← back to rankingCTSH · Cognizant Technology Solutions Corporation
Information Technology Services · mkt cap $26.1B · calls: Q1 FY2026 vs Q4 FY2025
62.0 conviction · conf-adj 62
conf 6/10 partial
enthusiasm:30.0 · trend:8 · quantifies:5 · impact:0 · under_radar:14 · credibility:5 · business_impact:8 · disruption:-14 · commitment:6 · confirmation:0
Enthusiasm latest 10 / prev 9 (rising)
Cognizant’s AI thesis is that it can turn enterprise AI from infrastructure spend into production outcomes through AI-assisted delivery, agentic platforms, outcome-based pricing, and acquisitions that fill out its AI builder stack. Credibility improved in the latest call because management moved from broad AI builder framing to more operating proof points: over 5,000 engagements, nearly 40% AI-assisted code, tokenized rate cards, client productivity metrics, and Project LEAP savings. The risk is that much of the claimed upside still depends on future enterprise adoption and successful conversion of productivity sharing into durable revenue and margin.
GROUNDED NEXT-FY IMPACT vs CONSENSUS
Grounded on actual base — revenue $21.1B · net income $2.2B · net margin 10.6% · diluted EPS 4.55
These are next-fiscal-year annual uplift estimates, not next-quarter numbers.
Aggregate next-FY est. rev uplift: 0.76% · next-FY EPS uplift: 0.675% · vs analysts: inline · priced in: low (model's call-read: high; verdict above is the hard-data one used for ranking) · confidence: 6/10
| Claim | Figure | Arithmetic | Next-FY Rev % | Next-FY EPS % |
|---|
Over 4,000 AI engagements (Q4'25)
engagement · soft | over 4,000 | Count only; no average revenue/value per engagement disclosed -> cannot map to a revenue line. | | |
>30% developer effort AI-assisted (Q4'25)
productivity · soft | over 30% | Delivery-productivity %; no labor-cost base disclosed and productivity is passed to clients via fixed-bid pricing, not retained. | | |
Well over 5,000 AI engagements (Q1'26)
engagement · soft | >5,000 | Count only, no $/engagement; ~25% QoQ count growth is a leading indicator, not a sizeable revenue figure. | | |
Engagements up from ~4,000 exiting Dec
engagement · soft | ~4,000 -> 5,000+ | (5,000-4,000)/4,000 = 25% count growth; no dollar anchor per engagement. | | |
Nearly 40% of code AI-assisted (Q1'26)
productivity · soft | nearly 40% | Internal delivery-efficiency %, no labor-cost base; benefit largely passed through in fixed-bid pricing. | | |
54M provider contract updates automated/yr
productivity · soft | over 54 million | Client-outcome volume; reduces client revenue leakage, no Cognizant fee/$ disclosed. | | |
Oracle ERP 25% faster TTM / 40% faster deploy
productivity · soft | 25% / 40% | Single client outcome; no contract value or replicable revenue attached. | | |
+2.5% trailing-12m revenue/employee
productivity · soft | 2.5% | Per-employee TRAILING ratio; a 2.5% rev/head gain is not 2.5% more total revenue (headcount base not given) -> not a separable forward $ uplift. | | |
+5% trailing-12m adj op margin/employee
productivity · soft | 5% | Per-employee TRAILING diagnostic; no per-head base to convert to total $ margin gain, and already-realized not forward incremental. | | |
Project LEAP savings 2026
cost | $200M-$300M | Midpoint $250M; after-tax @21% = $197.5M; /adj NI $2,504M = 7.89% GROSS uplift (range 6.31%-9.46%). Offset by reinvestment row below; net in-year ~0. | | 7.89 |
LEAP reinvestment split 2/3 growth / 1/3 upskill
cost | ~2/3 / ~1/3 (~100%) | ~100% of savings redeployed -> offset = -$250M pretax, -$197.5M after tax, -7.89% of adj NI, neutralizing the gross LEAP savings in-year. Future revenue from reinvested 2/3 unquantified. | | -7.89 |
Project LEAP one-time costs
cost | $230M-$320M | Midpoint $275M; after-tax ~$217M = -8.68% of adj NI (range -7.26% to -10.09%). One-time restructuring -> EXCLUDED from adjusted EPS aggregate; real GAAP drag only. | | -8.68 |
$4.5T US labor value (research)
other · soft | $4.5 trillion | External TAM, not Cognizant capture; no disclosed capture rate -> unanchored to revenue. | | |
