KYP.ai Process Intelligence gives us a digital X-ray of real work across desktops, applications, and systems – without prior process knowledge. The platform quantifies ROI per opportunity and produces a roadmap that spans both RPA and AI & Agentic Automation. Eight of our client engagements currently run on KYP.ai.
The Opportunity
The market is loud about AI. Every enterprise wants agents in production. Far fewer can say where automation actually creates value, or admit when a process should be fixed or retired before anything is automated at all.
Clients arrive with ambition, a budget and pressure to automate everything. What they rarely arrive with is evidence. Automating on instinct means putting agents on top of broken processes, burning cash and calling it transformation.
#NoBullshit is our answer to that: challenge the assumptions, validate the real business case, recommend whatever creates the most value – including improving the process first, or advising against automation entirely. Doing that credibly requires seeing the work as it actually happens, not as the flowchart claims. At enterprise scale, that needs measurement.
The Solution
KYP.ai is the discovery and measurement layer we bring in when the evidence has to hold up.
A digital X-ray of how work actually gets done
The platform captures real activity across desktops, applications and systems, including the manual steps, exceptions and workarounds that never reach a system log. We use that picture to expose digital waste and separate the process on the slide from the process in the daily work. Nothing gets automated until the waste is cleared and the case is proven.
Every candidate arrives with a number
Each opportunity is quantified in FTE and currency before a decision is made. That turns the conversation from “where could we automate” into “where should we”, and the second question is the one our recommendations are built on. Clients see what fixing something is worth, not only that it is technically possible.
More than RPA
One discovery layer feeds two roadmaps. The data tells us where classic RPA still pays and where Digital Agents earn their ROI, and just as often where neither is the right answer. That is what keeps the recommendation honest as buyer demand shifts from rules-based bots toward agents.
The Results
The proof is in the roadmaps we put in front of clients. Three anonymised engagements show the pattern. The figures below are identified improvement potential and benchmark-adjusted estimates – the quantified case we build before deployment, not realised post-implementation savings.
Customer A – IT solutions and industrial automation
Structured analysis surfaced five concrete automation candidates, with productivity improvement potential ranging from 50% to 90%. The strongest single candidate showed 90% potential, an estimated 7 FTE saving and roughly USD 182,000 a year. Across all five, the total reached 17 FTE and approximately USD 442,000 in annual savings potential.
Customer B – manufacturing
Using KYP.ai data analysis in Power BI, improvement patterns and AI Concierge prompting, we identified 5.39 FTE of improvement potential across three categories: AI Agents, IPA and RPA. After applying implementation benchmarks from prior projects, the realistic adjusted saving was an estimated 2.70 FTE, representing 11% of the measured scope. The largest opportunity sat in AI Agents: 3.18 FTE before adjustment, 1.59 FTE after.
Customer C – logistics
Analysis identified 8.71 FTE of improvement potential across productivity, RPA, AI Agents, IPA, communication and automatic time tracking. After benchmark adjustment the realistic estimate was 5.76 FTE, representing 24% of the measured scope. The biggest opportunities came from AI Agents (3.85 FTE potential, 1.93 FTE adjusted) and productivity improvements (3.18 FTE potential, 2.86 FTE adjusted).
“We show what the work really looks like. Office Samurai turns that into automation that has to justify itself in ROI, not in enthusiasm. Clients get the benefit of both, and the projects we have delivered together are the proof.”
Jakub Lutter, Senior Manager Partnerships & Alliances at KYP.ai
Delivery in Practice – the #NoBullshit model
The discipline is the product. Where KYP.ai is part of the engagement, it sits in the first three steps.
1. Understand first
Discovery before any recommendation. See the real process, not the flowchart.
2. Clear the waste
Fix or retire broken processes before automating them. No bots on top of chaos.
3. Quantify the should, not just the could
ROI and FTE attached to every opportunity before a decision is made.
4. Build the right roadmap
RPA, IPA and AI Agents, chosen on evidence rather than on whichever tool is in fashion.
5. Hand over, do not hook in
Train the client’s internal teams to read the data and run the platform. Capability, not dependency.
6. Govern and support
One accountable partner across the lifecycle.
Key Takeaways
Understand before you automate
Automation on instinct scales the wrong work. A digital X-ray of how work actually happens lets you clear the waste, or retire a process, before a single agent is built.
Almost anything can be automated
The question is what it is worth. Attaching ROI to every opportunity before deployment is what separates a roadmap from a wish list.
Two roadmaps, one data layer
The same discovery shows where classic RPA still pays and where AI Agents do. That way the recommendation follows the evidence, not the trend.
Build capability, not dependency
Training the client’s own team to read the data and run the platform is not a giveaway. It is what makes the partnership trusted enough to last.
