← back to rankingWM · Waste Management, Inc.
Waste Management · mkt cap $85.1B · calls: Q1 FY2026 vs Q4 FY2025
62.0 conviction · conf-adj 61
conf 5/10 partial
enthusiasm:21.0 · trend:8 · quantifies:5 · impact:0 · under_radar:5 · credibility:12 · business_impact:8 · disruption:0 · commitment:0 · confirmation:3
Enthusiasm latest 7 / prev 6 (rising)
WM treats AI as embedded operations tech—not a new revenue line—with the strongest disclosed wins in computer-vision pricing/contamination (Smart Truck, ~95% auto-processed images), recycling-plant intelligent automation, and AI-driven driver coaching tied to safety and retention. Enthusiasm is pragmatic and rising (Q1 had deeper AI Q&A and autonomy pilots) but management stresses "early innings," healthcare tech lag, and refuses a company-level AI margin target despite peer 100 bps talk. Credibility is supported by long deployment history and segment P&L outcomes (recycling EBITDA up 18% in Q1 on automation), though most quantified figures are operational (images, % automated) rather than AI-attributed dollars or margin points.
GROUNDED NEXT-FY IMPACT vs CONSENSUS
Grounded on actual base — revenue $25.2B · net income $2.7B · net margin 10.7% · diluted EPS 6.7
These are next-fiscal-year annual uplift estimates, not next-quarter numbers.
Aggregate next-FY est. rev uplift: 0.0% · next-FY EPS uplift: -2.62% · vs analysts: inline · priced in: medium (model's call-read: high; verdict above is the hard-data one used for ranking) · confidence: 5/10
| Claim | Figure | Arithmetic | Next-FY Rev % | Next-FY EPS % |
|---|
Smart Truck: 300M images/yr, ~95% auto-processed
productivity · soft | 300M images/yr, ~95% no-touch (285M auto, 15M manual) | Real labor-avoidance but NO $/image labor cost or review opex base disclosed in inputs → cannot convert volume to $ rev or EPS without inventing a unit cost. Operational, already in run-rate. | | |
AI-enabled truck camera tenure (6-7 yrs)
other · soft | 6-7 years | Deployment tenure only; benefits already embedded in current margins & consensus. No incremental financial anchor. | | |
Smart Truck technology tenure (~decade)
other · soft | ~a decade | Technology tenure only; benefits in run-rate. No financial anchor. | | |
Routing/logistics AI across ~19,000 trucks
productivity · soft | 19,000 trucks | Fleet-scale routing context; no disclosed $/truck fuel, labor, or route-efficiency saving → no company-level $ impact. | | |
AI driver coaching across 20K+ drivers
productivity · soft | 20K+ drivers | Headcount coverage disclosed; no coaching $/driver saving, accident-cost, or fuel delta disclosed. | | |
Driver/technician turnover 17.2%, -130 bps YoY
cost · soft | 17.2% (-130 bps) | Chained disclosed bases: 20,000 × 1.30% ≈ 260 fewer turnover events/yr. No replacement/training $/event in inputs → cannot size $ savings. | | |
Recycling op EBITDA +18% (Q1'26 YoY), automation lowers labor
cost · soft | +18% EBITDA | Genuine AI/automation benefit but recycling segment EBITDA $ base NOT in inputs → cannot map 18% to consolidated rev/EPS; also blended with volume & commodity effects. | | |
Recycling volume +9% YoY (Q1'26)
engagement · soft | +9% volume | Segment volume; no recycling segment revenue $ or revenue/ton disclosed → not convertible to consolidated %. | | |
Recycling op EBITDA >22% (FY25) despite ~-20% commodity
cost · soft | >22% EBITDA | Impressive but no segment EBITDA $ base in inputs → unanchored at consolidated level. | | |
Tech-initiative spend = 40 bps margin drag (Q1'26)
cost | 40 bps | ANCHORED to disclosed consolidated revenue. 0.40% × $25,204M = $100.8M pre-tax; ×(1-21%)=$79.6M after-tax; / consensus adj NI $3,037.8M = -2.62% EPS (~-$0.20/sh on 404.2M sh). This is the COST of the AI program, a near-term drag. | 0 | -2.62 |
Peer/analyst 100 bps margin uplift from AI/pricing (not WM)
other · soft | 100 bps | Analyst/peer expectation explicitly NOT adopted by WM management → not WM's claim, excluded from aggregate. Illustrative only: 1.00% × $25,204M = $252M pre-tax, ~$199M NI, ~6.6% vs cons NI. | | |
Assumptions: EPS uplift sized against consensus ADJUSTED net income ($3,037.8M / $7.51 adj EPS), not GAAP NI ($2,708M). Tax rate 21%. Incremental flow-through at current net margin (~10.7%) where applicable. 40 bps drag annualized off disclosed FY revenue ($25,204M); ~404.2M shares. Phasing: all WM AI is already-deployed/sustaining (cameras 6-7 yrs, Smart Truck ~decade), so benefits are in the run-rate, not a future step-up. Segment-level claims (recycling) and turnover/image productivity left soft because no recycling revenue/EBITDA $ base or unit-cost is disclosed in the inputs. No bookings claims present.
