David Suydam is the CEO of Architech, a Canadian AI advisory and engineering firm focused on redesigning enterprise workflows and engineering them into production systems. He writes about the gap between AI experiments and operational impact, the discipline of workflow-level redesign, and what it takes to move enterprise AI beyond the demo.
Areas of Expertise
- Enterprise workflow redesign
- AI-native operating models
- Production-grade AI systems
- AI governance and human-in-the-loop control
- Enterprise AI strategy
- AI advisory and opportunity evaluation
- Agentic AI orchestration
- Enterprise systems integration
- Cloud-agnostic AI architecture
- Retrieval-augmented generation
- Operational transformation
- Enterprise AI adoption
Posts by David Suydam
- 10 AI Consulting Firms for Mid-Market Companies in Canada in 2026September 28, 2026
A fit-based guide to 10 AI consulting firms serving mid-market companies in Canada, from global consultancies to workflow specialists. Not a ranking.
- The Manager MultiplierSeptember 20, 2026
You funded the tooling, the training and an executive sponsor, and the rollout still stalled. The standard answer is more training for the reps. Train ten of them and you have made ten interventions. Equip their manager once and you change what all ten people see every week, in the one-on-ones and pipeline reviews where a team decides what actually counts. That layer is the one almost nobody equips. What it takes, and three questions to ask this week.
- Two AI Rollouts Ran the Same Playbook. Only One Kept Growing.September 20, 2026
You funded the tools, the training, and an executive sponsor, and the rollout still stopped spreading. Two mid-size companies ran substantially the same AI playbook. One hit a ceiling. The other kept growing past what anyone prescribed. The variable was the direct manager of the team: whether they were equipped to own the change before launch, not asked to approve it after. Here is what that takes, and the four questions to answer first.
- Three Scores Pick Your AI Use Cases. Conviction Decides Which Ones Ship.September 8, 2026
Your prioritized AI list is scored on value, feasibility, and risk, and it still will not tell you which workflows ship. Those three scores rank what deserves to ship, not what will. The missing question is ownership: for each workflow, name the operating leader who will personally see it through, the outcome metric they have signed for, and the date they will defend it in their own staff meeting. The shorter list is the roadmap.
- The model was never the hard partJuly 16, 2026
You have watched it happen. A pilot demos beautifully in the boardroom, then meets real data and real handoffs and quietly never reaches production. It was not a weak model. The model was never the hard part. A durable AI build is a system built underneath the workflow: the data foundation, the redesigned process, and the measurement that runs for as long as the workflow does. That is where the advantage sits, and where most vendors spend the least.
- Your project managers are meeting-minute factories.June 17, 2026
Your project managers spend most of the week manufacturing artifacts about the work, not landing engagements. Here is the operating layer we built, and the boundary we hold.
- AI is not an IT initiative. It's a CEO mandate.June 16, 2026
An AI initiative has been parked on your desk somewhere. The CEO decided 'we need to do something about AI' and handed it to IT, the future Chief AI Officer hire, or the Digital Transformation portfolio. The initiative dies the same death every IT-sponsored change program dies. AI strategy and sponsorship belong to the CEO. Execution belongs to a senior executive accountable for delivery. The 12.2% AI adoption rate is not Canada's innovation report card. It is Canada's CEO report card.
- Stop waiting for perfect data to start with AIMay 6, 2026
Somewhere on your roadmap, an AI initiative has been parked for a quarter behind a six- or seven-figure data-modernization program. The pitch sounded right: fix the data first, then the AI. For a narrow set of workflows that pitch is correct. For most of the AI value an operator can capture this quarter, it is not. The right scope is the workflow, not the enterprise. Ask what data this workflow actually needs, and where it already lives.
- Evaluations are table stakes. Outcomes are not.May 4, 2026
Six months ago, "we run rigorous AI evaluations" was a defensible thing for an AI-services firm to say. Today every major enterprise platform ships built-in evaluators by default, and even custom evaluators only measure whether the model is behaving. None of them measures whether the workflow is still moving the KPI you bought it to move. That is the layer above evals, and it is the one most enterprise AI work skips.
- Every AI homepage says the same thingApril 28, 2026
AI-services homepages sell verbs because verbs commit to nothing. The test that filters them: name a workflow, a metric, an operator whose job changes.
- The Fix Wasn't an Agent. It was an Org Chart.April 22, 2026
In February 2025 we did exactly what we tell clients not to do. We automated our own content workflow with nobody left in it who could say no, and we shipped slop. The fix was not a better AI agent. It was a reporting line. The editor has a veto, and the person responsible for shipping cannot override it.
