The Agile enterprise needs to balance autonomy with effective alignment mechanisms. Agile strategy provides key alignment mechanisms:
Formulate a vision or “True North” [Dennis 2006] that exerts a magnetic pull
Identify and select the big bets or breakthrough objectives that drive cross-silo collaboration
Cascade objectives down the organization to allocate responsibilities and accountabilities
Monitor strategic measures and KPIs to correct course when results differ from expectations
As described in Strategy, the enterprise strategy will not be effective if it is not communicated, well understood, and deployed. To benefit from strategy, the Agile enterprise must address the issues below:
Strategy that is disconnected from field realities, because thinking is separated from doing
Social dynamics that favor superficial consensus over honest conversations
Unrealistic growth plans leveraged by executives to compete for resources and influence
Strategy formulation that ignores uncertainty or treats it as an afterthought
Inflexible strategic planning cycles that freeze priorities and budget allocation decisions for a year
Planning systems that emphasize strategic decision-making over strategy deployment
Agile strategy shall follow the tenets described in Agile Strategy Tenets.
Strategy formulation is often done behind closed doors in a “strategy room” supported by the corporate strategy function and/or strategy consultants. The world of market studies and operational/financial numbers must be completed by real-world experience. “Going to the Gemba” is a powerful Lean management practice that must inform the strategy formulation process. It means visiting the place (Gemba) where value is created; for example, when clients interact with the enterprise or when employees deliver products or services. “Gemba walks” are comparable to the Management by Walking Around (MBWA) practice popularized by Tom Peter. Data provides a model of the world; it must be completed by real-world experience. The map is not the territory.
Real-world experience can help executives to challenge the mental models that shape their view of competition and of their enterprise. Talking to clients and to employees who are closer to the front line helps to sense weak signals sooner. Asking the right questions helps executives discover the limits of their current knowledge.
Situational awareness is about seeing what is actually happening, unfiltered by existing mental models. It is also referred to as “sense-making”. Design thinking helps to discover and formulate the big problems that challenge the enterprise. It provides a structured process to help executives reflect on the situation and discover what it means.
Agile strategy is a problem-solving approach that focuses on the enterprise’s big problems and breakthrough objectives. It requires opening up the solution space to study different options. It uses design thinking to help inform difficult and challenging problems. Instead of creating strategic plans that are distant from the field, design thinking helps bring together multi-disciplinary teams with different points of view.
The authors of Strategy Beyond the Hockey Stick recommend identifying and debating real alternatives [Bradley 2018]. They also suggest framing strategy around “hard-to-reverse” choices, which are Type 1 decisions; see Architecturally Significant Decisions: "Think of it this way: real strategy is about making your hard-to-reverse choices about how to win. Planning is about how to make those choices happen. But the first step is being skipped too often, even if we try to make ourselves feel okay by labeling the plan strategic”.
When Type 1 decisions are made, the enterprise needs to factor in emergence, which is introduced in Emergence. When undesirable functions or outcomes are not anticipated and managed, Type 1 decisions are likely to result in poor or even disastrous consequences. This phenomenon is generally known as the “law of unintended consequences”.
In order to improve the outcome of Type 1 decisions, leaders should follow an explicit method that takes into account the complexity of the enterprise and its environment, the uncertainty of future events, and the cognitive processes of leaders who make those decisions. Bending the Law of Unintended Consequences describes a method that enterprises can follow to improve the outcomes of Type 1 decisions.
The annual strategy cycle tends to freeze strategy decisions into an annual budget that changes when revenue or profitability targets are not met. Budgets are rarely modified during a fiscal year for strategic reasons. Instead, Agile architecting shall hold regular strategy dialogs.
Chris Bradley et al. recommend decoupling big strategy conversations from the strategic planning cycle: “In fact, trying to solve your big strategic questions and lock down an agreed plan at the same time can be near impossible. You can be sure the urgent will dominate the important, and those big questions won’t get the airtime. Besides, those messy non-linear and uncertain strategic issues don’t fit into the linear world of the 3 to 5-year plan” [Bradley 2018].
Holding regular strategy dialogs should not result in strategy volatility. Strategy should evolve for good reasons. When market conditions change, the enterprise should be Agile enough to adjust its objectives and budgets to consider the new situation. Most of the time, this dynamism equates to battlefield mobility, to use a military metaphor. When more fundamental changes are sensed, the Agile enterprise should not hesitate to immediately start the necessary strategic conversations.
Agile strategy shall decouple strategic priorities conversations from Agile planning, which evolves at a faster pace: “The elements that lend stability to Agile planning are setting clear strategic priorities and defining closely related objectives for teams … The dynamism in Agile planning comes from holding quarterly planning sessions, in which funding is redistributed among business units, and providing teams with support to use funding as they see fit” [McKinsey 2019].
The stable part of the strategy continues to align Agile teams until it has to change due to new circumstances.
The first recommendation of Donald Sull and his co-authors to prevent strategy execution from unraveling is to foster coordination across units [Sull 2015]. This should become a focus of attention to strategy deployment approaches, such as Hoshin or Objectives and Key Results (OKR).
Before cascading down objectives or goals, big problems and breakthrough objectives should be analyzed by cross-functional teams. When root cause analysis identifies hypotheses to be verified and countermeasures to be experimented, it becomes possible to assign objectives down through the organization. This is a recursive process that not only improves the odds of strategy deployment success, it will also likely improve the quality of strategy formulation.
