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IBM · International Business Machines Corporation

Information Technology Services · mkt cap $303.2B · calls: Q1 FY2026 vs Q4 FY2025
90.0 conviction

enthusiasm:40.0 · trend:8 · quantifies:12 · impact:0 · under_radar:0 · credibility:12 · business_impact:8 · commitment:6 · confirmation:4

Enthusiasm latest 10 / prev 9 (rising)

IBM frames AI not as a standalone app bet but as enterprise plumbing—hybrid orchestration, governed data-in-motion (Confluent/watsonx), automation/control planes, and in-line mainframe inferencing (Spyre/Z)—with model neutrality ('Switzerland'). Management backs the thesis with unusually concrete metrics: $1.5B+ software AI revenue growing 40%+, consulting GenAI at $4B ARR and ~30% of backlog, and AI-linked MIPS/Z17 pull-through. Credibility is strengthened by repeated quantification and client-zero proof (Bob at 45% productivity, $4.5B internal savings), though much consulting AI revenue remains services-heavy and the standalone $12.5B cumulative book metric was retired as AI became embedded.

GROUNDED NEXT-FY IMPACT vs CONSENSUS

Grounded on actual base — revenue $67.5B · net income $10.6B · net margin 15.7% · diluted EPS 11.17

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

Aggregate next-FY est. rev uplift: 1.604% · next-FY EPS uplift: 10.281% · vs analysts: inline · priced in: high · confidence: 6/10

ClaimFigureArithmeticNext-FY Rev %Next-FY EPS %
Internal productivity savings (transformation engine)
productivity
$4.5B cumulative since 2023; +$1B expected in 2026Next-FY (FY2026) incremental only: $1.0B pre-tax × (1−0.21) = $790M after-tax; EPS uplift = $790M / $10,593M NI = 7.46%; rev uplift = $0 / $67,535M = 0%. Cumulative $4.5B is historical stock, not FY26 increment.07.458
IBM Bob developer productivity
productivity · soft
45% average productivity gains; entire developer workforcePercent productivity with no wage base, FTE count, or $ savings stated; cannot convert to $/NI without invented inputs → null.
Software AI platform revenue (agents/orchestration)
revenue
North of $1.5B TTM; ~25% penetrated; >40% growth; 2 pts software growthStock: $1.5B / $67,535M = 2.22% of company rev (already in base, not incremental). Incremental FY26 (hard): +2 pts on software ≈ $28.28B × 2% = $565.6M → $565.6M / $67,535M = 0.84% rev; @30% incr net margin (software) → $169.7M / $10,593M NI = 1.60% EPS. Alt YoY on platform: $1.5B × 40% = $600M = 0.89% rev / 1.70% EPS — overlaps 2-pt contribution; not summed in aggregate.0.8381.602
Consulting GenAI backlog penetration
engagement · soft
~30% of backlogMix only; $32B consulting backlog (Q4 FY25) × 30% = $9.6B GenAI backlog stock — no FY26 revenue recognition rate or net-new $ stated → null.
Consulting GenAI ARR
revenue · soft
Eclipsed $4 billion ARR in Q1Stock run-rate: $4.0B / $67,535M = 5.93% of total rev (embedded). Incremental FY26 phasing assumption: +20% YoY on $4B exit = $800M incr rev → $800M / $67,535M = 1.18% rev; @15% incr net margin (consulting) → $120M / $10,593M = 1.13% EPS. Step-up Q4 exit $3.6B → Q1 $4.0B = $400M (too short to annualize).1.1851.133
Consulting GenAI signings/backlog/revenue mix
engagement · soft
~40% signings; 30% backlog; >20% revenueMix metrics; ~20% of consulting rev ≈ $4B ARR (consistent with $4B ARR claim) but no incremental $ for FY26 → null.
Mainframe AI inferencing capacity
other · soft
~450 billion inferences per dayTechnical throughput; no $ revenue or savings per inference stated → null.
watsonx Code Assistant for Z MIPS growth
engagement · soft
3x faster MIPS capacity growth vs non-deployedClient cohort differential; no IBM revenue $ or MIPS $ attach rate → null.
Real-time fraud detection client savings
other · soft
Tens of millions of dollars (client savings)Customer P&L benefit, not IBM revenue/opex; unbounded 'tens of millions' → null per guardrail.
Data segment growth (GenAI-driven)
revenue
16% Q1; guided low 20%+ for FY2026Segment map: Data ≈ $5.75B FY25 base. Incremental FY26 vs ~11% baseline trend: (20%−11%) × $5.75B = $517.3M → $517.3M / $67,535M = 0.77% rev; @25% incr net margin → $129.3M / $10,593M = 1.22% EPS.0.7661.221
Z17 new MIPS shipments
engagement · soft
>100% growth of new MIPS for 4 consecutive quartersUnit/volume growth rate without Systems revenue $ or incremental FY26 $ → null.
Cumulative GenAI book of business
revenue · soft
Over $12.5B ($2B+ software; $10.5B+ consulting)Cumulative bookings stock: $12.5B / $67,535M = 18.5% of LTM rev if recognized in one year (not realistic). No FY26 recognition % stated → null for next-FY P&L.
Consulting GenAI quarterly book
revenue · soft
Surpassed $2 billion in the quarter (Q4 FY2025)Bookings flow ($2B/qtr ≈ $8B annualized), not revenue; recognition phasing unknown → null.
Consulting GenAI ARR run rate (exit Q4)
revenue · soft
$3.6 billionSuperseded by $4.0B Q1 ARR; stock metric. Q4→Q1 step +$400M ARR — not full-year FY26 increment alone → not additive to ARR growth claim above.
Consulting GenAI mix (exit Q4)
engagement · soft
>⅓ bookings; >25% backlog; >15% revenueMix on $32B backlog / consulting rev; no incremental FY26 $ beyond ARR/book claims → null.
Project Bob internal productivity
productivity · soft
20,000+ IBMers; 45% average productivity gainsMechanism for transformation-engine savings; overlaps $1B FY26 productivity claim — not double-counted.
Z17 vs Z16 AI inferencing throughput
other · soft
50% more AI inferencing operations per dayProduct spec; no $ attach → null.
Productivity savings run-rate target by 2026
productivity · soft
$5.5B annual run rate by 2026 (+$1B incremental in 2026)Duplicate of +$1B FY26 incremental (same arithmetic as first claim); $5.5B is cumulative run-rate target, not extra FY26 $ beyond $1B.0
Software GenAI book of business
revenue · soft
Over $2 billion; up 2x in Q4Bookings stock; overlaps software AI platform revenue. No FY26 revenue recognition % → null.

