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SOLV · Solventum Corporation

Medical - Care Facilities · mkt cap $13.2B · calls: Q1 FY2026 vs Q4 FY2025
51.0 conviction · conf-adj 51

conf 3/10 partial

enthusiasm:21.0 · trend:8 · quantifies:0 · impact:0 · under_radar:14 · credibility:0 · business_impact:4 · disruption:0 · commitment:0 · confirmation:4

Enthusiasm latest 7 / prev 7 (rising)

Solventum’s AI story is almost entirely Health Information Systems / revenue cycle management autonomous medical coding: management treats AI as an enabler, not the moat—rules, compliance rigor, proprietary data, and workflows are. Enthusiasm is substantive but disciplined (opportunity > threat; AI is not sufficient alone), with rising specificity on adoption (50% of customers; 80%–90% of codes eventually autonomous) but no disclosed AI revenue, margin, or bookings attribution. Credibility rests on long tenure in coding, scale datasets, and traction claims in inpatient/outpatient RCM, not on quantified financial impact from AI.

GROUNDED NEXT-FY IMPACT vs CONSENSUS

Grounded on actual base — revenue $8.3B · net income $1.6B · net margin 18.7% · diluted EPS 8.88

These are next-fiscal-year annual uplift estimates, not next-quarter numbers.

Aggregate next-FY est. rev uplift: % · next-FY EPS uplift: % · vs analysts: unclear · priced in: low (model's call-read: high; verdict above is the hard-data one used for ranking) · confidence: 3/10

ClaimFigureArithmeticNext-FY Rev %Next-FY EPS %
80–90% of all inpatient/outpatient coding eventually fully autonomous
other · soft
80% to 90% of codingCapability/TAM statement with NO timeframe ('eventually') and NO disclosed revenue base for autonomous coding. The line sits inside HIS (annualized ~$342M×4=$1,368M, ~16.4% of total revenue), but no $/customer, revenue-per-code, pricing uplift or internal cost-save base is given. Customer economics (FTE elimination) accrue client-side per management. Cannot translate a code-automation share into next-FY revenue/EPS without inventing a P&L bridge — declined.
~50% of customers move to autonomous coding (strategic plan period)
engagement · soft
close to 50% of customersPenetration/adoption target affecting HIS. HIS annualized ~$1,368M is disclosed, but per-customer autonomous-coding revenue, ACV/take-rate uplift on conversion, customer count and starting penetration are NOT disclosed, and the horizon is the multi-year strategic plan, not next FY. No way to size next-FY incremental revenue from a customer-count percentage without fabricating per-customer economics — declined. Not in aggregate.
~1M+ proprietary reimbursement coding rules/algorithms (AI moat)
other · soft
close to 1 million plusCompetitive-moat / training-data asset metric (rules count). No revenue, margin or savings figure attached; not P&L-quantifiable without assumed share/pricing effects.
HIS organic sales +4.7% (Q1 FY2026; AI not broken out)
revenue · soft
4.7% organic on $342M segment salesQ1 FY26 HIS sales $342M; prior-year Q1 = $342M/1.047 = $326.65M; one-quarter YoY Δ = $15.35M. Annualized at Q1 run rate HIS ≈ 4×$342M = $1,368M; if +4.7% held full year, segment Δ ≈ $64.30M → consolidated rev_uplift_pct ≈ 100×$64.30M/$8,325M = 0.77%. BUT management attributes growth to RCM + performance management broadly, AI NOT broken out → AI-attributable slice unisolable, already in segment-growth consensus. Illustrative-only EPS ceiling @18.69% net margin on consensus NI ≈ $12.0M (~1.1%) — not AI-attributed, excluded from aggregate.
HIS organic sales +3.2% (Q4 FY2025; AI not broken out)
revenue · soft
3.2% organic on $348M segment salesQ4 FY25 HIS sales $348M; prior-year Q4 = $348M/1.032 = $337.21M; one-quarter YoY Δ = $10.79M. Annualized HIS ≈ 4×$348M = $1,392M; if +3.2% full year, segment Δ ≈ $44.54M → consolidated rev_uplift_pct ≈ 0.54%. AI not broken out (RCM software + performance management) → not AI-attributable; informs HIS run-rate (~$1.37–1.39B, ~16–17% of total) only. Excluded from AI aggregate.

