DNAi Professional Certification

Start with the work, not the model. Sit with the people who run a process and trace one real instance end to end: who triggers it, what they look at, what they decide, what they hand off. Write each step as a verb with an owner. You are looking for three things. First, repetitive steps with stable rules and low ambiguity. Those are automation candidates. Second, steps where someone weighs context, exercises judgment, or absorbs risk. Those stay human and AI only assists. Third, the quiet steps nobody documents, like the rep who knows which accounts to never auto-email. That tacit knowledge is the connective tissue you protect. A claims team, for example, may spend forty percent of a case on data gathering and twenty percent on the judgment call that actually matters. Automate the gathering, surface it cleanly, and give the adjuster more room for the call. If you cannot draw the map on one page with the team nodding along, you do not understand the work well enough to route AI through it yet.

Use a plain test for every step. Ask: is the rule stable, is the cost of an error low or fully reversible, and does the step require no human accountability? If all three are yes, automate it and free the person. If any is no, augment instead: the AI drafts, ranks, retrieves, or flags, and a person decides. A marketing team can automate transcribing and tagging interview calls. They should not automate the final positioning claim, because it carries reputational risk and reflects judgment the team is accountable for. Augmentation here means the model proposes three angles with evidence, and a strategist chooses. The trap is automating the visible, satisfying tasks while leaving people the residue of low-value cleanup. That tanks morale and hollows out skill. Aim the opposite way: automate the drudgery, protect and elevate the judgment. When you augment, keep the human in the loop with real authority to override, not a rubber-stamp approve button they learn to click without reading. A human who cannot say no in practice is not in the loop.