Top line: Effectively none that is quantifiable. WM's AI is cost/quality/safety-oriented, not a new revenue stream. Smart Truck (300M images, 95% auto), +9% recycling volume, and the 19,000-truck routing fleet are operational metrics without disclosed segment revenue or pricing bases — cannot be converted to consolidated rev %. AI is a margin story here, not a topline story.
Bottom line: The only line anchoring to a disclosed base is NEGATIVE: the 40 bps tech-initiative drag ≈ $100.8M annualized spend, ~$79.6M after-tax, ~-2.62% vs consensus adj NI. Offsetting benefits are real (95% no-touch image processing, recycling automation driving +18-22% segment EBITDA, lower driver turnover at 17.2%/-130 bps on 20K+ drivers) but none carries a disclosed dollar base, so they cannot be sized without inventing numbers — they remain soft. Net adopter-side hard EPS impact is a small near-term investment drag, not positive uplift.
Consensus already embeds strong adjusted-EPS growth: $7.51 (FY25) -> $8.18 (+9.0%) -> $9.21 (+12.6%) on +4.9%/+5.5% revenue growth. WM's AI is sustaining and long-deployed (cameras 6-7 yrs, Smart Truck ~decade), so its labor/quality benefits are in the run-rate consensus extrapolates — and management is openly spending more now (the 40 bps / -2.62% drag) to fund the next leg. No anchored incremental figure lands ABOVE this trajectory; the one hard number nets slightly negative near-term. Hence priced_in=high and inline. The interesting unpriced optionality (remote heavy-equipment/landfill autonomy, post-collection tech) is explicitly pilot-stage and unquantified — upside exists but is not sizable today.
MODEL CONSENSUS (impact)
partial
Near-identical answers; both adopter, inline, priced_in high, conf 5. Reconciled the rounding (-2.62%) and set hard rev aggregate to 0.
