wishful thinking than willful practice.

Whatever else they may do, partners do not try to get the better of one another. If one party feels it has to out-maneuver the other in every contract negotiation, pad or dispute every invoice, cast doubt on quality or contribution well after delivery as a means of finagling a discount, or flaunt payment terms, it isn't a partnership. This isn't competition that makes for stronger individuals and better outcomes, it's subversion that prioritizes individual gain over mutual outcome.

There's nothing wrong with transactional relationships, and if we're honest, most commercial engagements don't have the potential to become genuine partnerships. Partnership is investment, and like all investments, there's only so many you can make and maintain. Over-using the term and confusing one type of relationship for another does the people and companies you do business with a disservice because it implies a commitment to them that you're not making. Transact faithfully with all (the world is a better place when it gets by on trust), and partner intensely with those who equally benefit from your association.

Invest in What You Know

Every day, millions of people buy expensive things they don't know much about: cars and residential homes, enterprise software and entire enterprises. Having a deep pocket - or investiture by people with deep pockets - is the only qualification required for an individual to have buying authority. As we saw previously, emotions have a share - often a disproportionate one at that - in buying decisions. This makes value a relative rather than an absolute concept, and absurd as a summable metric.

When purchases get large, we re-cast them as investments. As assets, acquisitions appreciate in value on their own (e.g., real estate) or they enable us to derive greater economic value than we otherwise would without them: a truck depreciates in value, but it is inefficient to run a flower delivery business without a truck, so having a truck on the asset line of the balance sheet boosts revenue on the income statement. Unfortunately, a lack of expertise in the things that we buy tends to give non-economic factors an important role in the decision. We may know horticulture and the asthetics of flower arrangement but not know much about forecasting operating costs and reliability in city driving, so our business investment comes down to factors like style, comfort, or just liking one salesperson over another.

Purely financial investments aren't immune to this, either. We're not experts in industrials or tech firms or utilities or the ETFs that collect them, so we develop criteria (consistent dividends, revenue growth), create justification frameworks (safety, income), and consult experts (research firms), but in the end we follow our emotions (I soooooooo love their products I'll park my IRA in their stock). We want to equate investing with rationality, but a lack of expertise - and the pressure to make investments - make it anything but rational.

Many people in the tech industry - myself included - have advocated recasting technology as a financial phenomenon that yields returns rather than an operating cost to be minimized. Well, more accurately, recasting some portion of technology this way. We don't need to measure return-on-the-time-and-expense-system: it's a tax on our business and all we want to do is pay as little per staff member as we possibly can. But we can't expect to create high-risk call options (R&D) or make strategic capital allocations (platforms) with stay-in-your-swim-lane staffing and structure. Form follows finance: because of the outsized effect that finance has on operations, we start by changing the funding model, which clears the path for new structure and process. Follow the money.

If tech is going to function as a financial rather than an operating phenomenon, it must take its guiding principles from financial investing. Benjamin Graham implied and Peter Lynch practiced the idea that you should only invest in what you know. Get to know the industry dynamics, the company in particular, and the people operating it before pledging any capital. You'll still have disappointments, but far fewer surprises. This separates thoughtful investing from reckless gambling.

In technology, "investing in what you know" requires substantial business domain knowledge and tech fluency with generous helpings of behavioral science and economics. Successive waves of efficiency gains mean we can't take intimate business knowledge for granted any more. All the organization, process, and ceremonies won't compensate for a lack of these things, the evidence of which is seen in the reference cases of Product organizations that create confusion rather than cohesion, and the large replatforming initiatives that require additional cash calls and goal reduction to be deemed successes. If we're going to "invest in what we know", the leadership imperative is in securing the fundamentals so that we have the basic competencies in place.

But that presents us with a recursive investing challenge. Developing the capability to competently invest in technology is an investment itself, and must be held to the same standard: are we investing in something we know? Do we know what we're looking for in that investment into capability? Or will investments in our future leaders be more emotional than rational?

