The Agentic Dividend: Stocks Leaning Most Aggressively into Agentic AI Labor Cost Reductions
A comprehensive examination into how agentic AI is reshaping workforces across industries
By reading this article, you acknowledge and agree to be bound by the terms of our full legal disclaimer. The information provided herein is for educational and general informational purposes only and does not constitute professional financial or investment advice. Discussion of any stock within this report is only for the purposes of highlighting the company’s plans in agentic AI and does not convey any investment recommendation.
Agentic AI
The first wave of AI changed how people worked. The next wave may change how many people are needed to do the work.
Only a few months ago, agentic AI was still an obscure term, albeit rising in frequency in company disclosures. Now with the recent earnings reports of software and fintech companies like Wisetech Global and Block, both of whom have announced 30%+ headcount reductions across their businesses linked to the technology, investors must actively get up to speed on how agentic AI may shape a company’s operating model, margins and earnings growth going forward. That is the real significance of the agentic AI era.
Unlike the chatbot phase, which largely revolved around assisting employees with drafting, summarizing, searching, and answering questions, agentic AI is increasingly being deployed to take on whole workflows: resolving customer issues, handling back-office processes, writing and testing code, and coordinating multi-step tasks with less human supervision. In practical terms, that shifts AI from being a productivity layer on top of labor to something much closer to digital labor itself. The strategic question for companies is no longer just whether AI can make employees more efficient. It is whether AI can allow the business to operate with fewer people, different people, or a fundamentally different cost base.
That is why the companies leaning hardest into agentic AI are now attracting attention. The payoff is potentially significant if companies can deploy the technology in place of 30%+ of certain workforces: lower support and administrative costs, better operating leverage, flatter headcount growth, and a more powerful translation from revenue into margin and earnings. Block rallied 20% following its announcement and Wisetch Global rallied 18% signifying at least initial positive surprise from the extent of the labor cost reduction opportunity.
At the same time, many of these companies are leading the way because they have been forced into early adoption due to threats to their traditional business models from artificial intelligence. Block had fallen 35% and Wisetech over 40% since November as the SaaSpocalypse gathered attention. Other examples in this report are down 60% from their peaks. This is the opportunity and risk for investors in stark focus: those stocks targeting the largest and most aggressive margin opportunities from agentic AI are also the ones whose share prices have often fallen the most due to the perceived AI threat. Successful execution may well see share price rebounds while botched execution could damage the stocks further.
This year, the evidence on agentic AI has become hard to ignore. Public disclosures increasingly show management teams moving beyond pilot programs and into explicit labor redesign. This trend has been highlighted in the recent hyperscaler earnings calls from Microsoft, Alphabet, and Amazon. Salesforce has highlighted a major reduction in customer support staffing while shifting toward more consumption-oriented pricing. Block is cutting more than 40% of its workforce as part of an intelligence-native reset. WiseTech has outlined a multi-year reduction of around 2,000 roles, or 30%. Fiverr has also annoucned a reduction in staff of 30%. C3.ai has tied 26% workforce cuts to a plan to remove substantial operating expense. What makes this moment different is not just the technology, but the willingness of companies to connect AI deployment directly to labor reduction, margin expansion, and future earnings power.
But there is no clean or guaranteed path through this transition. Large-scale agentic implementation brings real risks: many revenue models may have to evolve from stable seat-based subscriptions to more variable consumption or outcome-based pricing which can alienate customers, customer experience can deteriorate, internal morale can fracture as workforces are reshaped, and operational complexity can rise before efficiencies actually become durable. The upside is compelling, but so is the execution risk. In the agentic era, the winners may not simply be the companies adopting AI fastest. They may be the ones that can redesign their labor model without breaking their product, their culture, or their economics.
This article is written for two audiences. First it is structured into industries to show readers with a general interest in the topic, where agentic AI pressure will appear on workforces and functions. Second, it is for investors, to demonstrate which stocks are leaning in the most, which industries have highly material margin opportunities and those where the impact may be may be more muted and longer dated. In specific cases I also attempt to highlight where these initiatives are mostly a defensive measure to offset structural headwinds.