Assumptions: Next fiscal year = FY2026 (year ending 2026-12-31). LTM base: rev $67.535B, NI $10.593B, net margin 15.69%. Tax rate 21% on productivity saves (after-tax factor 0.79). Incremental margins: software/AI platform 30%; Data 25%; default 15.69% not used where segment-specific. Software rev proxy $28.28B for '2 points of growth' (= +2pp on growth rate ≈ 2% × segment rev). Data segment $5.75B; FY26 guide 'low 20%+' vs ~11% baseline → +9pp incremental growth. Consulting GenAI ARR: +20% YoY on $4B exit (soft phasing). Productivity: only +$1B FY26 incremental counted once. Bookings/ARR stock, mix %, MIPS/inferencing, and Bob 45% excluded from aggregate to avoid double-count. Confluent ~$600M FY26 dilution (forward statement) not modeled in uplift.

Top line: Hard FY26 revenue math sums non-overlapping items: software AI +2 pts ≈ $565.6M (0.84% of $67.535B) + Data GenAI-driven extra growth ≈ $517.3M (0.77%) = ~1.60% company rev uplift (~$1.08B). Existing run-rates ($1.5B+ software AI platform, $4B consulting GenAI ARR) are already ~8% of LTM rev and not incremental. Bookings ($12.5B cumulative, $2B/qtr consulting) and mix metrics do not add defensible FY26 P&L without invented recognition rates.

Bottom line: Dominant hard bottom-line item: $1.0B FY26 productivity savings → $790M after-tax → +7.46% vs LTM NI; topline 0% unless reinvested. Software (+1.60% EPS) and Data (+1.22% EPS) add ~2.8% EPS on LTM base. Aggregate hard EPS uplift ≈10.3% on LTM NI (~$1.09B), before optional +1.13% EPS from assumed +20% consulting GenAI ARR growth (soft). $1B productivity alone is ~70% of consensus FY25→FY26 NI increase ($1.13B).