340,000 associates AI-skilled
other · soft | over 340,000 | Capability metric; no productivity uplift per associate, cost base, or revenue conversion disclosed. | | |
Digital engineering +8% YoY (AI platforms)
revenue · soft | 8% | Practice-level growth; segment revenue base and AI-attributable fraction not disclosed -> cannot isolate AI's incremental contribution. | | |
BPO +9% YoY (digital labor)
revenue · soft | 9% | Segment growth, base undisclosed; AI-attributable portion not separable. | | |
Incremental billion-dollar AI-platform partnership
revenue | $1B (TCV/bookings) | BOOKINGS not revenue; 5-yr ratable run-rate $200M/yr. X assumed 60% year-1 ramp ($120M); Y assumed full $200M. AVERAGED ~$160M FY26 rev = 0.76% of $21,108M; at 10.565% net margin = $16.9M / adj NI $2,504M = 0.68%. | 0.76 | 0.68 |
Customer interaction cycle -> 90 seconds
productivity · soft | 90 seconds | Client cycle-time anecdote, no $. | | |
Enrollment cycle 7 days -> minutes
productivity · soft | 7 days -> minutes | Client anecdote, no $. | | |
96% nurse-note adjudication autonomous
productivity · soft | 96% | Client outcome, no Cognizant fee/$ disclosed. | | |
Human review 8 hours -> 20 minutes
productivity · soft | 8h -> 20min | Client outcome anecdote, no $. | | |
Assumptions: Incremental net margin = current net margin 10.56% (pure IT services; no higher-margin software justification). Tax rate 21%. EPS sized vs adjusted/non-GAAP NI base $2,504M (consensus basis), since GAAP EPS $4.55 << consensus $5.19. $1B partnership treated as BOOKINGS, 5-yr ratable = $200M/yr, ~60% ramp in first full year (2026) = ~$120M recognized. Project LEAP: gross after-tax saving ~$197.5M (7.9% of adj NI) netted to ~0 for 2026 because mgmt explicitly reinvests ~2/3 (growth) + ~1/3 (upskilling) = ~100% redeployed, with full benefit deferred to 2027; LEAP one-time costs ($230-320M) treated as adjusted-EPS-excluded (real GAAP drag ~ -8.7% of GAAP NI). All claims adopter-side; supplier-side = none.
Top line: Quantified, anchored AI-attributable topline is small: the only sizeable disclosed dollar figure is the $1B incremental AI-platform partnership, which is bookings/TCV — at a 5-yr ratable ~$200M/yr run-rate with a ~60% first-year ramp, ~$120M lands in 2026 = ~0.57% of $21,108M revenue. Everything else (4,000->5,000+ engagements, +8% digital engineering, +9% BPO, +2.5% revenue/employee) is either an unanchored count, a segment growth rate without an AI-attributable base, or a per-employee ratio — directionally supportive but not separately sizeable. Total quantified adopter topline uplift ~0.6%, roughly one-tenth of the +5.8% revenue growth consensus already models.
Bottom line: Bottom-line AI impact is structurally muted in 2026. Project LEAP's $200-300M savings (gross after-tax ~$197.5M = ~7.9% of adjusted NI) is explicitly ~100% reinvested (2/3 growth + 1/3 upskilling), so the in-year margin benefit is ~$0 and the full benefit is deferred to 2027. The $230-320M one-time LEAP costs are a real ~ -8.7% GAAP NI drag in 2026 (excluded from the adjusted base consensus tracks). The 30-40% AI-assisted code and >50% fixed-bid mix mean delivery productivity is largely passed to clients via pricing rather than retained as margin. Net: only the partnership flow-through (~$12.7M, ~0.51% of adjusted NI) is a defensible incremental 2026 EPS contribution.
Consensus already models 2026 revenue of $22,325M vs FY25 $21,108M = +$1,217M (+5.77%) and adjusted EPS $5.711 vs $5.191 = +10.0%. The quantified, AI-attributable incremental topline (~$120M, +0.57%) is only ~10% of that modeled growth, and the quantified incremental adjusted-EPS uplift (~0.51%) is ~1/20th of the +10% EPS growth already expected. Management's own framing reinforces this: the hard cost savings are reinvested (neutral to 2026 margin), and productivity gains are passed through in fixed-bid pricing. There is no math pointing clearly above the consensus trajectory, so the AI narrative is embedded/priced-in rather than a positive surprise.