The only way to assess progress on agentic AI adoption and benefits in the era of a new, unproven technology is to identify the adopters, specify the KPIs and monitor their progress through their earnings disclosures. The KPIs are clear: normalized (ex restructuring) labor cost / revenue ratios, gross and operating margins, revenue per employee, R&D / revenue ratio, customer review sentiment checks, net promoter score trends. These metrics are where progress will be shown or disproved.
Here are the companies that, based on the most recent public disclosures since late 2025, are targeting the most aggressive margin agentic AI implementations or disclosing the most tangible financial targets:
Enterprise Software / SaaS
Salesforce (NYSE: CRM)
Salesforce remains one of the clearest examples of a software company using agentic AI both defensively and offensively. Defensively, it is applying Agentforce internally to absorb labor in customer support, where the company says the system is handling roughly 1.5 million incoming queries with customer satisfaction “nearly identical” to human service levels. Offensively, it is using the same technology to reposition its commercial model away from seat-based exposure and toward monetizing autonomous digital labor. That combination matters because it addresses the two most important questions in the agentic era for SaaS: whether AI can reduce the vendor’s own operating expense, and whether it can preserve revenue when customers themselves need fewer human seats.
The labor implications are already material. Recent disclosures state that Salesforce reduced its customer support workforce from roughly 9,000 to approximately 5,000 people, a 44% reduction in that function, with a further 1,000 jobs cut across the broader organization in early 2026. Management explicitly linked those changes to Agentforce-driven efficiencies. For an installed-base business where support has historically scaled with customer count and product complexity, this is a meaningful structural shift in the operating model rather than a marginal productivity improvement.
The financial read-through is important. Company guidance ties the leaner labor base to an FY2027 non-GAAP operating margin of 34.3% up from 34.1% in FY26 and 33% in FY25. Shedding 5,000 total roles an an average assumed salary of $50,000 is a $250m tailwing which equates to a 40bps margin bump, presumably offset by 10-20bps of compute costs. Consensus is forecasting further margin gains over the following 2 years as well that accelerate in magnitude, however given more tepid revenue growth rates being achieved by Salesforce (than market expectations for an AI driven acceleration), it remains to be seen whether these targets can be achieved. The key equity implication is that Salesforce is attempting to convert agentic AI from a product narrative into a durable operating leverage narrative.
On the revenue side, the company’s introduction of their Agentic Enterprise License Agreement also suggests management is countering the revenue-side threat: as customers reduce human headcount, Salesforce wants to charge for agent interactions, tokens, and outcomes instead of seats. This is the shift to consumption based revenue generation, much as Adobe has implemented into its business model as the AI era was forced upon it. However as we have seen so far with Adobe, consumption pricing has failed to deliver an acceleration in revenue growth with seat based cannibalization eating away at it from the other side.
WiseTech Global (ASX: WTC)
WiseTech is one of the clearest non-US examples of a company redesigning both its labor structure and its revenue model for the agentic era. Internally, the company has deployed AI tools across its roughly 7,000 employees, including CargoWise Agent “ACE,” automated document ingestion, and AI classification assistants. Externally, it has moved around 95% of customers onto CargoWise Value Packs, abolishing traditional seat and user pricing in favor of transaction-based pricing tied to logistics execution and productivity.
That combination is highly strategic. Specialized process software faces an existential issue if its AI led product advancements make customer employees redundant: the software vendor loses paid seats. Adobe is a much debated example of this dynamic. WiseTech’s answer is to migrate away from seat pricing before that deflation becomes economically painful. This makes WiseTech one of the most important case studies in how software companies can protect revenue while also reducing their own labor base.
On labor, the scale is substantial. The internal program targets up to 50% reductions in product, development, and customer service teams and is expected to result in roughly 2,000 roles being removed across FY26 and FY27. This is approximately one-third of the workforce and one of the most aggressive AI-linked workforce actions in listed Australian software. Because R&D and support are labor-dominant cost lines in SaaS and logistics software, the margin implications are first-order rather than incremental. Total Selling, General and Administrative expenses plus R&D expenses run at approximately 40% of Wisetech’s revenue and are primarily labor orineted. These are the cost pools where Wisetech is targeting to remove over 30% of the headcount.