Assumptions: Next-FY = FY2026. Solventum is adopter-side (AI improves its OWN HIS software; it does not sell AI compute/chips), so no supplier side. Would-be defaults if a $ anchor existed: 21% tax on opex saves (none quantified); incremental margin at the software/services end (HIS is the software segment, ~$1.37B, ~16.4% of total); EPS sized off consensus ADJUSTED basis (~$1.06–1.15B NI / 175.3M shares, EPS ~$6.03–6.55), NOT the GAAP base ($1,556M / $8.88) which is distorted by one-offs. None applied — no claim carries a next-FY AI dollar anchor. HIS annualized from disclosed quarterly sales ($342M Q1 FY26, $348M Q4 FY25). No bookings/revenue conversion (no bookings $ claims). Customer ramp to ~50% not phased to FY26 absent disclosed starting penetration or $/account uplift.

Top line: Not quantifiable from disclosure. Every AI claim is a capability/adoption/moat metric (80–90% of codes eventually autonomous; ~50% of customers converting over a multi-year plan; ~1M+ proprietary rules) with no attached revenue dollars and, where a horizon exists, it is multi-year not next-FY. The only hard revenue figures (HIS organic +4.7% Q1FY26 / +3.2% Q4FY25, ~$1.37B annualized, ~16% of total, ~$45–64M annualized segment increment = ~0.5–0.8% of consolidated revenue) explicitly do NOT break out AI, so the AI-attributable slice cannot be isolated. Adopter-side aggregate rev uplift = null.

Bottom line: No quantified Solventum opex/FTE or margin expansion from AI. Customer economics (FTE elimination, productivity, better revenue capture) accrue to Solventum's CUSTOMERS, not as Solventum opex savings, so no cost-saving dollar flows to EPS. Denominator caution: consensus adjusted NI (~$1.06–1.15B) sits BELOW GAAP ($1,556M), so any EPS% must be sized off the lower adjusted base — moot, numerator unquantified. Hypothetical non-AI ceiling: all annualized HIS organic growth ($64.3M at 4.7%) at 18.69% margin ≈ $12.0M NI ≈ 1.1% vs consensus — not AI-specific, not in aggregate. Consensus EPS path FY25→FY26→FY27 ≈ $6.03→$6.55→$7.05 (+8.6% then +7.6%); no incremental AI EPS layered on. est_eps_uplift_pct = null.

[impact n/m (all claims soft/unanchored)] Adopter-side aggregate est_rev_uplift_pct and est_eps_uplift_pct are null (no anchored AI $). Consensus revenue is roughly flat-to-down then recovering: FY2025 $8,255M → FY2026 $8,186M (−0.8%; ~$139M / ~1.7% below FY25 actual $8,325M) → FY2027 $8,473M (+3.5%), with EPS growth ~+8.6% then ~+7.6% — modest, margin/deleverage-driven, not an AI inflection. The in-numbers HIS growth (~3–5% on ~16% of revenue ≈ ~0.5–0.8% of total) is consistent with what consensus already carries, so the realized, quantified AI contribution looks priced in. The transformational optionality (80–90% of coding eventually autonomous, ~50% customer conversion) is genuinely NOT in near-term consensus but is unquantified and long-dated, so no math points above consensus. Without a management $ target, cannot show consensus is behind on AI; absence of quantified upside is consistent with AI embedded in the organic HIS narrative rather than modeled as discrete upside.

MODEL CONSENSUS (impact)

partial

Near-identical answers: both null all AI uplift pcts, adopter-side, unclear vs consensus, confidence 3. Only priced_in (high vs medium) and a few type labels differed.