FY2025 actuals: rev $67.535B, EPS $11.17, NI $10.593B. FY2026 consensus: rev $71.467B (+5.82% / +$3.93B), EPS $12.44 (+11.34%), NI $11.723B (+10.67%). Hard AI-attributable FY26 uplift ≈1.60% rev and ≈10.3% EPS on LTM base — revenue math is well below consensus +5.82% (gap ~4.2pp), implying analysts already embed mainframe cycle, Confluent, and broader software/consulting growth beyond identifiable AI claims. EPS bridge is essentially inline: 10.3% modeled vs 11.3% consensus NI growth; $790M after-tax productivity ≈70% of the $1.13B consensus NI step. Optional consulting ARR +20% would lift totals to ~2.79% rev / ~11.4% EPS — still rev-light vs consensus. Forward Confluent ~$600M dilution is a headwind not in positive AI math. Priced-in high: quantified AI is largely run-rate disclosure plus $1B opex save already consistent with consensus EPS path.

QUANTIFICATIONS
Internal productivity savings (AI-enabled transformation engine): $4.5 billion cumulative since 2023; +$1 billion expected in 2026 (Since 2023; 2026 incremental, bottomline)
“Since 2023, this has driven $4.5 billion of productivity savings, with an additional $1 billion expected in 2026.”
IBM Bob developer productivity: 45% average productivity gains; entire developer workforce using Bob (Q1 FY2026 (GA), bottomline)
“Our entire developer workforce is using Bob with average productivity gains of 45%.”
Software AI platform revenue (agents, assistance, orchestration): North of $1.5 billion TTM; ~25% penetrated in software; growing north of 40%; contributing 2 points of software growth (Trailing 12 months (Q1 FY2026), topline)
“our AI platform agents, assistance orchestration is north of $1.5 billion. It's already about 25% penetrated and our software business growing north of 40%. It's contributing 2 points of growth on an annualized basis.”
Consulting GenAI backlog penetration: ~30% of backlog (Q1 FY2026, topline)
“Generative AI is now firmly integrated across our consulting engagements, representing about 30% of our backlog.”
Consulting GenAI ARR: Eclipsed $4 billion ARR in Q1 (Q1 FY2026, topline)
“in the first quarter, we eclipsed $4 billion ARR.”
Consulting GenAI signings/backlog/revenue mix: ~40% of signings; 30% of backlog; over 20% of revenue (Q1 FY2026, topline)
“Consulting is about 40% of our signings, 30% of our backlog is GenAI now, over 20% of our revenue.”
Mainframe AI inferencing capacity: ~450 billion inferences per day on a fully populated system (Current capability (Q1 FY2026), topline)
“Currently, I believe we have a fully populated system we can do about 450 billion inferences a day on the mainframe.”
watsonx Code Assistant for Z — MIPS growth differential: 3x faster MIPS capacity growth vs. non-deployed clients (Observed client cohort comparison, topline)
“Clients who have deployed watsonx Code Assistant for Z are growing MIPS capacity 3x faster than those who have not.”
Real-time fraud detection client savings: Tens of millions of dollars (Current client outcomes, both)
“Financial services clients are using this for real-time fraud detection, saving tens of millions of dollars.”
Data segment growth (GenAI-driven): 16% Q1; guided low 20%+ for FY2026 (Q1 FY2026; full year 2026, topline)
“Data revenue grew 16%, fueled by demand for our GenAI products”
Z17 new MIPS shipments: Over 100% growth of new MIPS for 4 consecutive quarters (Through Q1 FY2026, topline)
“for 4 quarters in a row, on Z17, we've shipped over 100% growth of new MIPS in the market, including first quarter.”
Cumulative GenAI book of business: Over $12.5 billion ($2B+ software; $10.5B+ consulting) (Exit Q4 FY2025, topline)
“Our cumulative Gen AI book of business now stands at over $12.5 billion, of which software is more than $2 billion and consulting is more than $10.5 billion”
Consulting GenAI quarterly book: Surpassed $2 billion in the quarter (largest GenAI quarter) (Q4 FY2025, topline)
“Our consulting generative AI book of business surpassed $2 billion in the quarter. Our largest quarter of GenAI.”
Consulting GenAI ARR run rate: $3.6 billion (Exit Q4 FY2025, topline)
“We have a $3.6 billion ARR Gen AI revenue run rate. In consulting”
Consulting GenAI mix: Over a third of bookings; over 25% of backlog; over 15% of revenue (Exit Q4 FY2025, topline)
“Gen AI now represents over a third of our bookings, over 25% of our backlog right now, $32 billion backlog, and over 15% of our revenue on an exit run rate.”
Project Bob internal productivity: 20,000+ IBMers; 45% average productivity gains (Q4 FY2025, bottomline)
“We have more than 20,000 IBMers that are using Project Bob, reporting productivity gains averaging 45%”
Z17 vs Z16 AI inferencing throughput: 50% more AI inferencing operations per day (Q4 FY2025, topline)
“processing 50% more AI inferencing operations per day than Z16”
Internal productivity savings run rate target: $5.5 billion annual run rate by 2026 (+$1B incremental in 2026) (By 2026, bottomline)
“we now expect an incremental $1 billion of productivity savings this year. Driving $5.5 billion of annual run rate savings by 2026.”
Software GenAI book of business growth: Over $2 billion; up 2x in Q4 (Q4 FY2025, topline)
“Gen AI, over $2 billion book of business, up two x in the fourth quarter”
PAST (realized)
CURRENT (now)
FORWARD (guidance)
TRACK RECORD — PROMISE vs DELIVERY