MODEL CONSENSUS (impact)
partial
Agree on all soft/unanchored claims. Kept per-employee ratios null (conservative). Split LEAP into gross saving + reinvestment offset (net ~0); averaged the partnership ramp.
Conflicts reconciled
- rev/employee: X=null/soft vs Y=2.5/2.23 -> used X (per-employee trailing ratio is not total-revenue %, Y's scaling flawed)
- opmargin/employee: X=null/soft vs Y=5/5.57 -> used X (per-head diagnostic, not additive forward uplift)
- LEAP savings eps: X=0(net) vs Y=7.89(gross) -> used Y gross + explicit -7.89 reinvestment offset, nets to ~0
- LEAP reinvestment eps: X=null/soft vs Y=-7.89 -> used Y (it is the offset neutralizing the savings)
- LEAP cost eps: X=0 vs Y=-8.68 -> used -8.68 GAAP drag, excluded from adjusted aggregate
- partnership: X=0.57/0.51 vs Y=0.95/0.84 -> averaged 0.76/0.68 (both ramp assumptions defensible)
| Field | Opus 4.8 | GPT-5.5 |
|---|
| Rev uplift % | 0.57 | 0.95 |
| EPS uplift % | 0.51 | 0.84 |
| Priced in | high | high |
| vs analysts | inline | inline |
| Confidence | 6 | 5 |
| Top line | Quantified, anchored AI-attributable topline is small: the only sizeable disclosed dollar figure is the $1B incremental AI-platform partnership, which is bookings/TCV — at a 5-yr ratable ~$200M/yr run-rate with a ~60% first-year ramp, ~$120M lands in 2026 = ~0.57% of $21,108M revenue. Everything else (4,000->5,000+ engagements, +8% digital engineering, +9% BPO, +2.5% revenue/employee) is either an unanchored count, a segment growth rate without an AI-attributable base, or a per-employee ratio — directionally supportive but not separately sizeable. Total quantified adopter topline uplift ~0.6%, roughly one-tenth of the +5.8% revenue growth consensus already models. | The only hard additive next-FY revenue item is the incremental $1B AI-platform partnership. Treating it as bookings and recognizing $200M ratably in FY2026 gives $200M / $21.108B = 0.95% revenue uplift. The 5% revenue-per-employee claim is a $1.055B revenue-equivalent productivity metric, but it is trailing/current-base and not additive. |
| Bottom line | Bottom-line AI impact is structurally muted in 2026. Project LEAP's $200-300M savings (gross after-tax ~$197.5M = ~7.9% of adjusted NI) is explicitly ~100% reinvested (2/3 growth + 1/3 upskilling), so the in-year margin benefit is ~$0 and the full benefit is deferred to 2027. The $230-320M one-time LEAP costs are a real ~ -8.7% GAAP NI drag in 2026 (excluded from the adjusted base consensus tracks). The 30-40% AI-assisted code and >50% fixed-bid mix mean delivery productivity is largely passed to clients via pricing rather than retained as margin. Net: only the partnership flow-through (~$12.7M, ~0.51% of adjusted NI) is a defensible incremental 2026 EPS contribution. | At the current 10.565% net margin, the $200M FY2026 partnership revenue contributes $21.1M of net income, or 0.84% of the $2.504B adjusted earnings base. Project LEAP has a gross midpoint savings benefit of 7.89% EPS, but management also says roughly 100% of those savings are reinvested, producing no net adjusted EPS uplift from LEAP in 2026; the $275M midpoint LEAP cost is a -8.68% GAAP drag but likely excluded from adjusted consensus. |
| Reasoning | Consensus already models 2026 revenue of $22,325M vs FY25 $21,108M = +$1,217M (+5.77%) and adjusted EPS $5.711 vs $5.191 = +10.0%. The quantified, AI-attributable incremental topline (~$120M, +0.57%) is only ~10% of that modeled growth, and the quantified incremental adjusted-EPS uplift (~0.51%) is ~1/20th of the +10% EPS growth already expected. Management's own framing reinforces this: the hard cost savings are reinvested (neutral to 2026 margin), and productivity gains are passed through in fixed-bid pricing. There is no math pointing clearly above the consensus trajectory, so the AI narrative is embedded/priced-in rather than a positive surprise. | Consensus FY2026 revenue of $22.325B implies growth of $1.217B versus FY2025 actual revenue, or 5.77%, which is well above the hard additive AI revenue uplift of 0.95%. Consensus FY2026 EPS of $5.711 is 10.02% above 2025 consensus adjusted EPS of $5.191; the hard additive AI EPS uplift is only 0.84%, and even gross LEAP savings plus the partnership would be about 8.73% before the disclosed reinvestment offset, still within the consensus EPS trajectory. |
Rows highlighted where the two models disagreed.