Conflicts reconciled
FieldOpus 4.8GPT-5.5
Rev uplift %
EPS uplift %
Priced inmedium
vs analystsunclear
Confidence3
Top lineNot quantifiable from disclosure. Every AI claim is a capability/adoption metric (80-90% of codes eventually autonomous; ~50% of customers converting over a multi-year plan; ~1M+ proprietary rules) with no attached revenue dollars and, where a timeframe exists, it is multi-year rather than next-FY. The only hard revenue figures (HIS organic +4.7% Q1FY26 / +3.2% Q4FY25, ~$1.37B annualized, ~16% of total) explicitly do NOT break out AI, so the AI-attributable slice cannot be isolated. Forward statements confirm 'no Solventum P&L figures.' Headline adopter-side uplift = null.
Bottom lineNo bottom-line AI figure is disclosed. Customer economics (FTE elimination, productivity, better revenue capture) are stated as accruing to Solventum's CUSTOMERS, not as Solventum opex savings, so there is no cost-saving dollar to flow to EPS. Note also the denominator caution: consensus net income (~$1.06-1.15B) sits BELOW the GAAP base ($1,556M), so any EPS% would have to be sized off the lower adjusted base — moot here since the numerator is unquantified. est_eps_uplift_pct = null.
ReasoningConsensus revenue is essentially flat-to-down then recovering (8,255M→8,186M = -0.8%, →8,473M = +3.5%) with EPS growth of +8.5% then +7.8% — i.e. modest growth driven by post-spin margin/deleverage, not an AI inflection. The in-numbers HIS growth (~3-5% on ~16% of revenue ≈ ~0.5-0.8% of total) is consistent with what consensus already carries, so the realized, quantified AI contribution looks priced in. The big optionality — 80-90% of coding eventually autonomous and ~50% customer conversion — is genuinely NOT in the near-term consensus, but it is also unquantified and long-dated, so there is no math pointing 'above' consensus to call it a clear low-priced-in opportunity. Net: the quantified piece is roughly in the numbers; the transformational piece is unpriced but unsizable.

Rows highlighted where the two models disagreed.

QUANTIFICATIONS
Eventual share of codes fully autonomous (inpatient + outpatient): 80% to 90% (eventually (management confidence increasing), both)
“The good news is our team's confidence is increasing in how much coding can eventually be fully autonomous—now talking 80% to 90% of all coding, inpatient and outpatient.”
Customer adoption — share of customers on autonomous coding: close to 50% (during the current strategic plan period, topline)
“during the current strategic plan period, our assumption—given our progress and the trust customers have in our capabilities—is that we could get close to 50% of our customers moving over to autonomous coding.”
Proprietary reimbursement coding rules/algorithms (AI training moat): close to 1 million plus (built over decades (cumulative capability), both)
“actually close to 1 million plus of those rules and algorithms, which is substantial.”
HIS organic sales growth (segment; AI not broken out): 4.7% (Q1 FY2026, topline)
“Our Health Information Systems had another strong result with $342 million in sales, an increase of 4.7% on an organic basis, driven by strength across revenue cycle management and performance management solutions”
HIS organic sales growth (segment; AI not broken out): 3.2% (Q4 FY2025, topline)
“Our HIS segment also contributed to our performance with $348 million in sales, an increase of 3.2% on an organic basis, driven by revenue cycle management software solutions and performance management solutions.”
PAST (realized)
CURRENT (now)
FORWARD (guidance)
TRACK RECORD — PROMISE vs DELIVERY

/100 (no quantified promises)   no-quantified-promises  6 calls reviewed

Across Q4 FY2024–Q1 FY2026, Solventum repeatedly promoted AI-driven autonomous coding and RCM momentum but never set a dated numeric AI target (automation rate, revenue, or productivity by year). The only AI figure was a 50%–90% addressable-case automation potential in Q4 FY2024, which lacks a milestone and reads as market sizing, not a deliverable commitment.