The Value Myth

When we think about value, we think in terms of hard measures like increasing revenue or decreasing cost, or soft measures like increasing customer satisfaction or reducing customer friction. This all sounds great, but we know in practice that value is not as concrete as we would like to believe: projections are conjecture, there are multiple forces at work that determine the result we get, and counterfactuals can't be proven to know for fact whether we'd have been better off doing something different or nothing at all given how circumstances played out. Good as it might be that value allows people to relate their actions to hoped-for outcomes, it is naive to think that the outcomes will result from the sum of the actions that we take. Business is far more complex and far more messy.

Saying otherwise is disingenuous, because it gives business a theoretical tidiness that it simply does not possess. Perhaps this is inevitable when non-business people like program managers (coordinator-administrators) and developers (engineer-nerds) traffic in business concepts (finance). Whatever the reason, it isn't helpful if tech wants to be taken seriously by the professionals - particularly the finance professionals - who run the business. Showing a direct line-of-sight from tech or process to business outcome sets up tech to get played and manipulated. Going from tech to business value in one step is a short-cut to being relegated by the board; it is not a path to business relevancy.

In this series of posts, I've taken a different tack, focusing on value and worth as behavioral rather than economic concepts. It stands to reason that if value and worth are in the eye of the beholder, their definitions will be heavily influenced by individual biases. Success of any business initiative comes down to behaviors, so the better we understand those the better we understand the complexity of what value really is in a complex business context.

Value is different things to different people for vastly different reasons. Consider insurance claims. During a storm, high winds blow a tree down and onto a house, collapsing a section of the roof. Insurance adjusters don't care about the aesthetics of different colored roof shingles used in the repair. The adjuster only cares that the roof is repaired and the house won't be taking on ballast the next time it rains. The insurance adjuster is under orders to repair the house with minimum impact to the insurance company's cash flow, and is therefore focused on the utility (which is easy to quantify), not the aesthetics (which are not). That the first thing any prospective buyer will point out is that those green roof shingles clash with the existing gray roof shingles appears nowhere in the adjusters "cost of repair" spreadsheet: whether green or gray or fluorescent pink, those shingles will keep out the rain and the snow and the critters. For the insurance company, the asset they insure was repaired with minimum impact on their cash flow.

The same applies to tech. An engineer infatuated with the tech stack supporting a hopelessly implemented feature set. A user clinging to an interface backed by an impenetrable monolith of code. The CFO who is tone deaf to responsiveness and excessive defects, solely because of the price. Try as we like to frame "business value" as an absolute, in practice it is a relative concept, interpreted and reconciled to the motivations and desires of each individual in the value chain. What gets measured is what gets managed, so "business value" becomes the means through which individual value is realized: we need this over-hyped cutting-edge tech stack because it will help us deliver it faster; how conveniently coincident that experience with that over-hyped cutting-edge technology flatters the resumes of the people working on it.

This creates cascading re-interpretations and re-assertions that smother value, ironically justified by the pursuit of value. A firm I audited years ago had, some months prior, formed a cross-functional committee of tech, business and finance to choose a mobile development toolkit. Business believed it needed a mobile solution, tech aspired to create one, but the board didn't share in the enthusiasm. In the end, finance won out, choosing the tool that cost the least but that tech found unstable and yielded software solutions the business didn't much care for. The tool was never used. Instead, developers rolled their own frameworks and infrastructure, below the radar of the CFO and with a wink-and-a-nod agreement with their business partner that they would do so. The purchased framework had negative value to its intended constituents, to a point that tech believed there was more value (and with the complicity of business in the decision, to the business as well) in creating proprietary development infrastructure. Whether spending twice for infrastructure was tech rescuing the "value" jeopardized by a crap product, or tech being intransigent and subversive to the board's agenda, all depends on your definition of value under the circumstances.

We don't win the triple crown of value all that often. Marketing doesn't appreciate losing the pricey boutique firm they could talk to each and every day, but tech looks like stars to the CFO for sending the work to a cheap offshore supplier. The CIO doesn't like being held hostage by employees who used an obscure tech stack in the name of getting something done "faster", only to be making it debilitatingly expensive as they exit the firm and go into private practice. Eliyahu Goldratt pointed out the tradeoff of local optimization for systemic optimization a long, long time ago. Local optimization infiltrates every value calculation, in temporal ways that defy models of value.