The law of unintended consequences states that decisions to intervene in complex situations create unanticipated and often undesirable outcomes.
In her article, Why Hypotheses Beat Goals [Ross 2019], Jeanne Ross recommends the development of a culture of evidence-based decision-making by promoting hypothesis generation: "A hypothesis emerges from a set of underlying assumptions. It is an articulation of how those assumptions are expected to play out in a given context. In short, a hypothesis is an intelligent, articulated guess that is the basis for taking action and assessing outcomes".
In a complex situation, hypothesizing simple causal relationships may not be sufficient. It is also not practical to experiment with “hard-to-reverse” decisions. The Agile strategist needs new methods to frame critical decisions and prevent unanticipated and undesirable outcomes. Richard Adler proposes a well-articulated and comprehensive method to help bend the law of unintended consequences [Adler 2020].
One of the central ideas of this method is to test drive decisions, much like consumers road test vehicles to get a better sense of how they satisfy wants and needs before choosing which one to buy. Test driving a decision provides insight into its consequences for a business and other parties of interest. This is accomplished by projecting the likely outcomes of implementing that decision over time under a range of plausible future conditions. Test drives for decisions, much like cars, are admittedly limited in scope and offer insights into the future that are imperfect. That said, they offer far better chances for identifying (and improving) a Type 1 decision than the alternative of blindly choosing a Type 1 option without testing it.
Richard Adler’s method combines:
“What-if” simulations and analyses
A decision rule for selecting a preferred decision option
Its key differentiators are:
Capturing scenarios as detailed, scalable, and easy to navigate software models rather than broad narratives
Inserting decision options into scenarios (so that they are actionable)
Projecting the consequences of decision options against the dynamic background of events and evolving conditions with simulations
The mechanical details of modeling and simulating dynamic scenarios are fairly complicated. Richard Adler indicates that: "Fortunately, these details are left to programming and simulation experts; the test drive method insulates analysts and decision-makers from most of the underlying technology".
Simulations must be instrumented with relevant business and technical performance metrics to drive comparative analytics of outcomes. The decision rule identifies an option that is robust in the face of the law of unintended consequences, namely the one that avoids “train wrecks” and which performs better than the alternatives across a spectrum of plausible futures.
Simulations can also be used during execution to periodically re-project outcomes of the chosen decision given the results to date, and scenarios updated to reflect the current situation. In this mode, the method detects emerging problems, prompting and allowing for mid-course corrections to ensure success.
This iterative process drives decision options experimentation – testing and validation, or refinement – in a virtual world. This is similar to conducting Agile experiments, without the high cost associated with a real experiment, such as an MVP.
In summary, this method does not replace Agile experiments, but adds a new way of evaluating – and iteratively refining – decision options, which is well suited to Type 1 decisions.
To improve the odds for the success of strategy implementation, the Agile enterprise shall define an effective system of objectives.
In alignment with the strategic vision, the deployment process starts with the definition of the few top-level objectives which will be further decomposed recursively into sub-objectives. The objectives tree shall contain objectives:
That are result-oriented, expressed as OKRs
Can guide breakthrough actions by opposition to incremental improvements
Are federative and deployable along the management line
To which teams and individuals are able to commit
An example is used here to illustrate a policy deployment anti-pattern. An enterprise wishes to change its positioning toward the high-end market. In order to satisfy high-end customers, it must dramatically improve service reliability.
It is not good enough to assign the same objective down through an organization, as illustrated in Objectives Deployment Anti-Pattern. Why? Because when the objective reaches a lower level of the organization:
The assignee may lack the competencies required to act on the assigned objective
The scope of the objective may far exceed the scope of responsibility of the assignee
The objective may not be specific enough to be actionable at a lower level
The Agile strategist will decompose objectives using the approach below:
Investigate the performance gap
Formulate a causality hypothesis
Conduct experiments to verify causality
Develop scenarios and run simulations to analyze complex situations
Identify and experiment with countermeasures
Objectives Deployment Example illustrates how the “Improve the Reliability of Our Services” objective can be decomposed.
Some of the sub-objectives can be assigned to one team. For example, “Monitor Distributed Systems Effectively” could be assigned to “IT Operations”. Being responsible and accountable for an objective may require a team to manage dependencies with other teams. In this example, “IT Operations” needs inputs and buy-in from “Engineering”.
Other objectives, such as “Shift Toward an Open and Blameless Culture”, are too broad to be assigned to a single team. In this example, the objective will be shared by several teams.
When the objectives decomposition tree is stabilized, two things need to happen:
The definition of the OKRs
A “catchball” process, to gain consensus on the objectives and the target results that are assigned
An OKR spells out the company’s priorities in terms of specific accomplishments and performance improvements. The Objective consists of a clearly defined qualitative change, while the Key Result is a specific, often quantitative performance target that must be met. Agile organizations should set OKRs annually and assess progress against them on a quarterly basis [McKinsey 2019].
KPIs can help the enterprise measure progress toward key results.
Strategy deployment is both top-down and bottom-up. When the vision of leaders is translated into concrete OKRs throughout the enterprise, the “catchball” process entails frank, reality-based discussions between and within management levels. Leaders normally define required results; the teams the means [Dennis 2006].