← back to rankingVZ · Verizon Communications Inc.
Telecommunications Services · mkt cap $199.9B · calls: Q1 FY2026 vs Q4 FY2025
64.0 conviction · conf-adj 62
conf 4/10 partial
enthusiasm:30.0 · trend:8 · quantifies:5 · impact:0 · under_radar:5 · credibility:5 · business_impact:8 · disruption:0 · commitment:0 · confirmation:3
Enthusiasm latest 10 / prev 8 (rising)
Verizon's AI thesis moved from broad intent in Q4 FY2025 to a detailed operating plan in Q1 FY2026 spanning customer service, software delivery, network automation, energy optimization, microsegmentation, and AI infrastructure revenue. Credibility improved because management cited realized benefits and specific targets, though the largest topline claim remains forward-looking and not yet contracted.
GROUNDED NEXT-FY IMPACT vs CONSENSUS
Grounded on actual base — revenue $138.2B · net income $17.2B · net margin 12.4% · diluted EPS 4.06
These are next-fiscal-year annual uplift estimates, not next-quarter numbers.
Aggregate next-FY est. rev uplift: 0.0% · next-FY EPS uplift: 0.0% · vs analysts: inline · priced in: medium (model's call-read: high; verdict above is the hard-data one used for ranking) · confidence: 4/10
| Claim | Figure | Arithmetic | Next-FY Rev % | Next-FY EPS % |
|---|
Customer SAT +1,280 bps YoY
engagement · soft | 1,280 bps improvement YoY | 12.8 pp SAT improvement, but no churn, ARPU, subscriber, or revenue conversion base disclosed; cannot anchor to revenue/EPS. | | |
Software delivery +40%
productivity · soft | 40% plus delivery increase | Productivity uplift on dev throughput; no disclosed software-development spend or capacity-monetization base anywhere in claims -> % with no obtainable base. | | |
Vendor support costs -70%
cost · soft | over 70% reduction | After-tax saving would be vendor_support_cost*0.70*(1-0.21), but the vendor support cost base is not disclosed ('a ton of money' is not numeric); unanchored. | | |
85% of network issues auto-resolved
productivity · soft | 85% of issues autonomously resolved | Operationally quantified resolution rate, but no issue volume, cost-per-issue, truck-roll, or service-credit base disclosed to convert to savings. | | |
BOM complexity 1M combos -> 20 kits
cost · soft | over 1 million combinations down to about 20 kits | ~99.998% complexity reduction, but no procurement, inventory, labor, or capex cost base disclosed; no P&L impact anchored. | | |
Energy savings >$200M (already realized)
cost | over $200M | Disclosed floor: $200M*(1-0.21)=$158M after-tax; $158M/$17,174M NI = 0.92% standalone EPS uplift; revenue impact $0. But 'already realized' -> baked into FY2025 base, so next-FY incremental ~0. | 0.0 | 0.92 |
AI infrastructure revenue 'multibillions'
revenue · soft | multibillions (specifics in 3-6 mo) | Selling AI infra capacity INTO the buildout = supplier side, excluded from adopter headline. 'Multibillions' has no firm number and no realization date; per guardrail, do not invent. | | |
Assumptions: Tax rate 21% on cost savings. Incremental net margin (current ~12.43%) would apply to dollar-anchored adopter revenue, but no adopter-side revenue claim is dollar-anchored. Phasing: energy savings is 'already realized', so it sits in the FY2025 base (net income $17.174B already reflects it) -> next-FY incremental = 0, though its standalone magnitude is 0.92% of NI. Supplier 'multibillions' left null (unanchored, no date). Operational metrics (SAT, auto-resolution, BOM, dev delivery, vendor support) have no disclosed dollar base, so they stay soft.
Top line: No adopter-side topline can be sized: SAT, delivery, auto-resolution, and BOM claims lack disclosed revenue-conversion bases, and the only revenue claim ('multibillions' from AI infrastructure) is supplier-side and unquantified with no realization date. Adopter revenue uplift = 0.0%; supplier uplift null (not invented). The genuinely interesting topline lever is the supplier AI-infra opportunity, but management explicitly defers specifics 3-6 months.