86/100 track record   delivers  7 calls reviewed

IBM rarely sets dated GenAI revenue targets; credibility rests mainly on productivity and infrastructure AI milestones, which they met or beat ($3.5B then $4.5B savings, z17 on time). Software growth came close but consulting/quantum items are partial or still open.

$3.5B annual run-rate productivity savings by end of 2024 (AI embedded across enterprise workflows) — promised Q3 FY2024
delivered Q1 FY2025 confirmed exit at $3.5B run-rate savings achieved in 2024.
$2B productivity savings exiting 2024 (2023 goal; AI-enabled transformation) — promised Q3 FY2025
delivered Later calls stated IBM was well ahead, with $3.5B achieved exiting 2024 and the target superseded by a higher 2025 goal.
$4.5B annual run-rate productivity savings by end of 2025 — promised Q2 FY2025
delivered Q3–Q4 FY2025 and Q1 FY2026 confirmed $4.5B run-rate savings exiting 2025.
z17 mainframe launch mid-2025 with enhanced on-platform AI acceleration — promised Q4 FY2024
delivered Launched in Q2 FY2025; subsequent quarters cited strong z17 AI inferencing uptake and Spyre availability in Q4 FY2025.
Software revenue growth approaching double digits for FY2025 (GenAI/innovation-led) — promised Q4 FY2024
partial FY2025 software grew 9% (record annual rate) but slightly below approaching-10% framing.
Quantum Advantage by 2026 (IBM hardware/software enabling partner claims) — promised Q4 FY2025
too-early As of the latest call (Q1 FY2026), management still expects partner Quantum Advantage examples in 2026; no clear company KPI hit yet in the transcript set.
PRICED-IN (REFINED)
HIGH (already in)

Est. revisions rising  ·  Fwd P/E 28.7  ·  EV/Sales 5.3x

AI claim maps to Software, Consulting, Infrastructure Services

Analyst sentiment is migrating up: combined strongBuy+buy counts rose from 9 to 13 over six months, sells edged down, and lastMonthAvg price targets ($330) are well above lastQuarterAvg ($292.82) while the stock at ~$326 has largely caught that raise. Forward consensus already bakes steady growth (FY26 revenue +6.6%, EPS +9.5%; FY27 EPS +7.9%), so the AI thesis is partly in the numbers, not purely optionality. At ~28.7x next-FY P/E and ~5.3x EV/Sales—rich for a mature IT services mix—the market is paying a premium for AI/hybrid-cloud upside that would flow mainly through Software and Consulting; rising revisions plus stretched multiples point to upside largely reflected.
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
8Q4 FY20248Q1 FY20259Q2 FY20259Q3 FY20259Q4 FY20259Q1 FY2026

AI enthusiasm across 6 calls — trend ↗ rising

GenAI book grew $5B to $12.5B+ with deeper agentic platform, z17 inferencing, and $4.5B internal productivity proof.

RECENT AI CATALYSTS & NEWS
BUSINESS IMPACT - QUALITATIVE MATERIALITY

8/10 qualitative impact   material  near-term · mixed evidence

Where AI matters: watsonx/software AI platform, GenAI consulting delivery, internal productivity, Z mainframe inferencing

Disclosed run-rates (~$1.5B+ software AI platform, ~$4B consulting GenAI ARR, ~30% consulting backlog) and a credible $1B FY26 internal savings line show AI is operating across software, services, and cost—not slideware; incremental FY26 revenue math (~1.6% of ~$67.5B) and services-heavy consulting mix cap how company-transforming the topline looks versus the narrative.

