What If Your Company Had a Theory of Itself?
Most companies operate without any internal model of how they actually work. What if that changed?
Let’s entertain a strange but timely question:
What if your company had a theory of itself?
Not a brand book. Not a strategic memo. Not a vague narrative you tell investors.
But an actual, living model—of what the company is, how it functions, what drives it forward, and what drags it back.
We build models all the time.
In physics, we use equations to describe planetary motion. In climate science, we build simulations to understand weather systems. Even in finance, valuation models help forecast future cash flows.
But inside companies?
For all the talk of dashboards and KPIs, most organizations have no systemic internal model of how they truly work. They navigate complexity using intuition, scattered data, and outdated mental maps. Even the best managers are often flying by feel—because the map they really need doesn’t exist.
At Wangari, we're trying to change that.
From ESG Scores to Systemic Intelligence
When we started building Wangari, our initial focus was ESG. We believed—and still believe—that environmental, social, and governance indicators offer overlooked clues about a company’s future.
But something was missing.
ESG data was useful, yes. But it wasn’t enough to explain the system. It offered correlation, not causation. Surface-level indicators, not the structure beneath. It measured parts, not the whole.
So we evolved. Wangari is no longer an ESG tool. It’s a causal engine for systemic insight—a digital twin that represents the company not just as a set of metrics, but as a dynamic, evolving system embedded in its environment.
This evolution wasn’t just technical. It was philosophical.
We started asking bigger questions:
What makes a company adaptable, not just profitable?
What patterns separate stagnation from innovation?
How do cultural signals like trust or diversity actually shape performance over time?
Can you simulate these relationships? Test them? Intervene?
You can't answer those questions with a dashboard.
You need a theory.
Building a Theory of the Company
In science, a theory isn’t a guess—it’s a structured explanation of how things work. It identifies key variables, explains relationships, and predicts outcomes.
That’s what we aim to build for organizations.
We start by constructing a causal model: a graph of variables (like leadership structure, innovation output, market share) connected by causal links. These aren’t arbitrary. They’re inferred using cutting-edge tools from causal AI—combining expert priors, AI-generated hypotheses, and statistical validation. We’re not just fitting data—we’re mapping mechanisms.
From this, we create a kind of digital twin—but not the typical kind made for machines or factories. Ours is closer to a “glass brain”:
It reflects the internal and external dynamics of a real company.
It updates as new data arrives.
It can simulate futures, compare scenarios, and explain why one path may outperform another.
And crucially, it doesn’t just show you what happened. It shows you what matters.
Imagine asking:
If our R&D team were more diverse, would our time-to-innovation improve?
Or:
If market sentiment drops, what’s the causal path to our revenue?
Or even:
What combination of internal and external variables best explains why our competitor bounced back faster than we did after a downturn?
These are the kinds of strategic hypotheses Wangari helps users explore—not just statistically, but causally.
The Birth of Strategic Self-Awareness
This might sound abstract. But it’s not.
Because something happens when a company starts interacting with a system that models it causally. Something surprisingly human.
It begins to recognize itself.
Not just as a collection of departments or a set of KPIs. But as a living system with structure, feedback loops, tensions, and leverage points.
It begins to notice how culture affects performance.
How communication patterns predict bottlenecks.
How the external environment doesn't just pressure it—but reveals its shape.
In a strange way, this is a kind of organizational self-awareness.
Not mystical. Not conscious in the way humans are.
But functional. Strategic. Powerful.
It’s the difference between reacting and understanding. Between guessing and seeing.
In the age of AI, we talk a lot about machines becoming aware.
But maybe the more radical shift is the opposite:
Organizations, through AI, becoming aware of themselves.
Not AI as an overlord.
AI as a mirror.
Why This Matters Now
Most strategy today happens in a fog.
Leaders are surrounded by data, but few have true insight.
They face complex, dynamic problems—but rely on tools built for static environments.
Wangari exists to cut through that fog.
We don’t promise certainty. We don’t claim omniscience.
What we offer is something subtler: a structured way to think better.
To see patterns. Test ideas. Simulate futures.
To build—not just execute—a strategy.
In that sense, our mission isn’t just about AI or data.
It’s about helping organizations think like scientists—
to become curious about themselves,
to build better theories,
and to act from a place of clarity, not noise.
The Bottom Line: From Metrics to Meaning
A company with a theory of itself is a company with leverage.
It can anticipate crises.
Adapt faster.
Focus where it counts.
It stops being reactive. It starts being intentional.
At Wangari, we believe this is the future of strategic intelligence—not more dashboards, but better models. Not more KPIs, but deeper structure. Not more noise, but causal clarity.
And maybe, just maybe,
the first step toward a more conscious economy
is helping our organizations finally see themselves.
Wangari’s Curated Reads
- has a gripping deep-dive into Palantir for you. The Eye That Sees All reframes the surveillance giant not as a passive observer, but as an active player in a covert digital war for justice and control. For readers at the intersection of tech and sustainable finance, it offers a provocative narrative on how data infrastructure—once the opaque machinery of global power—might be repurposed to expose corruption and enforce financial transparency through tools like RICO-ready metadata and predictive AI.
- explores in a contemplative essay how the quest to define AI consciousness is inadvertently revealing the limits of scientific objectivity—and the richness of human awareness. The AI Mirror poignantly asks: What if the future of intelligence isn’t just about computation, but about embodiment, presence, and the subtle dimensions of being? As machines get more powerful, the real question may not be whether they can become conscious—but whether we, in the rush to engineer them, remember what consciousness really is.
- lays out a compelling vision for the next frontier of consumer tech: semantic search that actually understands us. The Search for Meaning maps how AI-driven search can revolutionize user experience—unlocking more intuitive, accessible, and precise tools for discovery and commerce. It’s a call to developers and product strategists: adapt or risk irrelevance, as consumers increasingly expect to search not with keywords, but with natural language, images, and intent.
Very interesting take on what strategy actually means. Definitions are important.
I have my own takes on corporate strategy (I consult), mostly related to differentiation or, perhaps more aptly, idiosyncrasies.
This is where ESG becomes a hindrance rather than a help. Practitioners will tell you it helps investors make decisions but it actually increases sameness and compliance as well as adding overhead costs to whole sectors.
I find the discussion around compliance, disclosures, idiosyncrasies and investor decision-making (real stock pickers, not pension fund mandate investing) to be fascinating.
Though the more time I spend in the space the more I find myself more on the side of the companies, less on the side of compliance, and helping investors to embrace risk (they way they are supposed to, that's what the money is for) by doing their own research!
In all, I believe this is what provides the necessary, competitive, true innovation across sectors.
I really like your track with this.