Bottom line: Only one dollar-anchored bottom-line item: $200M energy savings -> $158M after-tax = 0.92% of net income — but it is 'already realized', i.e. baked into the FY2025 $17.174B base, so forward incremental ~0. The forward cost levers that WOULD be incremental (40%+ dev delivery, >70% vendor-support reduction) carry no disclosed dollar base and cannot be sized without inventing; they remain soft. Net hard-quantifiable forward EPS uplift ~0%.
Consensus already embeds meaningful growth: revenue $138.191B(FY25)->$142.832B(FY26) +3.36%, EPS $4.06(actual)->$4.95(FY26) +22%, ->$5.24(FY27). The only hard-quantifiable AI impact (energy 0.92% of NI, ~$0.037/sh) is already in the base and is dwarfed by — and subsumed within — that consensus trajectory, so there is no math pointing above consensus. The supplier 'multibillions' could change this, but it is unquantified and undated.
MODEL CONSENSUS (impact)
partial
Both agree all claims soft except energy (0.92% standalone); divergence only on phasing of energy and the inline-vs-behind verdict.
Conflicts reconciled
- est_eps_uplift_pct: X=0.0 vs Y=0.0092 -> used 0.0 because energy savings are 'already realized' and sit in the FY25 base, so next-FY incremental ~0 (sounder phasing, also more conservative)
- vs_analyst_expectations: X=inline vs Y=behind -> used inline because the hard AI impact is subsumed within consensus, not a shortfall; Y conflated 'small uplift' with 'miss'
- energy eps_uplift_pct units: X=0.92(pct) vs Y=0.0092(fraction) -> used 0.92 to match the _pct field convention
| Field | Opus 4.8 | GPT-5.5 |
|---|
| Rev uplift % | 0.0 | 0 |
| EPS uplift % | 0.0 | 0.009199953417957377 |
| Priced in | high | high |
| vs analysts | inline | behind |
| Confidence | 4 | 4 |
| Top line | No adopter-side topline can be sized: the only revenue claim ('multibillions' from AI infrastructure) is supplier-side and unquantified with no realization date, so adopter revenue uplift = 0.0% and supplier uplift is null (not invented). The genuinely interesting topline lever is the supplier AI-infra opportunity, but management explicitly defers specifics 3-6 months. | Hard adopter-side quantified topline impact is $0: SAT, delivery, autonomous resolution, and BOM complexity claims lack disclosed revenue conversion bases. The only revenue claim is supplier-side AI infrastructure, but 'multibillions' is unquantified and excluded from adopter headline uplift. |
| Bottom line | Only one dollar-anchored bottom-line item: $200M energy savings -> $158M after-tax = 0.92% of net income — but it is 'already realized', i.e. baked into the FY2025 $17.174B base, so forward incremental ~0. The forward cost levers that WOULD be incremental (40%+ dev delivery, >70% vendor-support reduction) carry no disclosed dollar base, so they cannot be sized without inventing a number; they remain soft. Net hard-quantifiable forward EPS uplift ~0%. | Only the energy-savings claim is financially anchored: $200M pre-tax becomes $158M after tax, equal to 0.920% of current net income. Other cost/productivity claims may matter but lack disclosed cost bases. |
| Reasoning | Consensus already embeds meaningful growth: revenue $138.2B(FY25)->$142.8B(FY26) +3.6%, and EPS $4.06(actual)->$4.95(FY26) +22%, ->$5.24(FY27). The only hard-quantifiable AI impact (energy $0.92% of NI) is already in the base and is dwarfed by — and subsumed within — that consensus trajectory. Every forward, incremental AI lever is unanchored, so there is no math pointing above consensus. The supplier 'multibillions' could change this, but it is unquantified and undated. | FY2026 consensus revenue of $142.832B implies +$4.641B, or +3.36%, versus current revenue of $138.191B; hard adopter AI revenue uplift calculated here is 0.00%. FY2026 consensus EPS of $4.95326 implies +22.00% versus current EPS of $4.06, while the hard adopter AI EPS uplift is only 0.920%, or about $0.037 per share. Consensus growth already more than covers the quantified hard AI benefit. |
Rows highlighted where the two models disagreed.