Caveats: Consulting GenAI ARR and backlog mix may be labor-augmented services with weaker margin and retention than platform software; Incremental identifiable AI revenue is small versus total growth and consensus, so cycle/Confluent/mainframe could dominate outcomes; Mainframe inference/MIPS metrics (e.g., 450B inferences/day, 3x MIPS cohort) lack clean IBM revenue attribution; Retired cumulative AI bookings metric and embedded AI reporting reduce transparency as claims get harder to audit

OPTIONS / MARKET STRUCTURE

option liquidity: good

ATM IV
TYPICAL BID-ASK
OPEN INTEREST

proxy inputs — dollar-ADV $2.3B · beta 0.581 · px $326.09

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 7/10 committed.
INSIDERS buying 2 open-market buy(s) vs 0 sell(s) — net accumulation
INSTITUTIONS (13F) flat as of 2026-03-31: 179 new / 411 closed positions; 1675 increased / 1328 reduced; institutional ownership -0.92pp; -229 net 13F holders
MGMT LANGUAGE 7/10 committed Long AI section with GA Bob, 45% and 3x metrics; tempered by building/platform and proof-of-concept framing.
commit “IBM Bob, our AI-based software development system, is now generally available.”
commit “Our entire developer workforce is using Bob with average productivity gains of 45%.”
commit “Clients who have deployed watsonx Code Assistant for Z are growing MIPS capacity 3x faster than those who have not.”
VERBATIM AI QUOTES
“They are modernizing core systems. They are scaling AI and they're making deliberate choices about where workloads should run and who controls the infrastructure underneath them.”
— Arvind Krishna, Q1 FY2026
“We also had strong performance in Distributed Infrastructure as generative AI increases demand for our storage offerings.”
— Arvind Krishna, Q1 FY2026
“Consulting grew 1% with momentum in enterprise data and business application transformations as clients modernize to deploy AI securely and at scale.”
— Arvind Krishna, Q1 FY2026
“Right now, the spotlight is on foundation models. Enterprises are building portfolios, frontier models for some workloads, smaller models running on-premise for others and open source models where control and flexibility matter the most.”
— Arvind Krishna, Q1 FY2026
“The core challenge is making all of this work together. This includes orchestrating across models, agents and workflows, governing enterprise data and securing these systems at scale. And that is exactly where IBM operates.”
— Arvind Krishna, Q1 FY2026
“We are building the platform that lets enterprises put AI to work on their terms, wherever it runs, whichever models they choose and under governance they control.”
— Arvind Krishna, Q1 FY2026
“More AI adoption means more demand for open flexible infrastructure.”
— Arvind Krishna, Q1 FY2026
“In automation, the logic is similar, agents multiply applications, integrations and execution paths. Managing that sprawl requires a controlled plane to provision infrastructure, integrate applications, secure environments and manage cost.”
— Arvind Krishna, Q1 FY2026
“In an AI-driven world, security risks are rising. IBM Concert identifies vulnerabilities proactively and automates remediation, helping enterprises maintain resilience at scale.”
— Arvind Krishna, Q1 FY2026
“AI is only as good as the data it can access. And increasingly, that data is not static. It is generated continuously across transactions, applications and interactions.”
— Arvind Krishna, Q1 FY2026
“Confluent, which we closed this past quarter, solves that directly. It streams live, governed data to models and agents across the hybrid environment.”
— Arvind Krishna, Q1 FY2026
“In a multi-model world, clients need to route between models, manage agent workflows and maintain governance. That is what watsonx Orchestrate and our watsonx platform deliver.”
— Arvind Krishna, Q1 FY2026
“We have also created AI additions of critical software products like Db2, Cognos and MQ. These embed agentic AI that can reason, act and automate at scale while preserving IBM grade security and trust.”
— Arvind Krishna, Q1 FY2026
“They run AI inferencing directly in line with those transactions. Our Spyre accelerator lets clients run AI on 100% of the transaction volume without moving data off platform, allowing them to embed AI directly into their transaction flows.”
— Arvind Krishna, Q1 FY2026
“Financial services clients are using this for real-time fraud detection, saving tens of millions of dollars.”
— Arvind Krishna, Q1 FY2026
“At the same time, AI-assisted modernization, including code understanding, refactoring and API integration makes it easier to evolve applications without compromising the guarantees the platform provides.”
— Arvind Krishna, Q1 FY2026
“Clients who have deployed watsonx Code Assistant for Z are growing MIPS capacity 3x faster than those who have not.”
