Assess, Prioritize & Prove Value with Automation Discovery

Ashling Discovery Services help you decide what to improve first and how to do it. We map value, identify the best processes to target, and produce a prioritized roadmap tied to business outcomes.

We provide three approaches to discovery:

Discovery with Communications Mining Analytics is a way to turn inbox traffic into clear categories, volumes, and patterns. We identify what customers, partners, or employees ask for most, then surface the best automation opportunities and validate one with a pilot.

Discovery with Process Intelligence is a way to use system event data to map how work actually flows end-to-end. We build a “digital twin” of the process, then identify where delays, rework, and bottlenecks are created so you know what to fix first.

The Discovery Diagnostic is a structured approach to build a prioritized pipeline and roadmap across departments. We assess impact, feasibility, and readiness so your team can move from ideas to a plan that is practical to deliver and maintain.

Discovery helps you avoid building the wrong thing. It makes sure you are solving the right problems at the right time, with a plan you can deliver confidently.

Discovery aligns teams on priorities, gives you a clear picture of where value is hiding, and turns ideas into a sequenced plan that reduces risk and speeds up results.

Discovery confirms feasibility early. It surfaces data gaps, system constraints, process complexity, and change management readiness before you build, so you do not get stuck mid-project.

Discovery identifies the fastest paths to impact and removes ambiguity upfront. That means your delivery team spends less time debating and reworking, and more time implementing and optimizing.

To get the most value quickly, we ask for a few simple inputs up front:

1) Confirm your executive sponsor
Choose a leader who can align priorities, unblock decisions, and keep the engagement moving.

2) Commit to the workshops
Make sure key stakeholders can actively participate in working sessions. Discovery works best when decisions happen in the room.

3) Select 3–5 functions to include
Identify 1–2 reps per function who know the day-to-day process and can share real examples, exceptions, and pain points.

4) Bring basic cost data
Ask your HR or Finance lead to provide FTE cost inputs so we can size value accurately and compare opportunities fairly.

5) Complete the intake pre-work
The process intake document captures a process inventory grounded in how work is actually done—enabling us to identify and qualify automation opportunities without bias. From there, we collaborate to optimize and reimagine processes for maximum impact.​

After discovery, we move you into a steady operating cadence that keeps value delivery predictable, keeps the pipeline healthy, and helps the program scale across teams and business units without losing control.

The Operating Cadence is built on four disciplines:

1) Financial Value Management
We treat value like a financial commitment, not a retrospective metric. Value is planned up front, validated at key checkpoints, and tracked quarterly to keep a clear line from platform usage to business outcomes.

2) Roadmap, Pipeline, and Ongoing Discovery
We maintain a rolling 12-month, value-backed pipeline. Discovery continues, but it is governed, so expansion into new teams and processes stays controlled and repeatable.

3) Delivery and Execution Discipline
We set quarterly delivery expectations and sequence work based on value, not convenience. This creates accountability and ensures execution decisions match the value commitments you have made.

4) Licensing and Platform Consumption
We actively manage utilization, capacity, and adoption signals to prevent renewal surprises, reduce scale risk, and confirm you are maximizing what you already own before expanding.

Takeaway: Discovery gets you the roadmap. The Operating Model makes sure you deliver on it, quarter after quarter. Value is managed as an ongoing commitment, which is what enables sustainable growth at scale.

We sustain and scale by keeping the right conversations happening with leadership, on a predictable cadence. When goals, metrics, and decisions stay connected, adoption becomes natural and expansion stays controlled.

1) Business goals and metrics
We tie automation work to the KPIs leadership already cares about, like throughput, cost per unit, quality, and labor productivity. Those metrics connect directly to OKRs and the roadmap, so platform usage stays linked to outcomes the business is actively tracking.

2) Insight, benchmarking, and decision support
We review trends vs plan, compare performance to peers, and normalize productivity metrics so leaders can answer questions like: “What should capacity and staffing look like for an organization like ours?” We also use a consistent tech decision framework to match use cases to platform capabilities and ensure current licenses are fully leveraged before adding tools.

3) Strategic alignment, operating model, and partnership
As the program grows, we keep alignment tight across executive, program, and project levels. Pod-based resourcing by domain and value stream makes scaling structured, not chaotic. And we stay aligned with software partners to bring market perspective and new platform capabilities into roadmap decisions.

Program enablers
These are the repeatable mechanisms that keep decisions consistent as you scale: disciplined tech selection, scalable delivery structure, and clear governance.

Takeaway: As business goals evolve, automation priorities evolve with them, guided by metrics, insight, and peer perspective.