QUANTIFICATIONS
customer SAT scores: 1,280 basis point improvement (year-over-year, bottomline)
“But what we are seeing already is a 1,280 basis point improvement in customer SAT scores year-over-year.”
software development delivery: 40% plus (forward-looking; no explicit date, bottomline)
“We see opportunities to do an increase in our delivery by 40% plus, and we spend a ton of money on vendor support here, and we see our way to reducing those costs by over 70% as a result of what we're doing with AI.”
vendor support costs: over 70% (forward-looking; no explicit date, bottomline)
“We see opportunities to do an increase in our delivery by 40% plus, and we spend a ton of money on vendor support here, and we see our way to reducing those costs by over 70% as a result of what we're doing with AI.”
network issues autonomously resolved: 85% (right now, bottomline)
“85% of all of our issues right now are autonomously resolved.”
network bill-of-material complexity: over like 1 million different combinations down to about 20 kits (already realized, bottomline)
“We used to have in our network by the bill of material, it was over like 1 million different combinations. Think about the cost and the complexity around that using AI, we've driven that down now to about 20 kits.”
energy savings: over $200 million (already realized, bottomline)
“We already have over $200 million of energy savings as a result of deploying AI into the network and looking how we can optimize on energy, and we're doing things now at industrial scale.”
AI infrastructure revenue opportunity: multibillions in revenues (next 3 to 6 months for more specifics, topline)
“And that is the potential for multibillions in revenues, quite frankly. We'll have more specifics on that in the next 3 to 6 months.”
PAST (realized)
- Q1 FY2026, Daniel Schulman: But what we are seeing already is a 1,280 basis point improvement in customer SAT scores year-over-year.
- Q1 FY2026, Daniel Schulman: 85% of all of our issues right now are autonomously resolved.
- Q1 FY2026, Daniel Schulman: We already have over $200 million of energy savings as a result of deploying AI into the network and looking how we can optimize on energy, and we're doing things now at industrial scale.
- Q1 FY2026, Daniel Schulman: We used to have in our network by the bill of material, it was over like 1 million different combinations. Think about the cost and the complexity around that using AI, we've driven that down now to about 20 kits.
CURRENT (now)
- Q1 FY2026, Daniel Schulman: We have begun to embed AI and automation into our operations and customer interactions, which is already significantly improving customer experiences and lowering costs.
- Q1 FY2026, Daniel Schulman: We are working very closely with Google and Anthropic and other best-of-breed AI players to bring this to life.
- Q1 FY2026, Daniel Schulman: We are testing these models, and we are fine-tuning them.
- Q1 FY2026, Daniel Schulman: We're deploying quad code across our software development life cycle.
- Q1 FY2026, Daniel Schulman: And I would also point out on the commercial side, we are in quite deep discussions right now with hyperscalers with alternative cloud providers, large enterprises to integrate our fiber, both dark and lit and our 5G assets to support their AI infrastructure efforts.
- Q4 FY2025, Daniel Schulman: To do this successfully and efficiently, we are determined to be an AI-first company, deploying AI at scale.
FORWARD (guidance)
- Q1 FY2026, Daniel Schulman: I want us to be not an AI-first company. I want us to be an AI-native company.
- Q1 FY2026, Daniel Schulman: We are going to be substantially complete with that entire AI tech stack by July, and we hope to be fully done by November.
- Q1 FY2026, Daniel Schulman: We see opportunities to do an increase in our delivery by 40% plus, and we spend a ton of money on vendor support here, and we see our way to reducing those costs by over 70% as a result of what we're doing with AI.
- Q1 FY2026, Daniel Schulman: And that is the potential for multibillions in revenues, quite frankly.
- Q4 FY2025, Daniel Schulman: We will use AI to optimize our operations and fundamentally reshape the customer experience.
- Q4 FY2025, Daniel Schulman: Eventually, every individual customer will have a tailored proposition.
TRACK RECORD — PROMISE vs DELIVERY
50/100 track record too-early 6 calls reviewed
Verizon talks heavily about AI (AI Connect, AI-first transformation) but its disclosures are mostly progress figures rather than clean number-plus-date AI commitments; the AI Connect $2B funnel was shown growing then quietly de-quantified, while the $5B 2026 OpEx target it attaches to is tracking but not yet due. Track record is largely unproven/too-early with one soft walk-back.