WiseTech sits among the most aggressive AI-first cost base resets, alongside Block (see Fintech section). Few companies are simultaneously attacking internal labor intensity, integrating acquired cost synergies ahead of schedule, and rebuilding pricing architecture to defend against the very labor deflation their products help create. The debate among analysts is the opportunity versus the risk. Wisetech discloses in Q&A some customer angst at consumptiomn based pricing changes. It is also likely that workplace cultural aspects play a role in elevating the risks associated with such large scale headcount removal. However at the same time, software businesses are unique. There have been many expamples of large scale reducdency programs being implemented in the industry with little interruption to customer experience. Wistech is a key stock to watch for how agentic programs can impact operations.
C3.ai (NYSE: AI)
C3.ai represents one of the most modelable pure-play agentic restructuring cases announced so far but like many other examples here is a company where its agentic AI push is defensive with the share price down 90%+ since its IPO. It is a company facing structural headwinds, encapsulated in a reported 46% fall in revenue, with a strategic transition to consumption based pricing proving to be insufficient to offset the loss of old, large subscription renewals. The company claims to have aggressively integrated state-of-the-art agentic AI systems, utilizing Anthropic's Claude models, across its entire operational footprint to right-size the business. It framed the company’s February 2026 reset as a direct consequence of agentic AI implementation across product development, engineering, sales, marketing, and customer service. The operational claims are aggressive:
During the Q3 FY2026 earnings call on February 25, 2026, CEO Stephen Ehikian outlined the magnitude of the claimed AI leverage to justify the severe headcount reductions:
● “In the past five weeks, I have restructured products, engineering, sales, marketing, and customer services to leverage state-of-the-art agentic AI across these business entities to dramatically increase the productivity of our people, in many cases by up to 100 times.”
● Highlighting specific departmental efficiencies, Ehikian stated: “we are leveraging agentic AI to design, develop and redeploy our website. This process previously took 9-12 months and many millions of dollars. It will now take weeks.”
● Regarding the software engineering groups utilizing Anthropic’s Claude, he cited productivity increases “by up to two orders of magnitude.”
What makes the case analytically useful is the quantified economics. The company announced a 26% workforce reduction and a plan to remove $135 million of annual non-GAAP operating expense, with roughly $60 million of that tied directly to headcount. Relative to FY2026 revenue guidance of roughly $247 million to $251 million and a projected EBIT loss of over $224m, the magnitude of the program is extremely large but also clearly necessary. Few companies in my research have articulated such a direct bridge between AI-led restructuring and a plausible path to materially different earnings.
That said, the market remains skeptical. C3’s share price has fallen a further 20% since its earnings report and it is projected to continue losing money for the next 3 years. C3 many need to find further AI led cost reduction opportunities to stem the bleeding. The report also highlights that full savings are only expected from 2H FY2027, reinforcing that agentic AI does not instantly convert layoffs into stable run-rate margin expansion. Workflow redesign, governance, and execution reliability remain gating factors.
CrowdStrike (NASDAQ: CRWD)
CrowdStrike is a less dramatic but still relevant software case because management’s framing emphasizes a structurally flatter hiring curve rather than a blunt-force labor reset. The company disclosed a reduction of roughly 500 roles, or about 5% of the workforce, with associated charges of $36 million to $53 million. A 5% cut is smaller than the most aggressive cases here, but it remains earnings‑material because (a) software security firms are personnel-cost heavy, and (b) management is explicitly signaling reduced future labor growth vs. historical which signals multi-year margin expansion.
The key point is not the absolute cut size, which is modest versus Block or WiseTech, but the underlying message that AI can allow a fast-growing security platform to scale with lower incremental labor intensity than the market may have historically assumed. After 3 years of flat EBIT margins around 21.5%, the market now has Crowdstrike’s operating margin growing linearly to over 27% by FY29 (3 years). This means that the company is projected to achieve an average of 30%+ EBIT growth off 20% revenue growth over that period.
Arguably, with such an elevated multiple (forward P/E of >80x) Crowdstrike needs to achieve this operating leverage and possibly find additional means to exceed it. The stock price is down over 30% since November on the back of the SaaSpocalypse. However, its agentic AI focus is just starting to generate benefits and success may see further programs announced.