Conflicts reconciled
- eps_uplift_pct/est_eps_uplift_pct: X=-2.6 vs Y=-2.62 -> used -2.62 because Y showed full arithmetic ($79.645M/$3,037.8M) while X rounded
- est_rev_uplift_pct: X=null vs Y=0 -> used 0 because the one anchored claim has rev_uplift_pct=0 and aggregate hard revenue uplift is genuinely zero
| Field | Opus 4.8 | GPT-5.5 |
|---|
| Rev uplift % | – | – |
| EPS uplift % | -2.6 | – |
| Priced in | high | – |
| vs analysts | inline | – |
| Confidence | 5 | – |
| Top line | Effectively none that is quantifiable. WM's AI is cost/quality/safety-oriented, not a new revenue stream. Recycling volume +9% and the routing fleet are real, but no segment revenue base is disclosed to convert them — so est_rev_uplift_pct is null, not zero-conviction. AI is a margin story here, not a topline story. | – |
| Bottom line | The only line that anchors to a disclosed base is a NEGATIVE one: the 40bps tech-initiative drag = ~$100.8M annualized spend, ~-$79.6M after-tax, ~-2.6% vs adj NI. The offsetting benefits are real (95% no-touch image processing, recycling automation driving +18-22% segment EBITDA, lower driver turnover at 17.2%) but none carries a disclosed dollar base, so they cannot be sized without inventing numbers — they remain soft. Net: a mature, self-funding automation program whose visible benefits are already embedded in margins and consensus, with current incremental investment showing up as a small near-term EPS drag. | – |
| Reasoning | Consensus already embeds strong adjusted-EPS growth: $7.51 (FY25) -> $8.18 (+9.0%) -> $9.21 (+12.6%), on +4.9%/+5.5% revenue growth. WM's AI is sustaining and long-deployed (cameras 6-7 yrs, Smart Truck ~decade), so its labor/quality benefits are in the run-rate that consensus extrapolates — and management is openly spending more now (the 40bps/-2.6% drag) to fund the next leg. There is no anchored incremental figure that lands ABOVE this trajectory; the one hard number nets slightly negative near-term. Hence priced_in=high and inline. The interesting unpriced optionality (remote heavy-equipment / landfill autonomy, post-collection tech) is explicitly pilot-stage and unquantified — upside exists but is not sizable today. | – |
Rows highlighted where the two models disagreed.
QUANTIFICATIONS
Smart Truck image volume: over 300 million images a year (current / annual run rate, both)
“we're using different forms of technology and AI to process about 95% of those images without a human having to touch them”
Automated image processing rate (Smart Truck): about 95% (current, both)
“we're using different forms of technology and AI to process about 95% of those images without a human having to touch them”
AI-enabled truck camera deployment tenure: probably 6 or 7 years (past to present, topline)
“We've been using these AI-enabled cameras now for probably 6 or 7 years”
Smart Truck technology tenure: probably closer to a decade (past to present, both)
“That technology on the truck I'm speaking of has been around for probably closer to a decade.”
Fleet scale for routing/logistics AI context: call it, 19,000 trucks (current, bottomline)
“What we're doing from a routing and logistics perspective with now, call it, 19,000 trucks we have on the street with the Health Care business.”
Drivers covered by AI coaching example: 20-plus thousand drivers (current, bottomline)
“we're using AI is one example from a coaching perspective with the 20-plus thousand drivers we have.”
Driver/technician turnover (linked to tech-enabled roles): 17.2% (Q1 FY2026, bottomline)
“total driver and technician turnover, both voluntary and involuntary remained low at 17.2%, improving 130 basis points year-over-year.”
Recycling segment operating EBITDA growth (automation/AI-linked in call): 18% (Q1 FY2026 YoY, bottomline)
“Operating EBITDA grew by 18% as we realized automation benefits that lower labor costs and higher quality material”
Recycling volume processed: 9% more volume (Q1 FY2026 YoY, both)
“processed 9% more volume”
Recycling segment operating EBITDA growth (prior year; AI cited in Q&A): over 22% (FY2025, bottomline)
“our recycling segment delivered over 22% operating EBITDA growth despite nearly 20% lower commodity prices in 2025”
Corporate technology-initiative spend drag on margin: 40 basis points (Q1 FY2026, bottomline)
“40 basis points of increased spending on technology initiatives”
Industry AI/pricing margin benchmark (analyst cite; not adopted by WM): 100 basis point improvement in margins (next few years (peer expectation), bottomline)
“they expect a 100 basis point improvement in margins over the next few years”
PAST (realized)
- Q1 FY2026 — James Fish: AI-enabled truck cameras deployed ~6–7 years; identify contamination; support price line and recycle-plant material quality.
- Q1 FY2026 — John Morris: Smart Truck on all commercial/residential trucks; ~300M images/year; AI processes ~95% without human review; in place ~a decade.
- Q1 FY2026 — John Morris: Recycling plants use automation and forms of artificial intelligence; structurally lowered operating cost model.
- Q1 FY2026 — John Morris: AI captures in-cab operating data used to coach drivers; linked to record-low turnover and best-ever Q1 safety.