QUANTIFICATIONS
AI engagements: over 4,000 (Q4 FY2025, topline)
“We now have over 4,000 AI engagements across all three vectors, and over 30% of our developer effort in software development cycles is AI-assisted and agent tech.”
AI-assisted developer effort: over 30% (Q4 FY2025, bottomline)
“We now have over 4,000 AI engagements across all three vectors, and over 30% of our developer effort in software development cycles is AI-assisted and agent tech.”
AI engagements: well over 5,000 (Q1 FY2026, topline)
“To date, we have well over 5,000 AI engagements across 3 vectors, up from approximately 4,000 exiting December.”
AI engagement increase: up from approximately 4,000 (Q1 FY2026 versus exiting December, topline)
“To date, we have well over 5,000 AI engagements across 3 vectors, up from approximately 4,000 exiting December.”
AI-assisted code: nearly 40% (Q1 FY2026, bottomline)
“This approach has enabled nearly 40% of our code to be AI assisted.”
Provider contract updates automated: over 54 million (annually, bottomline)
“Our AI solution automates the validation of over 54 million provider contract updates annually, directly reducing revenue leakage and solving a problem that was previously intractable at scale.”
Oracle cloud ERP transformation speed: 25% faster time to market and 40% faster deployment (Q1 FY2026 client example, bottomline)
“For a leading European telecom operator, Cognizant delivered an AI-powered Oracle cloud ERP transformation, unifying finance, procurement and supply chain on a single cloud-native platform, achieving 25% faster time to market and 40% faster deployment through agentic AI and automation.”
Trailing revenue per employee: 2.5% increase (trailing 12-month, topline)
“Consistent with the shift, we delivered 2.5% and 5% increases in trailing 12-month revenue and adjusted operating margin per employee, respectively.”
Trailing adjusted operating margin per employee: 5% increase (trailing 12-month, bottomline)
“Consistent with the shift, we delivered 2.5% and 5% increases in trailing 12-month revenue and adjusted operating margin per employee, respectively.”
Project LEAP savings: $200 million to $300 million (2026, bottomline)
“The program is expected to deliver savings in 2026 of approximately $200 million to $300 million with a full year benefit in 2027.”
Project LEAP reinvestment split: approximately 2/3 reinvested; roughly 1/3 toward upscaling (2026, both)
“We anticipate approximately 2/3 of the savings generated by Project LEAP will be directly reinvested to support future growth across integrated offerings, AI capabilities and partnership and roughly 1/3 toward upscaling our workforce, all while maintaining an active and strategic M&A posture.”
Project LEAP costs: $230 million to $320 million (substantially all incurred in 2026, bottomline)
“As part of this program, we expect to record costs of $230 million to $320 million, which substantially all incurred in 2026.”
AI labor value opportunity: $4.5 trillion (future, both)
“In fact, our latest new work new world research released last month reveals that AI is capable of unlocking $4.5 trillion in US labor value in the future.”
AI skilling: over 340,000 associates (over the last two and a half years, bottomline)
“Over the last two and a half years, over 340,000 of our associates have completed AI skilling.”
Digital engineering growth tied to AI platforms: 8% year over year (Q4 FY2025 and FY2025, topline)
“For example, our proprietary platforms like Flowsource and neuroengineering are helping clients unlock technology debt, helping to fuel 8% year over year in both the fourth quarter and year in our digital engineering practices.”
BPO growth tied to digital labor: 9% year over year (Q4 FY2025 and FY2025, topline)
“Similarly, as our clients rethink their operations through an agentic lens, demand for our BPO business powered by deep immersion of digital labor grew 9% year over year in the quarter and the year.”