Fiverr (NYSE: FVRR)
Fiverr is an example of how AI is both impacting as well as benefiting marketplace businesses. They are a smaller-cap platform using AI to rebuild its internal operating model around an “AI-native infrastructure and mindset.” The company operates a global freelance marketplace that connects businesses with independent professionals offering digital services across hundreds of categories, ranging from graphic design and programming to marketing and AI integration. Historically known for its “Gig” model, where tasks (gigs) were offered for a fixed starting price, the company is currently undergoing a massive strategic transformation in response to the rise of AI.
With the stock price down over 60% since mid 2025, the company recently disclosed a reduction of roughly 250 employees, or about 30% of staff. For a platform company of that size, the cut is meaningful and suggests management believes agentic tools can reduce coordination and execution overhead across functions such as support, trust and safety, internal operations, and engineering.
The core equity question is whether Fiverr can translate a leaner cost base into earnings expansion without impairing marketplace trust, reliability, or fraud controls. The company disclosures frame this as a transformation program rather than a single-quarter efficiency action, which implies that valuation upside depends on proving the platform can remain stable with far fewer internal human reviewers and operators.
SAP SE (XETR:SAP)
The trend of utilizing AI to justify large-scale reductions spans the entire enterprise software landscape right now. SAP, the German multinational software giant, announced significant layoffs as part of a strategic shift toward "Business AI." The company explicitly stated that automation would reduce internal staffing needs, resulting in the elimination of approximately 3,000 roles, though initial projections suggested up to 10,000 jobs could eventually be affected by this technological pivot.
SAP is focusing on "agentic governance" and intent-driven ERP systems, recognizing that as clients deploy hundreds of specialized AI agents, the software backend must evolve to orchestrate this digital workforce. SAP is described as moving toward “Business AI” and “agentic governance,” with layoffs of roughly 3,000 roles and the possibility that more could be affected over time as ERP systems evolve to orchestrate digital workforces.
The strategic takeaway is that not all software names will express the agentic dividend through disclosed layoffs. Some will express it through changes in what they sell and how they price it. Still, the broader message from the source material is clear: software vendors exposed to per-seat licensing are under pressure to pivot toward transaction, consumption, or outcome-linked pricing if their own products reduce user counts.
Financial Technology / Digital Payments
Block (NYSE: SQ)
Block is another of the most aggressive agentic AI implementations I can find in my research. The company describes its 2026 restructuring as explicitly AI-led, with more than 4,000 jobs cut, representing about 40% of the workforce. Management has tied the overhaul to an “intelligence-native” operating model in which agentic coding tools, automated financial operations, and daily internal AI usage allow the company to sustain or improve output with a radically smaller labor base.
From an equity perspective, Block is compelling because the labor cuts are explicitly connected to margin ambition. The company’s strategic target is the Rule of 40 by 2026, and the labor savings are expected to drive adjusted operating income margins higher in every quarter of 2026. The company reported Q4 2025 revenue of $6.25 billion and gross profit of approximately $2.9 billion, raised its 2026 gross profit target to roughly $12.2 billion, and expanded its share repurchase program by $5 billion, stoking a 20% surge in the share price. But again, Block is another case of a stock that appears to be forced into extreme action due to underperformance and AI distruption risk. Block’s share price fell 30% over 2025 in the “AI disruption sell-off” and is down over 70% since its 2021 peak. This experience has led the company to consider radical strategies. The result is a straightforward thesis: Block is attempting to use agentic AI to sever the historical link between engineering intensity and wages/payroll cost growth, with the savings dropping largely to the bottom line.
The operational evidence is notable so far. Management cited engineering tasks that now take a fraction of the prior time using agentic coding tools, and reported a 40% increase in production code shipped per engineer since September. In a fintech context, where engineering, risk operations, and support can dominate opex, that is precisely the type of productivity discontinuity that can support a durable margin re-rating if product velocity and control functions hold up.
The main risk is execution integrity. Block’s cuts are so deep that the market must determine whether the company has genuinely substituted labor with AI or simply front-loaded a radical efficiency campaign that could later require rehiring or control remediation. Even so, Block appears as one of the fastest and most aggressive “AI-first cost base reset” stories in public markets.
