Service Pathway — Emerging
Most AI initiatives stall because one word is covering everything from a scheduled batch job to an autonomous agent. OnTarget gives organizations a shared vocabulary, a disciplined way to screen ideas, and a realistic roadmap from stalled ambition to working pilots.
Use-case screening · Readiness assessment · Pilot roadmaps · Responsible implementation
Fixed, repeatable rules — no judgment. Workflow scripting, not AI.
Pattern-based flags and forecasts. A human still acts on it.
AI drafts, summarizes, suggests. The human reviews and decides.
AI decides and acts. Highest payoff, highest risk. Build last.
When This Service Is Needed
AI initiatives rarely die loudly. They stall — in scoping debates, tool evaluations, and pilots that never quite start.
Every team has automation ideas; nobody agrees what 'AI' means for any of them.
A tool decision is being debated before any use case has been selected.
An earlier automation effort stalled and sponsorship has cooled.
The proposed first project is the biggest problem instead of the most provable one.
Ideas are being scoped by enthusiasm rather than criteria.
Governance, risk, and success metrics are afterthoughts to the technology conversation.
Interactive — The Eight Criteria
Pick one automation idea your team has proposed and score it honestly. The more “yes” answers, the stronger the Phase 1 fit.
Delivery Approach
The methodology favors small, provable wins over one large build — sequencing credibility before ambition.
Gather the automation ideas teams have already proposed; score each against the eight criteria.
Prioritize small-footprint, high-frequency candidates — quick wins first, not the biggest problem first.
Only after candidates are chosen: build vs. buy for those specific tasks. Off-the-shelf covers most Level 1 needs.
Implement the top one or two with a before/after metric — hours saved, error rate, turnaround time.
Use proven results to re-energize sponsorship and open the predictive and copilot wave.
Screening bands: 6–8 yes = Phase 1 automation candidate · 3–5 = predictive/copilot wave · 0–2 = parked.
Specific Deliverables
Expected Outcomes
“Favor small, provable wins over one large build.”
A single successfully automated process with a clear before/after metric does more to restart an initiative than an ambitious project that takes months to show results.
Relevant Experience
This methodology was developed in live enterprise operations — customer service and back-office environments where automation ideas were plentiful, sponsorship was fragile, and one more stalled initiative would have ended the conversation. It reflects the same discipline PCM™ brings to large programs: shared vocabulary, evidence over enthusiasm, and momentum built on provable wins.
Common Questions
No — that's backwards. Tool decisions come after use-case selection. Most Level 1 candidates need workflow scripting or off-the-shelf RPA, not an AI platform.
It's typical. Stalled initiatives usually failed on vocabulary and sequencing, not feasibility. The screening process is designed specifically to restart them.
They're Levels 3 and 4 — real, valuable, and usually not first. The maturity model sequences them after the organization has built delivery evidence and governance at the lower levels.
Same discipline at different scale. For utility-scale AI transformation, see the firm's white paper on AI-driven grid modernization — and the stabilization pathway for programs already in motion.
Bring the list. You'll get a screened, ranked view of where to start — and a realistic path to the first provable win. Or start with the white paper: From Automation to AI.