PRICED-IN (REFINED)
LOW (room left)

Est. revisions falling  ·  Fwd P/E 12.9  ·  EV/Sales 2.2x

AI claim maps to Product

Analyst price targets have stepped down (lastYearAvg 89.54 → lastQuarterAvg 83.14 → lastMonthAvg 79.6), and rating counts show mild bearish drift (strongBuy 2→1; sells 1→2), while forward revenue is essentially flat (~$8.26B to ~$8.47B through 2027) with only modest EPS growth—little evidence consensus is baking in an AI-driven re-rating. Valuation is not stretched (fwd P/E ~12.9, EV/Sales ~2.2, TTM P/E ~9.5), so cheap multiples plus falling/flat revisions argue AI upside is not yet in the price. Any AI efficiency or revenue lift would most plausibly show up in the single reported line, Product (~$6.35B), not in geographic mix alone.
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
8Q4 FY20246Q1 FY20257Q2 FY20257Q3 FY20258Q4 FY20258Q1 FY2026

AI enthusiasm across 6 calls — trend ↗ rising

HIS autonomous coding stayed the sole AI story, deepening from 50-90% potential to Ensemble traction, acceptance metrics, and data-rules differentiation.

RECENT AI CATALYSTS & NEWS
BUSINESS IMPACT - QUALITATIVE MATERIALITY

6/10 qualitative impact   moderate  medium-term · soft evidence

Where AI matters: HIS/RCM autonomous medical coding

Autonomous coding is a real, differentiated product vector in ~16% HIS with rising adoption specificity (50% customers, 80-90% codes eventually) and a rules/data moat narrative, but management discloses no AI-attributable revenue or margin and consolidated financial uplift is unquantifiable versus ~84% non-AI Product revenue.

Caveats: No disclosed AI revenue, margin, or bookings—upside remains capability/adoption narrative only; HIS organic growth (~3-5%) is modest and not AI-broken out, so realized upside may already be in segment run-rate; Long-dated 50% customer / 80-90% code autonomy targets lack dated P&L bridges; Generic autonomous-coding competitors could pressure RCM pricing or erode differentiation over time

AI DISRUPTION / CANNIBALIZATION RISK  tailwind · 2/10

Solventum monetizes AI-enabled autonomous coding and compliance-heavy RCM software rather than selling billable labor hours; the core ~$6.4B Product franchise is not structurally automated away, and near-term coding disruption favors entrenched proprietary rules/datasets over generic LLM commoditization.

OPTIONS / MARKET STRUCTURE

option liquidity: fair

ATM IV
TYPICAL BID-ASK
OPEN INTEREST

proxy inputs — dollar-ADV $127M · beta 0.655 · px $78.07

source: proxy (no options chain on FMP)
FMP /stable/ exposes no options-chain endpoint on this key, so ATM IV, bid-ask spread and open interest are unavailable. Liquidity below is a PROXY from dollar-ADV, beta and price level (a stand-in for option depth), not measured option-market data.

