August 27, 2025
As AI reshapes business, many leaders focus on automating existing processes. This approach can yield short-term productivity gains, but it will not lead to true reinvention. Real transformation requires understanding the essence of your enterprise, independent of its current implementation.
This guide outlines how to use Jan Dietz's Design & Engineering Methodology for Organizations (DEMO) to guide an AI strategy. The central idea is to identify the fundamental human acts that create value and then design AI to amplify them, not replace them.
DEMO provides a framework for understanding an organization's core by separating its stable essence from its variable implementation. It focuses on two essential types of human actions:
DEMO reveals the "business DNA" by mapping these essential acts. This map ensures that technological changes enhance the core value creation rather than eroding it.
Enterprise ontology is one of three complementary approaches needed for a robust AI strategy.
The strategic insight is that successful AI transformation often requires all three approaches working together: understanding business essence (Enterprise), enabling formal reasoning (Sowa), and supporting AI reasoning within specific contexts (Domain).
Most organizations use AI to automate existing processes. Think of chatbots replacing call centers or machine learning optimizing advertising campaigns. This approach treats AI as a mere productivity tool, leading to three key risks:
This operational focus also contributes to widespread failures. A recent MIT Project NANDA study, "The GenAI Divide: State of AI in Business 2025," revealed a 95% failure rate for generative AI pilots in enterprises to deliver measurable returns. This stark statistic underscores that simply automating existing processes or applying AI as a "technology-first" solution, without a deep understanding of core business value and human accountability, is unlikely to yield true transformation. Common reasons for these failures mirror our concerns: vague objectives, a lack of deep business integration, and AI systems failing to learn or adapt to nuanced operational contexts.
The Design & Engineering Methodology for Organizations (DEMO), developed by Jan Dietz, offers a powerful way to analyze organizations by focusing on their essence. It abstracts away from implementation details, allowing us to see the fundamental transactions that create value. At its core, DEMO identifies three types of acts performed by human actors:
The key insight from DEMO is that only Original Acts (O-Acts), driven by human actors, create business value and commitments. I-Acts and D-Acts, while necessary, are essentially support activities for O-Acts. This distinction is vital for AI transformation, as it helps us identify what can be safely automated (I-Acts and D-Acts) versus what needs strategic enhancement (O-Acts).
Even when considering that autonomous AI agents might perform certain coordination acts, it is crucial that they operate on behalf of, and ultimately in the interest of, human stakeholders. This perspective highlights DEMO's potential to offer further insights into the ethical use of AI, by ensuring that human intent and accountability remain central to all value-creating transactions.
Let's illustrate this with a simplified example for ProMatch Inc., a fictitious professional services marketplace. Imagine a core transaction: "Matching a Client with a Service Provider."
A DEMO transaction typically involves a Requester and an Executor engaging in a coordinated dialogue, leading to a Production Act.
Transaction: Matching a Client with a Service Provider
| Step | Act Type | Actor | Essential Act |
|---|---|---|---|
| 1 | C-Act (rq) | Client | Requests a service provider match |
| 2 | C-Act (pm) | ProMatch | Promises to find a suitable match |
| 3 | P-Act (ex) | ProMatch | Executes the matching process (O-Act) |
| 4 | C-Act (st) | ProMatch | States a match has been found |
| 5 | C-Act (ac) | Client | Accepts the proposed match |
In this sequence, the P-Act (Execute matching process) is the core Original Act where ProMatch creates value by identifying a suitable connection. The C-Acts are the essential communication steps that make this P-Act possible and formalize the commitment between the Client and ProMatch. Understanding such essential transactions allows us to differentiate between core value creation and mere information processing.
Instead of just automating, a DEMO-guided strategy focuses on enhancing essential, value-creating activities. Let's consider our fictitious example ProMatch Inc.
Current AI Usage: ProMatch uses AI for operational tasks like automated matching and content generation. This is an "efficiency-focused" approach.
The Problem: Their AI investment has not created a competitive advantage. Competitors now have similar algorithms, leading to commoditization and reduced client loyalty.
DEMO Analysis and Solution: A DEMO analysis reveals that ProMatch's core value comes from human-centric acts. These are the Original Acts that truly matter, such as:
The current AI usage supports informational acts (processing data), but not these Original Acts. The DEMO-guided response is to design a new strategy that enhances these human acts with AI.
For example, an AI could analyze business data to surface patterns a human consultant might miss, providing a "deeper understanding of the business context". The human role remains to interpret emotional and cultural signals, make judgment calls, and take accountability for the final outcome. This perspective invites us to reconsider our own enterprises' processes in light of the "ProMatch Inc. Example." By reducing ProMatch's processes to their essence, we are freed from the shackles of existing operations, even if automated. This creates an opportunity to envision how such a business might be invented from the ground up using AI, autonomous agents, or a combination of AI and logic-based reasoning (neuro-symbolic AI) to fundamentally redefine its value creation.
As AI becomes more sophisticated, a critical question emerges: Who is responsible when things go wrong? To preserve the integrity of your business, a clear distinction must be made between authorized AI and autonomous AI.
For any AI-assisted decision, the "accountability test" is straightforward: If a human is responsible for the outcome, it is a valid act. If an AI is, it is not. This framework ensures human authorization chains remain intact and AI acts as an an intelligence amplifier, not a decision replacement.
In the coming years, information processing, document generation, and routine coordination will be fully automated. What remains, and what will become your competitive advantage, is the essence of human contribution:
The organizations that thrive will be those that use AI to amplify these uniquely human capabilities, creating authentic relationships at scale while preserving accountability and trust.