We want to believe that "value" is an absolute measure of something that improves the condition of the enterprise: In unitate es virtus. But we know that people have different interpretations and goals that materially impact the business outcome, so value is a weighted sum of disparate, unexposed agendas. The larger the enterprise, the more complex the calculus.

Value is money, and where there is money, there is politics. With that in mind, value is perhaps best understood as something Mike Royko taught us about the fundamentals of politics many years ago: Ubi est mea?

"Where's mine?"

Questions of Value

In March, we looked at questions of worth. This month, we look at "questions of value".

In the dictionary, value is defined by worth, and worth is defined by value. Why ask the question twice? Because even if they refer to the same thing, the words mean different things in different circumstances. In economic terms, "worth" refers to stored value, such as accumulated financial reserves (one's "net worth") or the price we're willing to pay to replace something we already own. We use the word "value" in reference to economic (or other) power unleashed by something that we have or do. An object has sentimental "value" to which we ascribe an inexplicably high economic "worth". An investment in a truck yields economic "value" on the income statement well above the worth we ascribe to it on the balance sheet, because without it we couldn't achieve delivery efficiencies.

Value traffics in moving, worth in storage.

Value is what we're willing to pay for something in exchange for the returns that it provides. We value cars for reasons ranging from their resale value to the status we think they project to the friends and strangers who see us driving it. We value houses for the school districts we can put our kids in, the relative price of houses nearby, their convenience to how we live and make our living, and the status that living in that post code conveys.

In software, we want to make decisions about where we invest based on value. But because we can't predict the future, value is conjecture. This forces us to ask: what defines value? And who defines value?

Value, like love, is a many splendored thing. There is value derived from features, there is value derived from construction, and there is value amplified from not spending too much. An asset that does many things, is low maintenance, and costs little will be higher yield than one that does few things, is high maintenance, and costs dearly. The problem of defining value is the problem of projection because there are no absolutes in those projections.

This creates a bit of a problem, because value is a future-tense term, and we can't know with much certainty what the most important characteristics are to realizing that value. This becomes a big problem when we want to "buy for value". Worth is bankable, if vulnerable to erosion; value is in the eye of the beholder and may never materialize.

Consider a house. Buyers define all kinds of evaluation criteria, things like proximity to public transportation, newer appliances, and rooms and layout that accommodates their possession and lifestyle. But a house is a building and its utility is a function of its construction as much as its design. Since most home buyers aren't carpenters or plumbers or electricians, they're not able to judge quality of the build. They rely on the opinions of experts. Hence we have inspectors, who are licensed in most states and built into residential contract law to provide their expert opinion on the house.

Selection criteria and expert opinions only go so far, though. A homeowner doesn't really know if a house is what they want until they've lived in it for a while, and besides, some of their criteria will be contradictory and some of their priorities will be out of order. Inspectors have limited expertise with building codes, practices and materials, and they're only spending a couple of hours looking over the carpentry, masonry, electrical, plumbing and mechanical of an entire building that took hundreds of person days to build. For all the sweating and scrutiny, at best our opinions tell us that we shouldn't buy something; they don't necessarily tell us specifically why we should. All house purchases are compromises, and in the end, the purchase is made for substantially - perhaps even largely - emotional reasons, and complex ones at that.

This applies to all kinds of purchases where "value" is a factor. Like cars: we develop criteria (seats, storage, zero-to-sixty speed), poll experts (trade press like Consumer Reports and Car & Driver), but still make a decision that is partially - even largely - informed by emotions. Look, it's got four doors, space for the kids & clubs, it's fuel efficient, and will you just look at those shouty rims?

Questions of value become even more conflicted when multiple stakeholders have different ways in calculating value, and ambiguous authority in setting it. We'll take a closer look at that next month.

Concrete Versus Abstract

Until a few years ago, enterprise software development was pretty easy to justify and execute because the income statement was the primary customer. Automating back office tasks, expanding market reach, creating customer self-service tools, even legacy technology replacements were all investments that could be explained in a straightforward manner as taking costs out or capturing revenue that would otherwise have been lost. Business cases weren't all that complex, and results were easy to direct.