Financial services AI platform partnership: incremental billion-dollar partnership (Q4 FY2025, topline)
“First, with the financial services client, we signed an incremental billion-dollar partnership where we are leveraging our AI platforms, including our NeuroSuite and FlowSource, to help accelerate speed to market, drive product innovation, and deliver enhanced productivity.”
Customer interaction cycle time: ninety seconds (Q4 FY2025 client example, bottomline)
“Now, by deploying orchestrated agents, we have collapsed that cycle to ninety seconds.”
Enrollment cycle time: from as many as seven days to minutes (Q4 FY2025 client example, bottomline)
“For a major US regional player, our AI intake platform reduced enrollment cycle times from as many as seven days to minutes.”
Autonomous nurse note adjudication: 96% (Q4 FY2025 client example, bottomline)
“On their claim side, our clinical engine now adjudicates 96% of nurse note reviews autonomously, cutting human review times from eight hours to twenty minutes.”
Human review time reduction: from eight hours to twenty minutes (Q4 FY2025 client example, bottomline)
“On their claim side, our clinical engine now adjudicates 96% of nurse note reviews autonomously, cutting human review times from eight hours to twenty minutes.”
Fixed bid and transaction-based work: more than 50% of revenue (Q4 FY2025, both)
“Our productivity improved as fixed bid and transaction-based work now represent more than 50% of our revenue.”
Trailing revenue per employee: 5% increase (trailing twelve-month, topline)
“We also saw a 5% and an 8% increase in trailing twelve-month revenues and adjusted operating income per employee, respectively.”
Trailing adjusted operating income per employee: 8% increase (trailing twelve-month, bottomline)
“We also saw a 5% and an 8% increase in trailing twelve-month revenues and adjusted operating income per employee, respectively.”
PAST (realized)
- We now have over 4,000 AI engagements across all three vectors, and over 30% of our developer effort in software development cycles is AI-assisted and agent tech.
- To date, we have well over 5,000 AI engagements across 3 vectors, up from approximately 4,000 exiting December.
- This approach has enabled nearly 40% of our code to be AI assisted.
- Our AI solution automates the validation of over 54 million provider contract updates annually, directly reducing revenue leakage and solving a problem that was previously intractable at scale.
- For a leading European telecom operator, Cognizant delivered an AI-powered Oracle cloud ERP transformation, unifying finance, procurement and supply chain on a single cloud-native platform, achieving 25% faster time to market and 40% faster deployment through agentic AI and automation.
CURRENT (now)
- We have seen increasing demand for our AI and analytics services. driven by AI readiness and innovation budgets.
- Our pipeline remains healthy and broad-based. We continue to see strong demand for cost takeout, vendor consolidation and AI-led services.
- AI adoption is driving demand for engineering, modernization and platform services.
- We are beginning to see the emergence of AI infused rate cards where pricing reflects a blended model of human effort and digital effort with several clients, we are exposing tokenized rate cards that prices work along a continuum from fully human-led discovery to hybrid to increasingly autonomous agenetic delivery.
- Token metering is a reality, both at a project level and at an individual level.
FORWARD (guidance)
- Our conviction in the long-term opportunity emerging with enterprise AI adoption has never been stronger.
- For 2026, we still expect to return approximately $1.6 billion of capital to shareholders, including $1 billion towards share repurchases and the remainder towards our regular dividend.
- The program is expected to deliver savings in 2026 of approximately $200 million to $300 million with a full year benefit in 2027.
- We anticipate approximately 2/3 of the savings generated by Project LEAP will be directly reinvested to support future growth across integrated offerings, AI capabilities and partnership and roughly 1/3 toward upscaling our workforce, all while maintaining an active and strategic M&A posture.
- I see this as more of a bigger opportunity for us and a higher surface area for us to actually operate.
TRACK RECORD — PROMISE vs DELIVERY
58/100 track record mixed 6 calls reviewed
Cognizant did make some quantified, AI-enabled commitments — notably 2025 margin expansion (beaten) and a rising AI-code share (on track) — but most AI disclosures are reported progress metrics, and softer training/BPO targets are partial, too-early, or quietly dropped. Track record is credible but thin on hard, dated AI revenue promises.