— Arvind Krishna, Q1 FY2026
“In consulting, AI is both a growth driver and a productivity engine. As agents take on more work, delivery becomes faster, more software driven and more scalable.”
— Arvind Krishna, Q1 FY2026
“Demand continues to accelerate as clients move beyond experimentation and focus on transforming applications, data and workflows to embed AI into core operation.”
— Arvind Krishna, Q1 FY2026
“ServiceNow is leveraging watsonx for automated data quality and observability to deliver AI-ready data and code generation to refresh legacy applications to modern application run times, including ServiceNow.”
— Arvind Krishna, Q1 FY2026
“With Nestle, we are using NVIDIA accelerated watsonx.data to embed AI directly into core order-to-cash operations, enabling faster real-time insights across Nestle's global supply chain.”
— Arvind Krishna, Q1 FY2026
“IBM Bob, our AI-based software development system, is now generally available. Our entire developer workforce is using Bob with average productivity gains of 45%.”
— Arvind Krishna, Q1 FY2026
“Bob automates the full software life cycle from legacy modernization to security using specialized agents and multimodal optimization.”
— Arvind Krishna, Q1 FY2026
“We also introduced Sovereign Core software that lets organizations run AI workloads under their own operational authority within a defined jurisdiction and auditable controls.”
— Arvind Krishna, Q1 FY2026
“Data revenue grew 16%, fueled by demand for our GenAI products, strengthen our strategic partnerships and inorganic contribution from data stack and Confluent, which closed in mid-March.”
— James Kavanaugh, Q1 FY2026
“Clients are investing in IBM Z as they modernize mission-critical workloads driven by requirements for resiliency, security and compliance, while enabling new AI capabilities on the platform.”
— James Kavanaugh, Q1 FY2026
“In storage, growth reflected strong adoption of our new flash offerings introduced in the first quarter, which incorporate industry-leading agentic AI capabilities.”
— James Kavanaugh, Q1 FY2026
“Signings returned to growth, up 6% with strength across our application and data transformation offerings, driven by clients modernizing their environments to support AI adoption and capture value.”
— James Kavanaugh, Q1 FY2026
“Generative AI is now firmly integrated across our consulting engagements, representing about 30% of our backlog.”
— James Kavanaugh, Q1 FY2026
“Through disciplined execution, eliminating manual touch points, simplifying processes and applying data, automation and AI at scale, we have built a proven repeatable AI-enabled transformation engine that is accelerating.”
— James Kavanaugh, Q1 FY2026
“Since 2023, this has driven $4.5 billion of productivity savings, with an additional $1 billion expected in 2026.”
— James Kavanaugh, Q1 FY2026
“We remain confident this will be our strongest cycle given the AI innovation value we are delivering to clients.”
— James Kavanaugh, Q1 FY2026
“AI is fundamentally reshaping our clients' operating environments, increasing complexity, risk and the need for flexibility.”
— James Kavanaugh, Q1 FY2026
“As people do more and more fraud protection, not on sampling 1 in 10 transactions in the mainframe, but every single, that causes the mainframe consumption to go up. And we can see that, by the way, in the mainframe numbers we printed in the first quarter.”
— Arvind Krishna, Q1 FY2026
“Value is going to decrease in that interaction layer because as agents replace people for some fraction, we can debate how much of the interactions, then the interaction layer by itself is not sticky. The agents are going to be interacting much more with the underlying data and the business logic.”
— Arvind Krishna, Q1 FY2026
“What happens if you could run a 20 billion, 30 billion parameter model right on the mainframe, suddenly because that is only milliseconds of latency, you can do that to every single transaction.”
— Arvind Krishna, Q1 FY2026
“Currently, I believe we have a fully populated system we can do about 450 billion inferences a day on the mainframe.”
— Arvind Krishna, Q1 FY2026
“Over the last trailing 12 months on an accelerating basis, our AI platform agents, assistance orchestration is north of $1.5 billion. It's already about 25% penetrated and our software business growing north of 40%. It's contributing 2 points of growth on an annualized basis.”
— James Kavanaugh, Q1 FY2026
“Consulting is about 40% of our signings, 30% of our backlog is GenAI now, over 20% of our revenue. And on an ARR revenue perspective, in the first quarter, we eclipsed $4 billion ARR.”
— James Kavanaugh, Q1 FY2026
“clients that have implemented watson Code assistant for Z, we're seeing 3x differential on growth and capacity”
— James Kavanaugh, Q1 FY2026
