In transformation, that’s not relativism—it’s systems thinking. Complex systems allow equifinality: multiple paths can reach the same outcome. The trap isn’t “being wrong”; it’s insisting there’s only one “right” way.
What this means for digital transformation & AI
- Context beats dogma. Centralized platforms or federated teams can both work if they fit your constraints (risk, talent, regulation, scale).
- There’s no silver bullet tech. Data mesh vs. lakehouse, build vs. buy, open vs. closed models—any can be “right” when aligned to clear outcomes.
- Humans stay in the loop. The “right” AI is the one that augments people and decisions under real governance (ethics, privacy, safety), not the flashiest model.
Guardrails so “many rights” don’t become chaos
- Define the non-negotiables: mission, values, risk appetite, compliance.
- Outcome over orthodoxy: tie work to 3–5 business results (e.g., cycle time, cost-to-serve, NPS, revenue per customer, risk reduction).
- Parallel bets, fast kills: run 2–3 approaches in small slices; keep what measurably wins, retire the rest.
- Architect for options: modular platforms, open interfaces, human-in-the-loop workflows so you can swap tools without rewiring the enterprise.
- Measure learning velocity: track time-to-value, model usefulness (adoption, error rates), and human satisfaction—not just output.
Examples of “more than one right way”
- AI operating model:
• Central AI platform → speed, consistency, strong governance.
• Embedded AI pods → proximity to users, faster discovery.
Many firms do both: a thin central platform + domain pods. - Data strategy:
Lakehouse or mesh can succeed—pick based on data ownership, literacy, and regulatory load. - Delivery approach:
SAFe value streams or product-trio squads; either works if outcomes and funding models are product-based, not project-based.
A simple playbook
Clarify outcomes → Map constraints → Run parallel pilots → Decide by evidence → Standardize what works → Keep optionality.
In the age of AI, the winners won’t be those who “picked the one right way.” They’ll be the ones who created the right conditions—clear purpose, tight guardrails, and the courage to let multiple good paths compete—then scaled the approaches that proved value.