AI Connect launched as a new AI-infrastructure revenue stream said to already have revenue and EBITDA impact in Q4 FY2024 — promised Q4 FY2024
partial By Q2 FY2025 the AI Connect sales funnel grew to ~$2B, showing traction, but no hard AI-revenue target was set or reported as delivered
AI Connect sales funnel of ~$2 billion (nearly doubled since launch), positioned as imminent enterprise/hyperscaler AI revenue — promised Q2 FY2025
quietly-dropped Later calls stopped quantifying the funnel; under new CEO it folded into vague 'enable AI at scale for hyperscalers' language with no booked-revenue update
Become an 'AI-first company deploying AI/automation at scale' to help drive a $5B in-year OpEx savings war chest in 2026 — promised Q4 FY2025
too-early Q1 FY2026 said they are 'well on our way' to the $5B target with cost of acquisition/retention down ~35%, but the year is not complete and AI is only one of several levers
Use AI/predictive models to reduce churn and lower costs as part of the 10-workstream transformation — promised Q4 FY2025
too-early Q1 FY2026 churn fell to <85bps with record customer-satisfaction, partly credited to AI, but no specific quantified AI churn/cost target existed to judge against
PRICED-IN (REFINED)
MEDIUMEst. revisions rising · Fwd P/E 10.2 · EV/Sales 2.8x
AI claim maps to Verizon Business Group, Verizon Consumer Group
Analyst ratings show a modest upward migration from January to June, with buys increasing and holds declining, and the last-quarter price target average is above the last-year average, so revisions are rising rather than falling. Forward revenue and EPS growth are positive but modest, while valuation at 10.2x forward P/E and 2.8x EV/Sales is not especially stretched for a mature telecom. AI benefits would most plausibly show up through Verizon Business Group and Verizon Consumer Group via network efficiency, automation, customer operations, and enterprise connectivity. Rising estimates make the AI thesis more priced-in, but the still-reasonable valuation keeps the verdict at medium rather than high.
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
4Q4 FY20246Q1 FY20258Q2 FY20252Q3 FY20258Q4 FY20254Q1 FY2026
AI enthusiasm across 6 calls — trend ↗ rising
AI moved from infrastructure opportunity to specific AI Connect demand, then broadened into an AI-first automation and customer-experience transformation.
RECENT AI CATALYSTS & NEWS
BUSINESS IMPACT - QUALITATIVE MATERIALITY
7/10 qualitative impact material medium-term · mixed evidence
Where AI matters: network operations, customer service, software delivery, cost efficiency
Verizon has credible adopter-side AI deployment at industrial scale, including autonomous network issue resolution, energy optimization, customer-service automation, BOM simplification, and software/vendor-cost productivity. The upside is material for a telecom cost base and customer experience, but not transformational because most benefits are efficiency-led, forward savings lack dollar bases, and the largest revenue claim is AI-infrastructure supplier demand rather than own-business AI adoption.
Caveats: Forward vendor-support and software-productivity claims lack disclosed cost bases; AI-infrastructure revenue opportunity is supplier-side and not evidence of adopter upside; Execution risk in integrating AI across legacy systems and customer workflows; Customer-service automation could hurt satisfaction if deployed poorly
AI DISRUPTION / CANNIBALIZATION RISK tailwind · 2/10
AI does not directly automate away Verizon's core product: connectivity, spectrum-backed mobile service, fiber, enterprise networking, and managed telecom infrastructure remain physical-network businesses with high capital and regulatory barriers. AI may pressure some customer-support labor and commoditize generic service interactions, but that is a cost opportunity more than a revenue-model threat.
OPTIONS / MARKET STRUCTURE
option liquidity: good
proxy inputs — dollar-ADV $1.2B · beta 0.224 · px $47.87
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 5/10 measured.
INSIDERS selling 2 open-market sell(s) vs 0 buy(s) — net distribution
INSTITUTIONS (13F) adding as of 2026-03-31: 339 new / 196 closed positions; 1658 increased / 1333 reduced; institutional ownership -0.87pp; +151 net 13F holders
MGMT LANGUAGE 5/10 measured AI is real but lightly detailed: firm early deployment claims, no AI-specific targets, savings, timelines, or quantified business impact.
commit “We have begun to embed AI and automation into our operations and customer interactions”
commit “already significantly improving customer experiences and lowering costs”
hedge “These work streams span everything from becoming an AI-first company”
VERBATIM AI QUOTES
“These work streams span everything from becoming an AI-first company to reducing friction in every step of the customer journey to reexamining outdated internal policies and procedures that slow us down and add to bureaucracy.”