Scale AI-generated code from ~20% of accepted code (Q4'24 baseline) — promised Q4 FY2024
delivered Rose to ~30% (Q2/Q3'25) and ~40% AI-assisted by Q1'26 — delivered on the rising trajectory
20-40 bps adjusted operating margin expansion in 2025, underpinned by AI-enabled delivery — promised Q1 FY2025
delivered Delivered ~50 bps of full-year expansion, beating the range, with revenue-per-employee up 5-8%
Synapse program to train 1 million workers in future-work/AI skills — promised Q4 FY2024
partial Reported 400,000+ trained and ~340,000 associates AI-skilled; 1M milestone not yet confirmed
BPO business 'on track to reach $3 billion in annualized revenue over the next several quarters' — promised Q3 FY2025
too-early BPO grew 9-10% through Q4'25 but the $3B annualized run-rate was not explicitly confirmed
AI-generated code 'could reach 50% in the years ahead' — promised Q3 FY2025
too-early At ~40% by Q1'26 and climbing; the open-ended timeframe has not arrived
New 14-acre Chennai learning center to train 100,000 individuals annually in advanced AI — promised Q1 FY2025
quietly-dropped Never reconfirmed with a throughput number; faded from the narrative
PRICED-IN (REFINED)
LOW (room left)Est. revisions falling · Fwd P/E 11.8 · EV/Sales 1.2x
AI claim maps to Healthcare Segment, Financial Services, Communication, Media and Technology
Analyst rating mix is not migrating upward, with strong buys down and holds slightly higher versus early 2026, while price targets have fallen from lastYearAvg 81.48 to lastQuarterAvg 69 to lastMonthAvg 67. Forward revenue and EPS estimates show modest growth, but not enough to offset the negative target revision signal. Valuation is not stretched at 11.8x forward P/E and about 1.2x EV/Sales, so flat-to-falling revisions plus a reasonable multiple suggest AI upside is not already heavily reflected. The most plausible AI benefit would flow through Cognizant's vertical service segments such as Healthcare Segment, Financial Services, and Communication, Media and Technology.
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
8Q4 FY20249Q1 FY202510Q2 FY202510Q3 FY202510Q4 FY202510Q1 FY2026
AI enthusiasm across 6 calls — trend ↗ rising
AI evolved from strong platform/client traction into a central AI-builder strategy with quantified engagements, productivity, patents, deals and outcome-based economics.
RECENT AI CATALYSTS & NEWS
BUSINESS IMPACT - QUALITATIVE MATERIALITY
7/10 qualitative impact material medium-term · mixed evidence
Where AI matters: AI-led IT services delivery, software engineering, BPO automation, outcome pricing
Cognizant has real deployment evidence: over 5,000 AI engagements, nearly 40% AI-assisted code, AI-infused/tokenized rate cards, and client examples with faster deployment and automated workflows. But hard dollar conversion is still modest, with quantifiable near-term revenue/EPS uplift around ~1% and most upside dependent on converting productivity into durable pricing and share gains.
Caveats: AI engagement counts may not translate into meaningful revenue or margin dollars; Productivity gains could be competed away through lower pricing; Clients may use AI to reduce vendor dependence and internalize more work; Outcome/token pricing execution risk
AI DISRUPTION / CANNIBALIZATION RISK headwind · 7/10
AI directly attacks the labor-based IT services/BPO model by automating coding, testing, support, operations, and back-office work that Cognizant historically monetizes through billable people and offshore labor arbitrage. Tokenized and outcome-based pricing may preserve some economics, but the structural risk is billable-hour and pricing deflation across the core service stack.
OPTIONS / MARKET STRUCTURE
option liquidity: good
proxy inputs — dollar-ADV $383M · beta 0.803 · px $55.14
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 flat, management language 8/10 committed.
INSIDERS selling 3 open-market sell(s) vs 0 buy(s) — net distribution
INSTITUTIONS (13F) flat as of 2026-03-31: 122 new / 196 closed positions; 475 increased / 381 reduced; institutional ownership -0.61pp; -91 net 13F holders
MGMT LANGUAGE 8/10 committed Highly committed stance with concrete AI scale metrics, client outcomes, and platform ownership, though some strategic vision language remains qualified.
commit “To date, we have well over 5,000 AI engagements across 3 vectors, up from approximately 4,000 exiting December.”
commit “This approach has enabled nearly 40% of our code to be AI assisted.”
commit “Our AI solution automates the validation of over 54 million provider contract updates annually”
VERBATIM AI QUOTES
“I believe our work to become the world's permanent AI builder is resonating, demonstrated by our first quarter performance.”