- Q4 FY2025 — John Morris: Technology enablement and AI already paying off in recycling; logistics tech paying off on legacy fleet margins/OpEx momentum.
CURRENT (now)
- Q1 FY2026 — John Morris: AI coaching live for 20,000+ drivers; benefits showing in OpEx and collection & disposal margins.
- Q1 FY2026 — John Morris: Still "early innings" embedding technology; healthcare segment has not yet realized most traditional C&D tech benefits.
- Q1 FY2026 — John Morris: Actively piloting remote heavy equipment at multiple sites.
- Q1 FY2026 — David Reed: 40 bps margin headwind from increased technology-initiative spend (corporate/other), benefiting other segments.
- Q4 FY2025 — John Morris: IoT embedding at landfills underway for operational visibility.
FORWARD (guidance)
- Q1 FY2026 — John Morris: Runway to accelerate tech investments; healthcare tech upside still ahead.
- Q1 FY2026 — John Morris: Continued work on routing and logistical capabilities.
- Q1 FY2026 — John Morris: Remote heavy-equipment pilots as a potential pathway to landfill autonomy.
- Q4 FY2025 — John Morris: Continued opportunity on post-collection/landfill technology to drive operating costs down.
TRACK RECORD — PROMISE vs DELIVERY
90/100 track record delivers 6 calls reviewed
WM rarely frames goals as explicit AI targets but does set quantified automation milestones (recycling plants, ERP synergies, tech-driven margin/cost); on judgeable 2025 items they delivered or beat guidance with no quiet walk-backs.
7 next-gen automated recycling plants scheduled to come online in 2025 — promised Q1 FY2025
delivered By Q4 FY2025 WM reported 5 recycling automation upgrades plus 4 new-market facilities completed during 2025, with multiple automated plants commissioned through the year (e.g., Pennsylvania, Oregon).
Up to $100M Stericycle/Healthcare synergies in 2025 via ERP and workflow optimization — promised Q4 FY2024
delivered Management affirmed through Q2–Q4 FY2025 that synergy capture was on track for the upper end of $80–100M and later said it exceeded initial expectations.
~110 bps full-year Collection & Disposal margin expansion (tech-enabled cost optimization) — promised Q2 FY2025
delivered Full-year 2025 Legacy margin expanded ~180 bps normalized and C&D contributed ~120 bps from price, cost optimization and mix, beating the 110 bps projection.
Recycling automation to sustain margin uplift despite commodity pressure — promised Q1 FY2025
delivered Q1 cited 20 bps margin contribution from recycling automation; Q4 FY2025 reported 22% recycling EBITDA growth with automation-driven labor savings despite ~20% lower commodity prices.
$250M annual run-rate synergies by 2027 (ERP/operational tech integration) — promised Q1 FY2025
too-early Still in progress; Q1 FY2026 raised the target to $300M run rate by end-2027 with integration ongoing.
Sustainability capex program from 2023 substantially complete in 2026 (includes recycling automation/RNG tech) — promised Q1 FY2026
too-early Management said the program is on track for substantial completion in 2026; outcome not yet judgeable within this transcript set.
PRICED-IN (REFINED)
MEDIUMEst. revisions flat · Fwd P/E 28.2 · EV/Sales 4.2x
AI claim maps to Commercial, Industrial, Recycling Processing and Sales
Analyst ratings are stable (buy counts flat, holds up 7→9, no recent price-target updates) and forward EPS growth is modest mid-single digits—not a clear upward revision wave. Valuation is rich (fwd P/E ~28, EV/Sales ~4.2, elevated vs typical mature waste), so much quality/defensive growth is in the multiple. AI-driven upside would most plausibly flow through collection and recycling lines, but flat revisions plus stretched multiples imply partial, not full, pricing of an AI efficiency thesis—hence medium, not high (no rising estimates) or low (valuation not cheap).
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
4Q4 FY20245Q1 FY20256Q2 FY20255Q3 FY20255Q4 FY20256Q1 FY2026
AI enthusiasm across 6 calls — trend ↗ rising
Ops automation and fleet tech grew more specific; no AI/ML or GenAI narrative emerged as a driver.
