PDS Consulting / AI Enablement
AI Enablement
Most AI consultancies pitch the model. PDS builds the system around it — a strategy that fits your business, use cases scored by realistic value, a pilot that proves the approach (or rules it out), and governance that lets you experiment without compliance landmines. We use AI in our own consulting delivery, so we know what survives production.
At a glance
- Approach
- The system around the model — data, integration, governance, adoption
- Engage
- 5 ways — from a 1-week Strategy Briefing to an ongoing advisory retainer
- Models
- Vendor-neutral (Claude, GPT, Gemini, Bedrock, open-source / self-hosted)
- Foundation
- Pilots need sound data first — built via Data & Analytics if it's not
- Pricing
- Quote, on scope — not on firm size
AI fails on the system, not the model.
When AI projects fail, it's almost never the model — it's the data underneath, the integration to where work actually happens, the governance, or the adoption. Even the best model gives confidently wrong answers on broken data. So we'll tell you when your data needs work before AI work — most consultancies skip that conversation because their incentive is to start the engagement, not delay it.
Does any of this sound familiar?
- The board wants an AI strategy by next quarter, and no one internally can write it.
- A team tried ChatGPT for a task, got 70% right, and no one knows how to close the gap.
- Every vendor is selling "AI-powered" features and it's impossible to tell what's real.
- Three departments are running uncoordinated AI experiments with no governance.
- There's appetite to use AI, but the data is a mess — and everyone knows it.
How we run it — five phases.
01 · Discovery & current state
Inventory AI and automation use today, the vendor pitches received, prior experiments, and your data-foundation maturity. Separate what's real from what's noise.
02 · Use case scoring
Workshop candidate use cases and score each by value, feasibility, data readiness, risk, and time-to-value. Rule out the hype; rank the rest.
03 · Pilot selection & data check
Pick 1–3 pilots, confirm the data foundation is sufficient (or scope what's missing), and set success metrics, risk thresholds, and human-in-the-loop checkpoints.
04 · Pilot or roadmap
Build the pilot — model selection, data integration, evaluation harness, deployment, monitoring — or, if scope is roadmap-only, deliver a sequenced rollout plan.
05 · Governance & operationalize
Stand up risk tiers, data-handling rules, approval flows, monitoring, and audit logs. Transition to ongoing operation or an AI Advisory Retainer.
Honest scope. About 80% of mid-market AI is strategy, integration, governance, and adoption — that's our lane. Deep ML / model training is partnered out.
What you're aiming at.
- A clear, board-presentable AI roadmap tied to real business outcomes and your budget.
- A scored use-case portfolio — explicit "worth piloting" and "skip the hype" calls.
- 1–2 piloted use cases with measured accuracy, business impact, and a defensible go/no-go.
- A governance framework that lets you experiment safely — risk tiers, data rules, escalation.
- Avoided spend on AI hype — often the highest-ROI deliverable of all.
What you get.
- Current AI landscape brief — what's real, what's noise
- Weighted use-case scoring matrix + 12–18 month roadmap
- Pilot design, evaluation harness & results report
- Deployed pilot with monitoring (on go) or clean shutdown docs (on no-go)
- Governance framework: risk tiers, data-handling rules, audit & monitoring
Outcomes are what these engagements are built to deliver — grounded in 15+ years integrating intelligent automation into enterprise systems, plus hands-on use of generative AI in PDS's own delivery (an AI-driven intake tool, built and in use). As PDS engagements close, this section gets measured numbers.
Five ways to engage.
Start with a lightweight read, run the flagship discovery sprint, or build a pilot end-to-end — quote-only, scoped to the work.
AI Strategy Briefing
1–2 weeks · A lightweight read on where AI does and doesn't fit, myths debunked, ranked use-case recommendations.
AI Use Case Discovery Sprint
3–4 weeks · The flagship — identify 8–15 candidate use cases, score them, recommend 2–3 to pilot, deliver an executive roadmap.
AI Pilot Implementation
6–10 weeks / use case · Build one use case end-to-end — design, model selection, data integration, evaluation, deployment, monitoring.
AI Governance & Risk Framework
4–6 weeks · Risk-tier policy, data-handling rules, human-in-the-loop standards, audit and monitoring — standalone or bundled.
AI Advisory Retainer
Ongoing · Quarterly portfolio reviews, vendor evaluations, governance updates, and ad-hoc strategy support as the landscape moves.
Why bring PDS in for this.
The system around the model
Evaluating models is the easy 10%. The hard 90% is data, integration, governance, and adoption — the same problems we've solved in non-AI programs for 15+ years. We see the failures coming.
We use AI in our own delivery
PDS built and runs an AI-driven consulting intake tool — not a demo. We know the difference between "works in a vendor demo" and "survives real inputs week after week," because we've lived both.
Vendor-neutral on model providers
No partner tier with Anthropic, OpenAI, Google, or Bedrock. Sometimes the answer is Claude, sometimes GPT, sometimes a self-hosted model — and sometimes you need a process change, not a model.
We'll tell you to skip AI
When AI isn't the answer, we say so — most consultancies can't and stay in business. Avoided spend on the wrong pilot is often the most valuable thing we deliver.
Pairs with Data Architecture & Analytics as the Data + AI bundle (foundation first), and follows Digital Transformation Advisory.
No pitch, no pressure
Board wants an AI strategy?
A 30-minute call is enough to tell whether AI is the right answer for what you're facing — and where a sensible, low-risk first step would be.
Book a call →Or email directly: info@prendergastdigital.com