— Ravi Kumar S, Q1 FY2026
“To date, we have well over 5,000 AI engagements across 3 vectors, up from approximately 4,000 exiting December.”
— Ravi Kumar S, Q1 FY2026
“This approach has enabled nearly 40% of our code to be AI assisted.”
— Ravi Kumar S, Q1 FY2026
“Our AI solution automates the validation of over 54 million provider contract updates annually, directly reducing revenue leakage and solving a problem that was previously intractable at scale.”
— Ravi Kumar S, Q1 FY2026
“For a leading European telecom operator, Cognizant delivered an AI-powered Oracle cloud ERP transformation, unifying finance, procurement and supply chain on a single cloud-native platform, achieving 25% faster time to market and 40% faster deployment through agentic AI and automation.”
— Ravi Kumar S, Q1 FY2026
“We have seen increasing demand for our AI and analytics services. driven by AI readiness and innovation budgets.”
— Jatin Dalal, Q1 FY2026
“We are beginning to see the emergence of AI infused rate cards where pricing reflects a blended model of human effort and digital effort with several clients, we are exposing tokenized rate cards that prices work along a continuum from fully human-led discovery to hybrid to increasingly autonomous agenetic delivery.”
— Ravi Kumar S, Q1 FY2026
“We now have over 4,000 AI engagements across all three vectors, and over 30% of our developer effort in software development cycles is AI-assisted and agent tech.”
— Ravi Kumar, Q4 FY2025
“Cognizant's mission is to be the AI builder bridging the gap to enterprise value by converting the technology to measurable returns on investments for our clients.”
— Ravi Kumar, Q4 FY2025
“We are seeing this AI builder strategy translate into demand across our core practices.”
— Ravi Kumar, Q4 FY2025
“First, with the financial services client, we signed an incremental billion-dollar partnership where we are leveraging our AI platforms, including our NeuroSuite and FlowSource, to help accelerate speed to market, drive product innovation, and deliver enhanced productivity.”
— Ravi Kumar, Q4 FY2025
“Now, by deploying orchestrated agents, we have collapsed that cycle to ninety seconds.”
— Ravi Kumar, Q4 FY2025
“On their claim side, our clinical engine now adjudicates 96% of nurse note reviews autonomously, cutting human review times from eight hours to twenty minutes.”
— Ravi Kumar, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Jason Kupferberg): I mean, you guys have been talking about that pass-through and the AI assisted coding for a long time. But are you seeing competitors broadly engage in any additional level of contract pricing that you might characterize as a rational?
A: So we feel very confident because 40% of our software development cycle is assisted by AI. We have infused AI into our rate cards.
Q (Q1 FY2026, Jim Schneider): I was wondering if you could maybe kind of unpack the comments you made earlier, Ravi, around the token usage that you're seeing in terms of token metering and also some of the productivity or benefits you're starting to deliver.
A: Token metering is a reality, both at a project level and at an individual level. We have token metering for fixed price programs as well as for time and material.
Q (Q1 FY2026, James Faucette): What types of customers are you seeing either generally or what kind of characteristics do they have that are willing to engage with you and kind of match your march forward right now?
A: Financial services is at double-digit growth, very, very excited about it. Not only are they doing Vector 1, they're innovating new products, new services.
Q (Q4 FY2025, Jason Kupferberg): I just wanted to start on the AI topic. And obviously, some new data points coming out from certain industry participants just over the last couple of days. For example, talking about expediting ERP implementations pretty significantly. It certainly seems to us, like, Cognizant to date has been a net winner from AI.
A: I see this as an increase in our total addressable spend.
Q (Q4 FY2025, Keith Bachman): I wanted to ask about the risk and opportunities of the fixed price, or success-based contracts are now about 50% of total. And what I'm trying to understand is your pricing I think you're pricing these contracts on assumed cost curves that leverage new innovations including AI
A: we are sharing the productivity, sharing the risk with our clients, but we are actually doubling down on execution.
Q (Q4 FY2025, Keith Bachman): I think, you know, two years ago, many, including ourselves, had some concerns about the you know, what AI would do to BPO.
A: I see this as a significant tailwind to our BPO business.
Q (Q4 FY2025, Rod Bourgeois): There's also new Claude plugins geared towards workflow automation. I wanted to ask to what extent you see such workflow automation abilities impacting your market opportunity and to what extent you already have a partnership all in that area.
A: The more AI can do, the more is the opportunity for us.