BUSINESS IMPACT - QUALITATIVE MATERIALITY
7/10 qualitative impact material medium-term · mixed evidence
Where AI matters: fleet ops, recycling automation, contamination pricing, driver safety
Decade-scale Smart Truck CV (~300M images, ~95% no-touch), recycling plant automation, and driver coaching are real cost, pricing, and quality levers in core collection/recycling—not narrative—but management will not anchor company-level margin uplift and near-term spend is a disclosed drag.
Caveats: 40 bps tech-initiative spend is a near-term margin/EPS headwind with no mgmt AI margin target to offset; Segment benefits (e.g., recycling +18% EBITDA) blend automation with volume/commodities and lack AI-attributed dollars; Healthcare and landfill autonomy upside is explicitly early/pilot vs legacy C&D tech already in run-rate
AI DISRUPTION / CANNIBALIZATION RISK tailwind · 1/10
WM sells regulated physical pickup, hauling, and disposal; GenAI does not commoditize route density, permits, or landfill capacity, and WM’s own AI/autonomy pilots reduce labor cost rather than deflate what customers pay for.
OPTIONS / MARKET STRUCTURE
option liquidity: good
proxy inputs — dollar-ADV $450M · beta 0.495 · px $211.93
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
Mixed — insiders selling, institutions adding, management language 4/10 measured.
INSIDERS selling 32 open-market sell(s) vs 0 buy(s) — net distribution
INSTITUTIONS (13F) adding as of 2026-03-31: 197 new / 161 closed positions; 1155 increased / 827 reduced; institutional ownership +0.40pp; +30 net 13F holders
MGMT LANGUAGE 4/10 measured No AI/ML mentioned; automation framed with Q1 results but mostly generic technology language.
commit “we realized automation benefits that lower labor costs and higher quality material and processed 9% more volume”
commit “Automation and Technology continue to help us flex costs and drive efficiency as volumes fluctuate”
commit “our success using Technology and Automation to reduce costs”
VERBATIM AI QUOTES
“And a lot of what we're doing in the recycling facilities that Tara team have talked about with AI and robotics and automation.”
— John Morris, Q1 FY2026
“And part of what's helping us do that is we're using AI is one example from a coaching perspective with the 20-plus thousand drivers we have.”
— John Morris, Q1 FY2026
“So as much benefit as we've seen that showing up on our OpEx numbers in our collection and disposal margins, I think we still see a good bit of runway there to continue to accelerate those investments.”
— John Morris, Q1 FY2026
“As it relates to AI and pricing, we've been using AI-enabled cameras, for example, on trucks to it both helps us with the quality of the materials.”
— James Fish, Q1 FY2026
“We've been using these AI-enabled cameras now for probably 6 or 7 years on and it is interesting watching the word because they're able to identify pretty accurately non-recycled materials coming out of that can.”
— James Fish, Q1 FY2026
“And then we're able to contact a customer and clean up their recycle streams. So -- and if they choose not to all have to recycle stream, then we'll build them for it. So it has been a positive on the price line. It's also been a positive on the quality of the material coming into the recycle plants.”
— James Fish, Q1 FY2026
“And that's really -- that's not so much robotics, but it's automation and forms of artificial intelligence that we've put in those plants.”
— John Morris, Q1 FY2026
“We've got all of our commercial and residential trucks outfitted with technology that allows us to capture over 300 million images a year. We could never do that manually. We couldn't put enough people anywhere to be able to do that, but we're using different forms of technology and AI to process about 95% of those images without a human having to touch them, and it's given us tremendous amounts of data that we can use, whether it's to evaluate safety, contamination, pricing opportunities, over serviced, under service customers, et cetera.”
— John Morris, Q1 FY2026
“And I do think that's a true contributor to the historically low rates we're seeing or the high retention rates we're seeing, if you will, and turnover rate has been as low as they've ever been.”
— John Morris, Q1 FY2026
“I think a lot of what we're able to do using artificial intelligence to capture data on how our folks are operating inside the cab has given us the information to go and coach folks.”