The nature of enterprise tech investments has changed. We want consumer-facing tech that is less workflow-driven and more situationally adaptive. We want platforms on which we can quickly build complex applications, not a collection of solutions that we tie together with jumbled integration and overlapping data warehouses. These investments are justified not by cost or revenue, but by a future defined by new consumer behaviors and new services; it is a future we do not control and over which we cannot judge our influence, and by their emergent nature offers no baseline for measurement. As tech investments become more ambitious, their investment criteria become more nebulous and vague, their justification more speculation and hope, and their actual impact more difficult to trace and validate.

The landscape has become far more abstract, too. For one thing, the business threats have become less physical. A business had months to prepare for a rival building an outlet down the street, but can't immediately see (let alone know how to respond to) a purely digital competitor slowly siphoning away customers. For another, the tech is less tangible. Technology was easier to grasp when you could map software solutions to a handful of rack-mounted servers and desktop PCs; try explaining cloud-based AI architecture to non-tech buyers as anything other than a black box.

Yet enterprise execution remains rooted in the concrete. Companies achieve scale and efficiency through disciplined execution: develop patterns of operations, sweat the details, codify procedures, and spread through an ever growing network. The more cookie-cutter, the more predictable; the more efficient, the more cash flow from operations.

I've written before that this encourages heavy levels of debt finance, and that debt finance stifles investment by crowding it out: debt not only consumes cash that could otherwise be used for investment, it discourages high-risk investments with unknown returns. Debt finance binds a company to a tomorrow that is the same as today. Visions are equity plays, not debt ones.

Capital structures aside, there's another aspect to this: enterprise leadership that isn't equipped for the challenge. The enterprise leader must be capable of forming a considered opinion on tech and business matters to know whether someone is feeding them the level truth or blowing sunshine up their backside. That leader must be able to describe the world through the lens of a plausibly achievable future state, not something that comes across as improbable sci-fi, and more substantive than a how-things-have-always-been-only-faster state. That leader must be pragmatic enough to know how to walk the fine line between enabling knowledge workers to hold the future hostage to their subject matter expertise, and overwhelming those knowledge workers with change fatigue, maturing them into future operating leaders of the business.

This requires a leader with depth of knowledge in operational, technical and financial matters. It also requires an ability to think abstractly and translate abstraction into concrete action. He or she will have to articulate an integrated business & technology operating model, restructure finance from cost-driven to investment-driven, and change recruiting and retention and contracting practices. Plus, she or he must be able to explain why the current modus operandi is geared toward running the wrong kind of business (an efficient opco), and what is necessary for it to become the business it needs to be (an efficient opco ingesting the innovation generated by a biz&tech platformco).

This leader cannot over-emphasize one area - the transformation, the vision, the tech, the finance - above others. Doing so creates an imbalance that will lead to organ rejection by the established enterprise. Key enterprise constituencies don't react favorably to having their area of specialization demoted. It starts whispers that "this person leading our so-called replatforming just doesn't get the business" that undermine their leadership.

The hard-driving entrepreneur, the professional administrator, the technology futurist are the wrong leader archetypes. This calls for statesmanship, someone who can project from policy to practice in a complex corporate and competitive landscape, translate goals and changes into multiple business tongues (the executor, the developer, the middle manager), behave diplomatically while remaining above corporate politics, and be patient for new business principles to sprout within the enterprise.

Of course, people who fit this bill are as rare as hen's teeth. Operating companies don't incubate abstract thinkers, they incubate concrete thinkers because they reward concrete execution. Nor does it help that we've taken authority away from middle managers instead of developing them into the next generation leaders.

The firm that does recognize the scope of this leadership challenge but can't staff the role from within or without will resort to the multi-headed leadership team (at least one business and one tech, potentially more from across the corporation depending on the political landscape) and hope that the whole will be equal to the sum of the parts. Conway's Law guarantees that the outcome will be plagued with local optimizations that inhibit - and potentially impair - the hoped for outcomes.

Ambitious transformative tech investments may be viable, and even necessary for survival. They need vision and execution, cooperation and skills. But they're going absolutely nowhere without the right leaders and leadership.