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Adaptive Organization: taking the pace seriously

If there is one thing that I'm sure crossed every leaders' minds in the past 6 month of the GPT march is "We won't be able to keep up with this, this is too fast! We can't even consider the consequences of one technical shift and a new announcement is made that has to be considered!"

In an abstract form this realization sounds like this: "Technical development is faster than the pace of my evaluation and implementation cycle!" Although ChatGPT brought this message home to a huge amount of leaders, the phenomenon is not new. There were two big waves of building organizational capabilities that are designed for continuous change: in the 90's the concept of "learning organization" as a response to the increasing pressure from competition and the second half of 2010's with the concept of "adaptive organizations" as a response to the increasing pressure from technical development and digitization opportunities. The growing awareness of the capabilities of disruptive or exponential technologies (deep learning based AI, blockchain, IoT and edge computing, metaverse and virtual reality...) gave a very technical flavour to more general management problem: how can I build an organization that not only performs well today, but can also continuously respond to the challenges of tomorrow.

This is our stage of Adaptive Organizations.

Adaptive Organization

Flexibility from technical and organizational and ecosystem aspects

Throughout the previous stages the focus was on how to get a grasp of the competitive advantages that available technology holds. In the data-driven organization we went to a strategic level, where we want everyone across the organization to be able to use data and technology. We realized that technology has a major impact on our business. In the Adaptive organization the strategic focus shifts to the realization of the speed of technological development rather than the state of the art. Although it sounds banal, that technical change is going to be permanent and accelerating, only a few companies have the capability to sit down with this law face to face and reconsider strategy accordingly. And this is especially true for artificial intelligence.

If we assume that technology will continuously bring new potentials that we have to realize, evaluate, and implement, we have to prepare both our architecture and organizational structure to deal with this flexibility and it leads us to a very different self-definition in the ecosystem.

Technical features

The key feature of the architecture of an Adaptive organization is that we have to be able to continuously change parts that we realize are not valid anymore. When every department comes with new AI tools they want to try and maybe they find insane efficiency improvement potentials we have to be ready to say OK, under what circumstances can I allow to integrate these new tools to keep stability, support business processes, maintain security. We have to redefine on an architectural level what does it mean to integrate, how fast we can evaluate, what is the process of evaluation, what policies do we have to judge if someone can try something or not. This is where microservice architectures start to be inevitable, where data mesh philosophy might start to flourish, when fast, stand-alone developments for specific purposes have standard procedures, and when we simply can't avoid cloud and platform thinking. Specifically with building AI at this stage for internal use, it really starts to matter how up-to-date developer tools we are providing with our developers, how easy it is to use state of the art libraries, open source experimentations and run pilot tests. Although this might sound trivial the overhead of experimentation that is engineered into the architecture and development processes might be eye-popping if we don't have a strong purpose.

Organizational capabilities

The first thing that is associated with adaptability in the domain of organizational structure is agile. Not in the sense of dynamic, but in the sense of a management philosophy broken down to very pragmatic ceremonies from stand-ups to reviews and roles like agile coach, product owner and scrum master. But agile became a bit overused and inflated by the lots of companies who implemented ceremonies and roles without the shift in management philosophy. Traditional management philosophies are around being more effective and efficient by pushing people more, defining goals better, automating things more. In this sense agile means faster, more dynamic. The agile philosophy evolved around software development: where I have to discover/change unknown stuff to achieve desired goals. When applied in a traditional organizational context to manage BAU this becomes a disaster. But if we use it for "how do we change ourselves to be better?" and only these things are the scope of agile operations agile does not mean faster. It means adaptive. We build structures (roles, scrum teams, ceremonies...), culture (leadership style, trust, agency, communication style...) and skills (managing change, dealing with uncertainty, take responsibility...) for continuous development, experimentation, self-correction, cross-functional collaborations, and eventually continuous adaptation to external factors with our best capabilities.

The other big organizational topic in Adaptive organization is how we relate to automation. When we realize that AI (especially now with generative AI) has huge potential to automate large parts of people's tasks there is an immediate question: will I have a job tomorrow if AI can do most of what I do? This existential threat might become paralyzing especially in organizational contexts when we say that we want to implement technology to make us more efficient. And it is quite challenging to invite people to actively find and implement new technologies that will replace them. But it is important to note that adaptive does not mean that we can only meet the same needs with less resources. It means: the capability of reacting to all needs of customers with all tools available. In this formula the "needs of customers" part is not constant. There is a big difference between being more efficient to do the same and to be able to meet more customer needs. This is the difference between the cost mindset and the growth mindset. In turbulent times like these you will need the power of teams and engaged people to find what you can do. And this requires psychological safety, that requires the growth mindset. It is all interconnected. That is why in adaptive organisation leadership is more important than ever despite this is a very technically driven stage.

Ecosystem mindset

Besides technical and organizational structures there is a typically new element in adaptive organizations: institutionalizing sources of inspiration. They tend to have processes about how to gain insights from innovators like universities and startups and how to discover new needs from clients. The hard part does not seem to be when they regularly meet with them, but what do they do with the gained information. With universities they can set up shared labs, support PhD programs, collaborate with professors... With startups they take part in incubators, offer experimentation playground or sandboxes, drive hackathons. With clients they initiate innovation circles, be part of early research and development innovations or even participate in joint go-to-market strategies in longer chains. The general consequence is, that the boundaries of such an organization become more blurred. There are several people running around in several hats (startup, university professor, leader) that requires a huge flexibility from HR, from internal communications policies, from IT asset management, from IP management. When transitioning to this phase the elevated ideas of the strategy might be cooled by the very pragmatic traditional boundaries of the corporation: we can't give you permanent access to the building unless you are an employee. We can't invite you to an internal workspace. We don't know who is going to own the end result of a joint research project, so let's wait until we find out. What if the startups are just going to run away with our ideas?

Playing an active role in the ecosystem around the company and even a leading one in the industry requires a lot more than individual people who might have the network or are capable of inviting partners. Betting on adaptation means betting on running faster than other not on more protection against newcomers. This impacts all aspects of the organization from mindset to structures, leadership and technology.

What is beyond adaptive?

From many aspects Adaptive organization is the holy grail of a traditional company. They can bring the most out of their competitive edge. What we do not include in this step, but Adaptive usually opens the appetite for is self-disruption. When we realize that AI can not only make us insanely efficient (solving the same problems better), but we can redefine the playground (solve problems that could not be addressed before). We might realize that our AI capabilities can be leveraged to generate totally new businesses that might even make the traditional business model obsolete is when we enter the field of disruptive thinking and build our business model around AI. When we start to reinvent our business around AI and provide AI first tools, that is when we enter the stage of the AI Factory.


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