— Daniel Schulman, Q1 FY2026
“We have begun to embed AI and automation into our operations and customer interactions, which is already significantly improving customer experiences and lowering costs.”
— Daniel Schulman, Q1 FY2026
“These improvements come from the work our teams are doing in our transformation streams, smarter channel mix, less friction, better tools and modeling the beginning of AI and naval processes and a tighter focus on fiscally responsible offers that drive profitable growth.”
— Daniel Schulman, Q1 FY2026
“At the same time, we have launched and are executing against the [indiscernible] transformation program that is making Verizon an AI-first simpler, more efficient and more customer-centric company.”
— Daniel Schulman, Q1 FY2026
“I want us to be not an AI-first company. I want us to be an AI-native company.”
— Daniel Schulman, Q1 FY2026
“One is around operational efficiency, taking out costs, improving productivity, delivering more value to customers.”
— Daniel Schulman, Q1 FY2026
“The second, really important, I'll give some examples of this is customer satisfaction improvements. How can we better serve our customers through the use of AI?”
— Daniel Schulman, Q1 FY2026
“And then finally, how do we fully ingest AI capabilities into our value proposition? How do we take that so that we micro segment down to every single customer?”
— Daniel Schulman, Q1 FY2026
“We are working very closely with Google and Anthropic and other best-of-breed AI players to bring this to life.”
— Daniel Schulman, Q1 FY2026
“But what we are seeing already is a 1,280 basis point improvement in customer SAT scores year-over-year.”
— Daniel Schulman, Q1 FY2026
“We see opportunities to do an increase in our delivery by 40% plus, and we spend a ton of money on vendor support here, and we see our way to reducing those costs by over 70% as a result of what we're doing with AI.”
— Daniel Schulman, Q1 FY2026
“85% of all of our issues right now are autonomously resolved.”
— Daniel Schulman, Q1 FY2026
“We already have over $200 million of energy savings as a result of deploying AI into the network and looking how we can optimize on energy, and we're doing things now at industrial scale.”
— Daniel Schulman, Q1 FY2026
“And I would also point out on the commercial side, we are in quite deep discussions right now with hyperscalers with alternative cloud providers, large enterprises to integrate our fiber, both dark and lit and our 5G assets to support their AI infrastructure efforts.”
— Daniel Schulman, Q1 FY2026
“And that is the potential for multibillions in revenues, quite frankly.”
— Daniel Schulman, Q1 FY2026
“To do this successfully and efficiently, we are determined to be an AI-first company, deploying AI at scale.”
— Daniel Schulman, Q4 FY2025
“We will use AI to optimize our operations and fundamentally reshape the customer experience.”
— Daniel Schulman, Q4 FY2025
“We are leveraging it to simplify offers, personalized interactions and reduce churn through smart, consistent marketing.”
— Daniel Schulman, Q4 FY2025
“By using predictive models, we can anticipate customer pain points before they happen, allowing us to solve problems proactively.”
— Daniel Schulman, Q4 FY2025
“We will use our data and AI capabilities to not just massively improve our efficiency and customer satisfaction, but to redefine our value propositions and deliver hyper-personalized experiences.”
— Daniel Schulman, Q4 FY2025
“Beyond these internal efforts, we are unlocking new revenue streams by reimagining our existing assets, leveraging our deep fiber footprint and distributed network facilities to enable AI at scale for our enterprise customers, including hyperscalers.”
— Daniel Schulman, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Sean Diffley): Dan, you spoke recently about AI transforming the economy and impacting jobs. I was hoping you could elaborate a bit on how you think about the ability to take out more costs across the business. Obviously, you referenced it a bit on the OpEx commentary. But any tangible examples of AI use cases that are being implemented at Verizon? How you think about total head count growth over time?
A: I want us to be not an AI-first company. I want us to be an AI-native company. And I think there are 3 areas Were you going to see us utilize AI to its fullest. One is around operational efficiency, taking out costs, improving productivity, delivering more value to customers. The second, really important, I'll give some examples of this is customer satisfaction improvements. How can we better serve our customers through the use of AI? And then finally, how do we fully ingest AI capabilities into our value proposition?