“We made the decision about 3 years ago that we were going to be neutral and Switzerland like also on our usage of frontier models.”
— Arvind Krishna, Q1 FY2026
“we are executing on our strategy to advance IBM as a software-led hybrid cloud and AI platform company.”
— Arvind Krishna, Q4 FY2025
“Enterprises are prioritizing technology investments that drive productivity, resilience, and flexibility, particularly in hybrid cloud, AI, and mission-critical infrastructure.”
— Arvind Krishna, Q4 FY2025
“As AI adoption accelerates, enterprise clients are increasingly focused on how to keep operations running smoothly in a more complex and hybrid environment fueled by a surge of new applications.”
— Arvind Krishna, Q4 FY2025
“Consulting continued to grow, up 1%, reflecting increased demand for AI services as clients need help designing, deploying, and governing AI at scale.”
— Arvind Krishna, Q4 FY2025
“A key contributor to this momentum is the innovation value we are delivering with Z17, processing 50% more AI inferencing operations per day than Z16 and bringing real-time inferencing capabilities inside IBM Z.”
— Arvind Krishna, Q4 FY2025
“Our cumulative Gen AI book of business now stands at over $12.5 billion, of which software is more than $2 billion and consulting is more than $10.5 billion, with both seeing their largest quarterly increase to date.”
— Arvind Krishna, Q4 FY2025
“Our opportunity is to make it easy for clients to build AI that is specific to their data, their processes, and their competitive needs, including the effective use of smaller, more efficient models where they make sense.”
— Arvind Krishna, Q4 FY2025
“Confluent has the most capable technology to unlock the real-time value of data across applications, clouds, APIs, and as AI agents enter the enterprise, they will need access to that data in real-time.”
— Arvind Krishna, Q4 FY2025
“IBM's hybrid approach to models also enables clients to use the best option for each use case. IBM's Granite models, third-party models, or open models from Hugging Face, Meta, and Mistral.”
— Arvind Krishna, Q4 FY2025
“In addition to being a demand driver, AI is also a powerful productivity driver for IBM. Contributing to our strong financial performance.”
— Arvind Krishna, Q4 FY2025
“today, we are well ahead of that, exiting 2025 with $4.5 billion of annual run rate savings.”
— Arvind Krishna, Q4 FY2025
“Project Bob is IBM's next-generation AI-based software development system designed to transform developer productivity.”
— Arvind Krishna, Q4 FY2025
“We have more than 20,000 IBMers that are using Project Bob, reporting productivity gains averaging 45%, a powerful client zero use case.”
— Arvind Krishna, Q4 FY2025
“we recently developed Hashi InfraGraph, a real-time graph of infrastructure and application configuration. By fusing InfraGraph's insights with IBM automation products like Concert, we unlock true root cause analysis and proactive prevention for clients.”
— Arvind Krishna, Q4 FY2025
“Data grew 19%, fueled by the demand for our Gen AI products and strong performance with established strategic partners.”
— Jim Kavanaugh, Q4 FY2025
“Clients are investing in Z17, for its differentiated capabilities, real-time AI inferencing, quantum-safe security, and AI-driven operational efficiency.”
— Jim Kavanaugh, Q4 FY2025
“As clients prioritize cost efficiency, while continuing to invest in AI-enabled transformation.”
— Jim Kavanaugh, Q4 FY2025
“Our consulting generative AI book of business surpassed $2 billion in the quarter. Our largest quarter of GenAI.”
— Jim Kavanaugh, Q4 FY2025
“We exited 2025 with the Gen AI book of business greater than $12.5 billion.”
— Jim Kavanaugh, Q4 FY2025
“AI is now embedded across our business. From how we deliver services to our software portfolio to the capabilities we're adding to our infrastructure platforms, and how we drive our own productivity.”
— Jim Kavanaugh, Q4 FY2025
“As a result, a standalone Gen AI metric no longer reflects the full scope of how AI is driving value across IBM.”
— Jim Kavanaugh, Q4 FY2025
“as people have more and more infrastructure, they put more and more AI, they put more and more compute. They need that software to help them manage all of it.”
— Arvind Krishna, Q4 FY2025
“If you can do it right in line with the transactions, that's a milliseconds delay. Opposed to multiple seconds if you take it off-platform.”
— Arvind Krishna, Q4 FY2025
“Gen AI now represents over a third of our bookings, over 25% of our backlog right now, $32 billion backlog, and over 15% of our revenue on an exit run rate.”
— Jim Kavanaugh, Q4 FY2025
“We have a $3.6 billion ARR Gen AI revenue run rate. In consulting”
— Jim Kavanaugh, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Amit Daryanani): As AI adoption really scales, where in that stack, do you see the most incremental value accruing to IBM versus the ecosystem.
