𝗛𝗼𝘄 𝗪𝗲 𝗙𝗿𝗮𝗺𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗪𝗶𝗹𝗹 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗕𝘂𝗶𝗹𝘁

The information technology era is ending, at least in part. It collected data and moved it to where it was needed. Intelligence technology is different.

What that era becomes is not settled. It is being decided now, in procurement decisions and architecture reviews, by the answers being given to questions like these.

🔸 𝗔𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗔𝗜 𝘃𝗲𝗿𝘀𝘂𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗔𝗜

Adoption selects a vendor and measures uptake. Engineering establishes that a system is safe, reliable, and capable of delivering benefits before it is used. We chose adoption, and the engineering phase was declared unnecessary rather than skipped. Whether an application demands engineering is a decision to make before procurement, not after an incident.

🔸 𝗛𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗟𝗼𝗼𝗽 𝘃𝗲𝗿𝘀𝘂𝘀 𝗔𝗜 𝗶𝗻 𝘁𝗵𝗲 𝗛𝘂𝗺𝗮𝗻 𝗟𝗼𝗼𝗽

In one arrangement the person originates the work and the system assists. In the other the system originates and the person approves. The same phrase describes both, and most deployments claim the first while running the second. Name the act the human performs and you will know which loop you are in.

🔸 𝗦𝗽𝗲𝗰𝗶𝗳𝘆𝗶𝗻𝗴 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 𝘃𝗲𝗿𝘀𝘂𝘀 𝗦𝗽𝗲𝗰𝗶𝗳𝘆𝗶𝗻𝗴 𝗣𝗿𝗼𝗰𝗲𝘀𝘀

Outputs say what must be produced. Process says how, and process is where quality, safety, and security are made. Specify only the output and anything optimizing for it will remove the rest. Decide which parts of the process must stay before the work starts, because afterwards there is nothing left to regulate.

🔸 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝘃𝗲𝗿𝘀𝘂𝘀 𝗔𝗜 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻

Governance controls act once, at a boundary. Regulation acts on a system while it runs and corrects the difference. A guardrail restrains while a regulator steers. Ask what corrects the behaviour while it is happening. If the answer is a report read afterwards, nothing is being regulated.

🔸 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝘃𝗲𝗿𝘀𝘂𝘀 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆

A hazard is a source of uncertainty that creates the opportunity for risk. Risk assessment walks from hazard to failure mode to control, and with these systems that walk breaks at the first step, because how harm arises cannot be enumerated in advance. Evaluating uncertainty is different work: margin, reversibility, containment, and people close enough to notice.

🔸 𝗗𝗮𝘁𝗮 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘃𝗲𝗿𝘀𝘂𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗱 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲

In the old model, intelligence sits between knowledge and wisdom and operates on what is known. Today's AI is inference over data, used as a proxy for both knowledge and intelligence, with the layers between skipped. A proxy for knowledge is not knowledge. Without managing your own, the inference runs on what it learned from everybody else.

🔸 𝗛𝘂𝗺𝗮𝗻 𝗢𝗻𝘁𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝘃𝗲𝗿𝘀𝘂𝘀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗧𝗮𝘅𝗼𝗻𝗼𝗺𝗶𝗲𝘀

We describe these systems in words borrowed from human cognition. This is anthropomorphism, and it points to a category failure. Memory names three unrelated mechanisms. Reasoning names token generation. An ontology of AI comes first, built from what these systems are and do. A proper taxonomy follows from it.

Most of these questions already have answers in use, carried over from the information era or supplied by vendors. Those answers were made for a different technology or for someone else's interests, and they are settling into practice unexamined.

The model, the platform, and the tooling can be bought. The engineering, the regulation, the organization's own knowledge, and the accountability cannot. They must be built.

𝗛𝗼𝘄 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗮𝗻𝗱 𝘄𝗵𝗼 𝗰𝗵𝗼𝘀𝗲 𝘁𝗵𝗮𝘁 𝗳𝗿𝗮𝗺𝗶𝗻𝗴?

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Raimund Laqua, P.Eng., PMP, is the founder of Lean Compliance Consulting and one of Ontario's first licensed software engineers. He works with organizations in highly regulated, high-risk sectors to build the operational capabilities needed to keep their promises, and is a national advocate for licensed professional digital engineering in Canada. He writes about compliance, engineering, and AI at leancompliance.ca.