Transformation & Organizational Capability / Service

Process Improvement & Automation Readiness

Process diagnostics, automation opportunity mapping, AI-readiness assessment, use-case development, business-case narratives, and implementation roadmaps.

Process improvement and automation readiness consulting

The Work

Regulatory and R&D work is full of automation opportunity. It is also full of mis-identified automation opportunity. The work that looks automatable — document drafting, data entry, repeated formatting — often isn't, because the value is in the judgment surrounding the keystroke, not the keystroke itself. The work that is genuinely automatable — literature surveillance, cross-document consistency checks, evidence reconciliation, signal triage, label-change impact assessment — is often invisible to leadership because it lives below the line of executive attention. A good diagnostic surfaces the second category, not the first.

There is a real difference between a process diagnostic and a tool evaluation. Tool evaluations start from a vendor demo and reverse-engineer a justification. Process diagnostics start from how the work actually moves — who touches it, where it waits, where it gets reworked, where decisions are made twice — and only then ask which interventions (automation, AI, governance change, role change, training) actually move the metric. Most automation programs that fail to deliver were tool-led rather than process-led.

The business case is where most regulatory and R&D automation efforts get stuck. Finance has seen too many decks promising productivity gains that never showed up in the run-rate. A credible business case names the baseline metric, the realistic post-change metric, the implementation cost, the change-management cost (which is usually larger than the implementation cost), the timeline, and the downside scenario. It survives the second meeting because the assumptions were built to survive the second meeting.

Implementation roadmaps account for organizational change, not just system change. The fastest way to lose an automation program is to deliver the tool before the operating model is ready to absorb it — roles unchanged, governance unchanged, performance metrics unchanged. The roadmap sequences the work so that the people, the process, and the system change in the same direction at the same time.

What's typically included

  • Process diagnostic — across regulatory and R&D workflows, with where-it-waits and where-it-gets-reworked mapping.
  • Automation opportunity mapping — with prioritization by value, feasibility, and risk.
  • AI-readiness assessment — for specific workflows where AI is being considered.
  • Use-case development and validation — including scope, success criteria, and exit conditions.
  • Business-case narratives — for finance and executive audiences, with defensible assumptions.
  • Implementation roadmaps — including change management, role redesign, and performance-metric alignment.

When this is the right conversation

Operations leaders evaluating where to invest in automation, transformation offices prioritizing initiatives, finance leaders skeptical of automation business cases, or teams trying to scope an internal AI/automation effort.

Output you can use

A prioritized opportunity map, written business cases for top opportunities, a phased implementation roadmap, and a baseline metric set to track outcomes.

Looking at automation in regulatory or R&D?

Tell us what's on the agenda and where the team is uncertain.

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