A: Arvind Krishna: As they get to scale, they've got to use the data from their internal systems... many parts of our portfolio, be it Red Hat, be it Confluent, will come to be consumed more and more... As people do more and more fraud protection, not on sampling 1 in 10 transactions in the mainframe, but every single, that causes the mainframe consumption to go up... Value is going to decrease in that interaction layer because as agents replace people for some fraction... the agents are going to be interacting much more with the underlying data and the business logic... AI is structurally increasing the demand for the portfolio.
Q (Q1 FY2026, Fatima Boolani): Put quantitative framing around mainframe AI inferencing — workload mix today vs. emerging AI use cases, and how transaction processing / MIPS growth momentum should transpire.
A: Arvind Krishna: AI is adding a third kind of compute capacity into the mainframe... What happens if you could run a 20 billion, 30 billion parameter model right on the mainframe... you can do that to every single transaction... Currently, I believe we have a fully populated system we can do about 450 billion inferences a day on the mainframe. James Kavanaugh: 450 billion AI inferences at 1 millisecond of response time... for 4 quarters in a row, on Z17, we've shipped over 100% growth of new MIPS in the market.
Q (Q1 FY2026, James Schneider): Comment on AI bookings metric — did it accelerate or decelerate? Update on consulting growth given macro uncertainty.
A: James Kavanaugh: We exited last year with a book of business around AI... over $12.5 billion... our AI platform agents, assistance orchestration is north of $1.5 billion... growing north of 40%... contributing 2 points of growth... Consulting... 30% of our backlog is GenAI now, over 20% of our revenue... eclipsed $4 billion ARR... 80% of our GenAI book of business right now is coming from capture from net new clients.
Q (Q1 FY2026, Matt Swanson): How are you setting IBM up to win regardless of who wins the GenAI application layer? What investments does that take?
A: Arvind Krishna: We made the decision about 3 years ago that we were going to be neutral and Switzerland like also on our usage of frontier models... Project Bob... 200 people signed up to use it... we use Confluent to go manage and control how they expose data from inside things... We want to work with all of them [frontier model providers]... We produce models where we have either domain expertise or people may want much smaller models to be able to run them on-premise.
Q (Q4 FY2025, Brent Thill): Dig into components of software growth acceleration to double-digit and what's important on the software portfolio for 2026.
A: Arvind Krishna: as people have more and more infrastructure, they put more and more AI, they put more and more compute. They need that software to help them manage all of it... data benefits both from our data products... the organic innovation we have done with WatsonX, both the AI pieces and the Orchestrate piece for agents... Jim Kavanaugh: Gen AI, over $2 billion book of business, up two x in the fourth quarter.
Q (Q4 FY2025, Jim Schneider): Outline consulting trajectory — signings were weaker but strong AI backlog; how does that convert to revenue in 2026?
A: Jim Kavanaugh: Gen AI now represents over a third of our bookings, over 25% of our backlog right now... over 15% of our revenue on an exit run rate. We have a $3.6 billion ARR Gen AI revenue run rate. In consulting, and we see that continue and accelerate.
Q (Q4 FY2025, Eric Woodring): Could mainframe AI capabilities drive more sustainability in the Z cycle than infrastructure guidance accounts for?
A: Arvind Krishna: If you can do it right in line with the transactions, that's a milliseconds delay. Opposed to multiple seconds if you take it off-platform... a very large number of our clients actually told us they're interested and they kept space in the machines to put in those cards. We call them the spire cards... Expect that will take some months to happen, but that provide tailwinds against some of the dynamics.
Q (Q4 FY2025, Matt Swanson): Holistic view: what does strength in Z refresh, data, automation, and consulting say about enterprise GenAI transformation today and heading into 2026?
A: Arvind Krishna: Gen AI so far has largely been a consumer topic... [transcript cuts off]
Q (Q4 FY2025, Wamsi Mohan): Could higher memory pricing from AI servers pressure the server refresh cycle and Red Hat Enterprise Linux?
A: Arvind Krishna: A big reason for that... is because a lot of the capacity is moving over to HBM... There is no AI server without a bunch of CPUs right next to it... I also believe that both Red Hat AI and OpenShift AI will feed into the demand from the AI servers which was gonna help that demand.