Klarna (Pre-IPO)
Klarna is not public yet, but it offers another clean example of AI-driven labor decoupling. The company’s internal AI agents are said to perform the equivalent work of 853 full-time staff, up from 700 earlier in 2025, while the actual workforce has nearly halved over three years from 5,527 employees in 2022 to 2,907 in late 2025. Unlike Block’s blunt-force layoffs, Klarna relied heavily on attrition and a prolonged hiring freeze.
The financial outcome is striking. According to reports, Klarna increased total revenues by 108% while holding operating costs flat, pushing revenue per employee to roughly $1.1 million. This is one of the strongest examples of agentic AI altering core unit economics rather than simply trimming labor. The fact that average compensation for remaining staff also rose materially suggests the company is using AI not just to cut labor, but to upgrade the mix toward fewer, more technical, more highly paid employees who orchestrate agentic systems.
For investors, Klarna is an example of the pure “agentic dividend”: flat opex, much higher revenue productivity, and a stronger pre-IPO valuation anchor. The obvious caveat is that pre-IPO companies face different disclosure standards and valuation incentives than public issuers, but as a case study in revenue-headcount decoupling, it is among the strongest in the dataset.
PayPal and Revolut
PayPal and Revolut are more thematic than central to this report, but they reinforce the broader fintech pattern. PayPal is described as having cut approximately 2,500 roles across 2024 and 2025 while deploying automation in fraud detection, risk management, and customer support. Revolut, without announcing mass layoffs, is framed as an agentic-scaling story: the ability to support more than 40 million customers across payments, crypto, and trading without a linear increase in compliance, support, or operating staff.
The implication is that fintech may be the sector where agentic AI translates most directly into margin opportunity because the labor pools are large, measurable, and deeply embedded in variable customer servicing costs and core systems are (usually) modern, integrated and therefore more readily interacted with by agents. That aligns with the broader observation that software and fintech show the strongest traction in converting AI into immediate bottom-line labor savings.
Banking / Financial Services
Lloyds Banking Group (LSE: LLOY)
Lloyds is one of the strongest banking examples because the company has attached explicit value targets to agentic deployment. Management designated 2026 as the year of enterprise-wide deployment for agentic AI and expects these systems to generate £100 million of additional value in 2026 after £50 million in 2025. It is, however, clear that within the highly regulated banking industry, companies are having to be more conservative with agentic labor reduction targets and the pace of depolyments than in software and fintech sectors. The regulatory, reputational and remediation cost of a botched agentic implementation could be huge. Hence these companies will have to move more methodically over longer timeframes. £100 million is large in magnitude but small in materiality to Lloyd’s bottom line (~1%). The operational disclosures of Lloyds however shows where the agentic focus is within banking: fraud investigations and complaint resolution at the current time, where AI acts as a semi-autonomous digital co-worker and allows the bank to reserve human labor for more nuanced escalations.
For a bank, the relevance is obvious: highly regulated support, operations, and control functions have historically required large pools of human analysts. Agentic AI is a means of increasing throughput in those functions without scaling headcount, which has direct implications for cost-income ratios and medium-term earnings leverage. Lloyds is therefore not just running a technology pilot; it is attempting to industrialize digital labor inside the most process-heavy parts of the bank. We should expect to see similar programs being outlined across the banking sector with the potential for meaningful cost to income ratio improvements, but over an extended horizon.
Intesa Sanpaolo (BIT: ISP)
Intesa is arguably the cleanest bank restructuring case I can find. The company disclosed 9,000 headcount exits by 2027, including 7,000 in Italy and 2,000 internationally, within a digitization and AI context. It also disclosed €500 million of annual personnel savings beginning in 2028 and around €350 million of net-of-tax charges booked earlier in the program. The €500 million euro target represents a 2 percentage point improvement in cost to income ratio.
What makes this especially interesting is the bridge from one-time cost to recurring savings. The company announcements highlight Intesa as a structural bank cost-out program anchored to AI and digitalization rather than vague efficiency programs.
