The Five Ages of Organizational Evolution

Since 2013, BMNT has pioneered the adaptation of Lean Startup best practices for use in government and mission-driven ...


Since 2013, BMNT has pioneered the adaptation of Lean Startup best practices for use in government and mission-driven organizations.. Along the way, we’ve learned a great deal about how to: 

  • Gather the right problems to solve;
  • Analyze those problems to derive insights about an organization; and
  • Apply those insights to align problems to the right pathways to solve them.  

 

Over time, we’ve increased the impact that solving problems has on an organization by strategically increasing the:

  • Depth of Insight – how specific, detailed, and accurate the generated insights are.
  • Breadth of Access – the scope of the contributions from across the organization.
  • Speed to Action – how quickly an organization can progress from seeking problems to solving them.

 

Following outlines the evolution of our products and services over time in relation to the impact to the organization.  

 

Age
Depth of Insight
Breadth of Access
Speed to Action
Impact = 
(D x B) + S
No. of People Reached

Prehistoric

High

Low

Low 

4

10 - 20

Stone

Low

Moderate

Moderate

4

100s

Bronze

Moderate

High

Moderate

8

1,000s

Iron

High

High

High

12

10,000s

Information

Exponential

Exponential

Exponential

20

100,000s

Prehistoric Age

Pre-2020, BMNT gathered an organization’s problems by interviewing 10-20 people for 45 minutes each. Our analysts then manually sifted through the data that was gathered and generated insights, suggesting courses of action for the organization. The organization would then choose one or two problems to begin solving using their familiar solution pathways.Stone Age

In June 2020, we began collecting data about an organization’s problems using InsightAI’s conversational interface to talk to hundreds of users simultaneously. Our analysts then manually analyzed each extracted problem and developed a set of labels representative of the dataset and applied them. This analysis formed problem topics, or groupings of similar problems a community is facing which included the prevalence of each topic and the people associated with it. With this increased organizational visibility, its leaders could now prioritize a handful of problems to begin solving, by tapping users who provided the data to solve them. 

Bronze Age

By August 2020, we continued to leverage InsightAI, but analyzed the data we collected using our recently built machine learning capability.  This system uses semantic similarity to automatically read the data and produce problem topics and descriptions. Analysis taking only hours, not days, we saw the user engagement  scale into the 1000s. With machine-generated problem topics and descriptions, an organization can choose several problems to focus on solving simultaneously, cascading across multiple organizational levels, given the granularity with which the problems can be attributed to subsets of the organization.  

Iron Age

In January 2021, we began automating even more granularity into InsightAI’s problem analysis. In addition to the machine-generated problem topics and descriptions, this enabled us to parse specific elements of a problem. That is, we could extract the type of person impacted by the problem, the root cause creating the problem, and the desired end state if the problem were to be solved. This allows an organization to make data-driven decisions about which prescriptive actions to take. For example, an organization could know that if they solve the problem topic “Policy and Procedure Overload”, contracting officers will be able to more accurately track the progress of active contracts, reducing fraud, waste, and abuse. Such rich information will increase the velocity with which an organization can solve the right problems and create maximum impact.

Information Age

The final stage of evolution for organizational insights begins and ends with data - and lots of it. Future insights derived on behalf of our customers will benefit from the massive scale of data we’ve analyzed in the past, which has helped our models grow smarter over time, learning along the way with each new user interaction. 

As we enter the “elbow” of our evolutionary curve, we expect not just an exponential increase in the insights we’re able to derive from an organization’s users, but also an exponential increase in the impact we’re able to deliver to our customers. 

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