The SteepRock Evidence Desk
ROI claims in life sciences technology tend to arrive as round numbers or enticing percentages with no visible math or traceability. This article takes the opposite approach. It walks through ten weeks of measured usage data from a live deployment of SteepRock's Embedded Intelligence (EI) layer at a global medical technology company, separates what was measured from what was assumed, and shows the calculation in full. The client cannot be named here, but the underlying data — usage logs, deliverable counts, completion rates — is theirs, drawn from production deployment between late April and early July 2026.
What Embedded Intelligence Is
Embedded Intelligence is SteepRock's unified intelligence layer: an AI capability that works natively across a client's own data, applicable proprietary and licensed sources, and SteepRock's Opinion Leader Management System (OLMS) and Opinion Leader Analytics (OLA) search engine. Rather than answering questions in isolation, EI performs cross-domain inquiry, analysis, and insight generation — and produces finished work products: customer profiles, congress strategies, advisory-board plans, executive presentations, and intelligence digests, assembled directly from the data already in production.
The value of EI is not measured in answers delivered but in deliverables completed — the research-and-synthesis work that otherwise consumes hours of skilled staff time per item.
Ten Weeks - Measured
Between April 29, 2026 and July 7, 2026, a nine-person commercial team at a top 10 global med tech organization put EI into daily use. The headline facts below are measured from usage logs, not estimated:
Adoption was full and sustained. All nine licensed users were active in every one of the ten weeks. This was not a pilot with a burst of curiosity followed by decay — usage held week over week, embedded in the daily workflow.
The work was substantial. The team completed 143 deliverables in the period, concentrated in the highest-value engagement work: KOL and customer profiling — the single most time-consuming category of manual research — accounted for nearly half of all engagements, followed by congress strategy, executive slides and briefs, and advisory-board planning. Annualized, the run-rate is approximately 756 deliverables per year, or about 2,270 recovered staff hours.
The output was dependable. Roughly 90% of tasks completed successfully (292 successful deliverable runs out of 323 total turns), which means the time saved was realized rather than lost to rework.
The Math, With Assumptions Labeled
The model is simple: measured work volume × time saved per deliverable × fully loaded cost per hour.
Two of those three inputs are measured. Deliverable volume, active users, and completion rate come straight from the usage logs. The other two are modeled — stated management assumptions: hours saved per deliverable (a base case of 3 hours, which is conservative for a full customer profile or a congress plan) and a fully loaded employee cost of $250,000–$300,000 per year, or $120–$144 per hour on a 2,080-hour basis.
Run the model across three scenarios and the reading is straightforward: even the base case clears the fully loaded cost of one employee. A nine-person team, ten weeks in, is already generating approximately one full-time employee of additional capacity. EI is, in effect, a self-funding headcount.
How sensitive is the result to those assumptions? The table below shows the annualized value at three hours-saved levels and both ends of the loaded-cost range, so readers can locate the break-even point and the upside for themselves. Against a fully loaded employee cost of $250,000–$300,000, the model breaks even at roughly 2.3–3.3 hours saved per deliverable — below the base case, and well below what a full customer profile or congress plan plausibly saves.
Value @ $120/hr
Hours saved per deliverable
Value @ $144/hr
Value @ $144/hr
FTE equivalent
1.5 hrs (below break-even)
Publications, congresses, guidelines, advisory boards
Social media, podcasts, video, online communities
~0.5
3 hrs (base case)
Deep, institutional, slow-building
Fast, broad, conversational
~1.1
6 hrs (upside)
Peer specialists, institutions
Clinicians, trainees, patients, public
~2.2
