Is your organization intelligent?

The world is changing faster and becoming more unpredictable. Rigid workflows are often not fit for purpose anymore - they're becoming the equivalent of dinosaurs' brains: not helpful when the ecosystem is upended. Despite the massive shift due to the pandemic, many large organizations still struggle to evolve how they collectively sense, create, decide, act and learn.

What design of that collective organizational brain provides a superior ability to adapt and compete? And can your teams put it into practice?

The graph of knowledge

Today's organizations witness an explosion of knowledge available, absorbed, and filtered by organizational networks now solidly wired through the likes of Outlook and Slack. That knowledge is then amplified and evolved by internal and external social networks and meshed with continuous streams of other ideas curated by AI-based algorithms. These structures were in their infancy only five years ago. Now they're increasingly the way business happens.

Not coincidentally, one of the fastest-growing data science spaces is “knowledge graphs”, whose biggest advantage is to document and process relationships similar to what Google does with the world's knowledge.

What do those networks of knowledge look like? Have a look at the next chart, starting with the red "cell" in the middle, and think of an example that many executives experience daily: innovation teams (and people, the red dots) working on the next big thing, or simply on business-as-usual continuous improvement. Their network structure looks like something like the following – and will increasingly add blue nodes, that is, networked machines that complement and amplify the knowledge of people. 

Most managers don't look at this picture when they design their organizations - because we don't learn how to design a collective intelligence in school or as we rise through the ranks. But today we can do much more than designing org charts, workflows, and instilling traditional management practices.

Beyond dated organizational-design practices

How do we add to the traditional management methods (in Figures 1, 2, and 3 below) the ability to deliberately orchestrate networks that span across organizational boundaries (in figure 4)? How do we architect such a system?

First, we need to unlearn. For instance, we likely need to do away with the conventional boundaries between disciplines - HR, IT, knowledge management, etc. The intelligence fabric depends on blending those practices.

Think of a salesperson using specialized documentation for a sales pitch created by a subject matter expert; or a contact center agent prompted to respond to a client in a certain way, based on a machine-learning algorithm that optimizes the customer interaction. How do the organization's tools, practices, and processes add to these people's impact? How does the sum of the parts becomes larger than the individual parts?

Let's peel the onion, a neural layer at the time.

The neural net you inadvertently designed

First, individual people owe their capabilities to their experience, and their impact depends on the match between those capabilities and the job at hand...

...then the performance can be enhanced through learning and development (L&D) activities, helping people to develop pattern recognition in the new environment...

...which is further supported in the flow of the specific processes they run, thanks to management support, documentation, etc.

Then, people connect with others to attack problems that they can't solve by themselves. Both L&D and enablement resources are provided by the organization, and those practices are among some of the best predictors of enterprise effectiveness[i]. But here’s the important twist: those capabilities are amplified by the connections that people have, which help them complement their skills and use others as creative soundboards. The combination of individual knowledge (existing, new, and targeted enablement) with network connectivity generates collective intelligence that’s superior to the mere sum of people’s own intelligence and their knowledge[ii].

If this looks like a neural net not unlike our brain's, that's because it likely does some of the same, at a different level (if you're interested in more abstraction that might surprise you, read here.)

Designing and building your organization's collective intelligence

The result is the scope of work for CIOs and enterprise architects who need to design the "augmented collective intelligence" system with COOs, CHROs, and P&L owners. Look closely at the yellow boxes - that's technology and practices you've likely built organically. It is time to make them part of a cohesive plan.

Some of them are established capabilities with modern data components (e.g. skill inventories or learning - e.g., what we built using collective intelligence, or what Workday embeds into its ERP SaaS). Some are emerging capabilities that enterprise vendors such as Workday, or Gloat are focused on - for instance, internal jobs marketplaces, or AI-enabled chatbots that keep tabs on employees' engagement. Some are net-new things that fall outside of traditional categories, like virtual watercoolers based on dynamically-generated network analysis.

The enterprise technology market is understandably all over this - though often in a fragmented way, making the work of enterprise architecture harder. One of the most cohesive responses to this opportunity (and challenge) is Microsoft Viva, which aims at redefining the “employee experience” market space.

All of these solutions belong to the category of augmentation of collective intelligence and should be part of enterprise-wide organizational design efforts. The prize is competitiveness in unpredictable times.

To learn more on how to structure the work check some of the previous articles, for instance here - and particularly the "principles" book and practitioner guidebook here. Be in touch if you have experiences and opinions to share.

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References

[i] Mckinsey Quarterly, “Organizational Health: a fast track to performance improvement”, 2017

[ii] A few papers have explored the importance of being exposed to adjacent knowledge. Matthew S. Clancy, Paul Heisey, Yongjie Ji & GianCarlo Moschini “The Roots of Agricultural Innovation: Patent Evidence of Knowledge Spillovers”, 2020; and Philipp B. Cornelius , Bilal Gokpinar , Fabian J. Sting  “Sparking Manufacturing Innovation: How Temporary Interplant Assignments Increase Employee Idea Values”, 2020