CONFIRMATION — INSIDERS · 13F · LANGUAGE
Confirming — insiders buying, institutions flat, management language 5/10 measured.
INSIDERS buying 1 open-market buy(s) vs 0 sell(s) — net accumulation
INSTITUTIONS (13F) flat as of 2026-03-31: 82 new / 148 closed positions; 352 increased / 280 reduced; institutional ownership +1.54pp; -69 net 13F holders
MGMT LANGUAGE 5/10 measured Real HIS autonomous-coding ownership and traction; AI framed as supportive tool, no AI metrics or timelines.
commit “our autonomous coding offering continues to gain traction in both outpatient and inpatient settings.”
commit “We are differentially able to leverage AI thanks to our unique ability to efficiently and effectively train it.”
commit “The economics of autonomous coding are compelling.”
VERBATIM AI QUOTES
“Inside RCM, our autonomous coding offering continues to gain traction in both outpatient and inpatient settings.”
— Bryan Hanson, Q1 FY2026
“Relative to AI and autonomous coding, I will reiterate what I said on our last call. We see AI as a helpful tool to deliver better outcomes when it comes to autonomous coding, but what differentiates the outcomes is the data, the rules, and the rigor behind them. We are differentially able to leverage AI thanks to our unique ability to efficiently and effectively train it. We built deep rules and algorithms designed to assure accurate and compliant reimbursement coding, and this, combined with our vast datasets and proprietary workflows, allows us to more effectively train and maximize AI and, ultimately, deliver autonomous coding that our customers can trust.”
— Bryan Hanson, Q1 FY2026
“The economics of autonomous coding are compelling. Our customers benefit by improving productivity, eliminating FTE cost infrastructure, and improving revenue capture thanks to increased accuracy.”
— Bryan Hanson, Q1 FY2026
“The good news is our team's confidence is increasing in how much coding can eventually be fully autonomous—now talking 80% to 90% of all coding, inpatient and outpatient.”
— Bryan Hanson, Q1 FY2026
“during the current strategic plan period, our assumption—given our progress and the trust customers have in our capabilities—is that we could get close to 50% of our customers moving over to autonomous coding.”
— Bryan Hanson, Q1 FY2026
“we continue to see adoption of 360 Encompass progress against our international expansion efforts and gains in autonomous coding.”
— Bryan Hanson, Q4 FY2025
“relative to autonomous coding, our strong automation and acceptance rates are further positioning us as the largest, and importantly, most capable autonomous coding vendor. Over decades, we've built deep rules and algorithms designed to ensure accurate and compliant reimbursement coding. This, combined with our vast data sets and proprietary workflows, uniquely positions us to leverage AI-driven autonomous coding, our customers can trust.”
— Bryan Hanson, Q4 FY2025
“We actually see AI as an opportunity more than we do a threat.”
— Bryan Hanson, Q4 FY2025
“Number one, I think we see artificial intelligence as a lever to drive autonomous coding. That's why we've been spending so much in that area, and that's what's driving us in autonomous coding. But we don't see it by itself as the answer to autonomous coding. I think that's important, by itself is not the answer. It's just a piece of the equation.”
— Bryan Hanson, Q4 FY2025
“We see it as a tool, a variable in the equation to solve for autonomous coding. Remember, autonomous reimbursement coding, not computer coding, right?”
— Bryan Hanson, Q4 FY2025
“we truly do believe that we're differentially capable of using AI because, number one, we've been in the market for decades. And as a result of that, we have vast number of proprietary. I'm going to call it algorithms and rules that we have around reimbursement coding, actually close to 1 million plus of those rules and algorithms, which is substantial. And of course, because we have been working at scale with the hospitals, we have very vast data sets as well. So we really believe that what we have available to us allows us to train AI in ways that others can't.”
— Bryan Hanson, Q4 FY2025
“in HIS, you've seen various applications in autonomous coding and a lot of applications for Encompass 360 when we look at outside the U.S. implementation.”
— Bryan Hanson, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Rick White (Stifel)): Shifting to Health Information Systems, can you approximate your current mix of full AI autonomous coding versus primarily traditional computer-assisted coding? If not revenue, maybe from a customer adoption standpoint?
A: Bryan Hanson: Confidence is increasing that 80%–90% of all coding (inpatient and outpatient) can eventually be fully autonomous; in practice implementation takes time so today there is a mix across customers. During the current strategic plan period, assumption is close to 50% of customers could move to autonomous coding, with autonomous share within those accounts expanding over time from initial swim lanes. Value proposition: FTE infrastructure reductions, faster productivity and speed to reimbursement, improved revenue capture from fewer mistakes; moving rapidly but safely given compliance and revenue implications.
Q (Q4 FY2025, Travis Lee Steed (Bank of America)): since there's been more focus on the health care IT business and some of the AI stuff that's going on, just would kind of love to give you the opportunity to kind of maybe explain that and explain your business a bit more for investors.
A: Bryan Hanson: AI is an opportunity more than a threat. AI is a lever for autonomous coding (where they have been spending), not the sole answer—it's a tool in the equation for autonomous reimbursement coding. They believe they are differentially capable because of ~1 million+ proprietary reimbursement rules/algorithms and vast hospital-scale datasets that let them train AI in ways others cannot.
Q (Q4 FY2025, Ryan Zimmerman (BTIG)): Following up on the HIS comments, and there's been a lot of investor focus on this... how should we think about maybe what's contractually obligated over a certain time period or any other additional details... around the HIS business?
A: Bryan Hanson: Multi-year contracts and switching costs provide guardrails, but differentiation matters—leader today and expected to lead the transformation. Trust and compliance risk if coding is wrong. Views some autonomous coding competitors as "risking autonomous coding" without Solventum's proprietary rules/algorithms; sees opportunity not risk at this point, not relying on contracts alone.