— John Morris, Q1 FY2026
“Going forward, I think we've got tremendous opportunity in terms of routing and logistical capabilities that our folks continue to work on. We're actively right now piloting remote heavy equipment and a number of spots. We see that as a potential pathway down the road to forms of autonomy at some of our landfills, et cetera, et cetera.”
— John Morris, Q1 FY2026
“In the Recycling segment, even though pricing for single-stream commodities declined 27%. Operating EBITDA grew by 18% as we realized automation benefits that lower labor costs and higher quality material and processed 9% more volume.”
— James Fish, Q1 FY2026
“These contributions partially offset -- were partially offset by 40 basis points of increased spending on technology initiatives”
— David Reed, Q1 FY2026
“And that's where technology enablement and AI are paying off already, and we've made a lot of progress there.”
— John Morris, Q4 FY2025
“When you think about the 15,000 refuse vehicles we run and now another -- call it, another 4,500 on the health care side, building out technology enablement as a logistics service is where you -- I think that's paying off too.”
— John Morris, Q4 FY2025
“We're taking kind of an IoT approach at our landfills, too, by embedding technology in those facilities that's going to give us visibility to the operation in a much more efficient manner than we traditionally have done.”
— John Morris, Q4 FY2025
“By using data and analytics, we're offering pricing that reflects the premium value of our service, our leading commitment to environmental sustainability and the strength of our asset network.”
— James Fish, Q4 FY2025
“This combination of operational excellence and strategic investment across our business has produced record margin performance and accelerated cash generation.”
— James Fish, Q4 FY2025
“The value of our recycling investments is clear, particularly when you consider our recycling segment delivered over 22% operating EBITDA growth despite nearly 20% lower commodity prices in 2025.”
— James Fish, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Trevor Romeo (William Blair)): And then I would love to get maybe your perspective or John's perspective on AI and new technologies. Obviously, WM has been living into automation for a long time at this point. But just in terms of AI, there's a lot of hype out there. So would love your views on whether there are any new tools you're looking at that could accelerate your efficiency going forward.
A: John Morris: Embedded tech across recycling (AI, robotics, automation), routing/logistics on ~19,000 trucks; healthcare still lacks most C&D tech benefits; still "early innings"; AI used for driver coaching among 20,000+ drivers; material benefits already in OpEx and C&D margins with runway to accelerate investment.
Q (Q1 FY2026, Connor Cerniglia (Bernstein)): Others in the industry have talked about some of the benefits they've seen from a pricing standpoint. I think there's been commentary that there's -- they expect a 100 basis point improvement in margins over the next few years. Have you all seen similar benefits mainly on pricing? And do you have a sense of maybe what that number could be? Or it sounds like it's about -- still too early to tell, but any color there would be helpful.
A: James Fish: AI-enabled truck cameras in use ~6–7 years; identify contamination accurately; enables customer outreach, chargebacks, and cleaner recycle streams—"a positive on the price line" and on inbound material quality; did not endorse a 100 bps margin figure.
Q (Q1 FY2026, Toni Kaplan (Morgan Stanley)): Which initiatives, whether it's robotics or automation or the cameras and the coaching that you talked about, just which of the technology benefits are you seeing the most benefit right now? And sort of when you look forward continuing to benefit from those?
A: John Morris: Recycling—structural OpEx reduction via automation/AI (less commodity sensitivity); Smart Truck—300M+ images/year, ~95% AI-processed without human touch for safety, contamination, pricing, service levels; cab AI data drives coaching tied to retention/safety; forward—routing/logistics, remote heavy-equipment pilots, potential landfill autonomy.
Q (Q4 FY2025, Toni Kaplan (Morgan Stanley)): When you think about 2026, which areas are you most focused on for efficiency or technology? Just anything that to highlight with level of automation that you're able to continue to do and which areas have the most runway for that?
A: John Morris: Recycling—"technology enablement and AI are paying off already"; fleet/logistics tech enablement scaling to healthcare vehicles; post-collection IoT at landfills for operational visibility and cost reduction.