Teal Unicorn and AI

AI is as revolutionary as steam power, electricity, transistors, and the internet. Its impact will take many years to roll out, and it will be a historical landmark. It is the next level of work. We have automated away most of the work in the levels below: manual, craft, industrial, and clerical work. Now it is the turn of knowledge work: AI is knowledge automation. Many workers will move on to the next level. What shall everyone call it? Insight work? Wisdom work? Those sound a bit pretentious. We at Teal Unicorn will try calling it concept work, until a consensus emerges. Welcome to the next level.

Although we mostly stay away from technology, we can’t avoid discussing AI. Like every new technology before it, it has been hyped to absurd levels by the vendors and the commentators. AI is a particularly egregious example because the business model seems to be to burn large amounts of venture capital, pump the share price, and run. Nevertheless, it is real. At Teal Unicorn, LLMs have been transformative to our work. We use them daily, mostly ChatGPT but also Dall-E and Gemini (we have tried many others, they are all impressive, we stick to what we know. And we refuse to use Grok until Musk no longer owns it.) 

For years we battled to stop people saying “AI”. There’s no intelligence here. It’s an astonishing programmer party trick, an amazing simulation. But we gave up, and now we use the term. More correctly, we should say “machine learning”, and more specifically “Large Learning Models (LLM)”, or even more specifically “Generative Pre-trained Transformer (GPT)”. But hey, it’s all AI. And it blows us away. We have gone from sceptics to using AI every day. We use AIs to:

• get us started with an abstract or a list of bullet points on some topic
• translate content to or from Vietnamese
• analyse content and summarise it
• write content for us
• edit our content for grammar, structure, logic, or brevity
• create graphics
• design articles, presentations, courses, or books
• consolidate content into an interactive body of knowledge
• strategise and develop marketing
• coach and learn about AI

Agent‑based platforms already stitch together customer intent, supply‑chain signals, and compliance rules faster than any human dispatcher. There is the possibility of a “multiprocessing bus” where micro cognitive processes—discerning sentiment, optimising routes, drafting contracts—plug in and hand off seamlessly. Used well, this architecture gives everyone super‑human reach: AI does the pattern matching at machine scale so people stay focused on purpose and judgment.

We practise what we preach. Much of our latest book, Leading the Open Enterprise,  was drafted in dialogue with Om, the TealGPT, our in‑house large‑language model fine‑tuned on Teal Unicorn’s own corpus. We rely on Om every day—for research sweeps, scenario drafting, even sparring over ideas—because it lets us test ideas at a velocity our human brains alone could never match. In that sense AI is a force multiplier: it amplifies curiosity, exposes blind spots, and frees time for the slow art of wisdom‑making.

Yet we reject the fantasy of the fully autonomous enterprise. Knowledge work still demands human discernment, empathy, and ethical framing. Therefore a good rule is human‑in‑the‑loop by design: no automated decision that materially affects a customer, employee, or citizen goes live without explicit oversight and approval. This guard‑rail prevents the quiet decay of skill where staff stop learning because “the model will sort it out,” a risk especially acute for new hires who have not yet built baseline competence.

Bias, opacity, and accountability remain real issues. Every model is a statistical shorthand; trained on skewed data it can embed the prejudices of its creators or its era. The black box also frustrates auditability—when an algorithm allocates credit or flags fraud, who stands behind the decision? Recent history offers little comfort: Boeing settled rather than faced prosecution after the 737‑MAX disasters, and we expect automotive giants to negotiate similar deals over autonomous‑vehicle deaths. The Open Enterprise must do better. You should log prompts and outputs, run bias‑variance tests, and publish model lineage so that stakeholders can inspect the logic rather than relying on marketing gloss.

In short, AI augments rather than replaces. Your task is to wield it as a moral, transparent force multiplier—one that elevates insight workers from repetitive data wrangling to higher‑order sense‑making—while never abdicating responsibility for the judgments made in your organisation’s name.

To learn more about AI in business, we recommend the  Business Agility Institute’s authoritative research paper From Constraints to Capabilities: AI as a Force Multiplier.  It identifies three principles for effective human-centric AI:

Augmentation, Not Automation:

 Amplifying, not replacing, human-produced value.

From Creator to Composer:

 Redefining the role of humans to direct AI’s potential.

AI as a Force Multiplier:

 Achieving greater business impact within existing capacity.