IT Services / Consulting / Engineering
Accenture (NYSE: ACN)
Accenture is the most important consulting-sector case because it captures all sides of a very complex agentic transition for the IT consulting sector: Client AI related disintermediation of consultants and external engineers, internal consulting staff labor displacement and offsetting client-facing AI monetization mandates. The company has doubled specialized AI and data professionals to 77,000, trained more than half a million employees in generative AI basics, and generated $5.1 billion of generative AI bookings in 2025. At the same time, it cut 11,000 jobs in a single quarter and recorded approximately $865 million in restructuring costs.
The strategic logic is harsh but coherent. Consulting has historically depended on labor intensity and pyramidal staffing. Accenture is attempting to preserve relevance by moving away from manual consulting labor toward higher-margin AI architecture, data engineering, and agent orchestration. Management’s comment that some staff were exited on a compressed timeline because reskilling was not viable is especially telling: this is not merely a productivity enhancement program, but a change in the underlying skill mix required to generate revenue.
For equity holders, the question is whether Accenture can sustain bookings growth while compressing labor intensity fast enough to defend margins against broader industry disruption. The IT services sector is one of the industries most threatened by agentic AI because it undermines geographic labor arbitrage. In that context, Accenture’s restructuring looks less optional than preemptive.
Tata Consultancy Services (NSE: TCS)
TCS also presents less as a winner and more as a company facing structural pressure. TCS cut around 12,000 roles in 2025, roughly 2% of its workforce, citing automation and AI productivity programs. However at the same time, its revenue growth rate collapsed from 17% 3 years ago to 5% in 2025. The entire offshore IT export model is likely vulnerable because agentic AI bypasses geographic labor arbitrage. If enterprise customers can replace outsourced coding and support work with AI agents at the marginal cost of compute, the historic value proposition of offshore billable bodies compresses materially.
That does not mean TCS cannot adapt, but it means the “agentic dividend” here may be as much about defense, survival and business-model reinvention as straightforward margin upside. Relative to software and fintech, the consulting/BPO sector faces a more existential version of the labor reset.
AECOM (NYSE: ACM)
AECOM is interesting because it demonstrates AI adoption within the industrial and engineering sector. The company has also made the economics of AI unusually explicit for investors. Complex engineering tasks such as CAD drafting, structural optimization, predictive maintenance scheduling, and site planning are increasingly handled by autonomous workflows in AECOM. In response, the company raised its long-term margin target to an exit rate above 20% (from 17%) by fiscal 2028 and lifted expected adjusted EPS CAGR to above 15% for 2026 to 2029. Unfortunately this came with a material downgrade to revenue forecasts which saw the stock down 20% back at its November earnings reporting date.
An important detail regarding AECOM is the sensitivity of its earnings to labor costs in such a labor heavy industry. Analysis suggests each 1% of labor savings generated by AI adds roughly 20 basis points to operating margin at AECOM. That makes agentic AI a direct margin lever in an industry historically constrained by billable hours and human-intensive project delivery. Unlike some sectors where the savings thesis is still speculative, the engineering use case appears relatively intuitive: automate drafting and repetitive design work, keep high-value advisory labor, and expand margins without matching revenue growth with payroll growth.
Telecom / Hardware / Communications
HP Inc. (NYSE: HPQ)
HP is a broad “AI enablement” case rather than a narrow agentic example, but the numbers are material enough to warrant attention. The company disclosed a fiscal 2026 plan centered on AI adoption and enablement, including $1 billion of gross run-rate savings by FY2028, approximately $650 million of restructuring and related charges, and a gross global headcount reduction of 4,000 to 6,000 by the end of FY2028. That $1bn savings target represents 14% of the entire company’s non manufacturing costs.
The key analytical point is that this is a mature hardware company using AI as an internal productivity and cost-base reset tool. That makes the labor savings potentially meaningful because margin expansion is typically difficult in mature device businesses.
BT Group (LSE: BT.A)
BT is a high-scale telecom example with a staggering headcount reductiomn plan. The company disclosed plans to reduce labor resources by up to 35,000 out of ~110,000 employees (or over 30%) by 2030, with around 10,000 roles linked to AI, digitization, and automation. The operational focus includes customer call handling, diagnostics, and network management, all areas where agentic systems can automate investigation, routing, and remediation.
The materiality of this plan is obvious in magnitude but complicated in attribution. Research highlights that BT’s labor reductions are partly intertwined with other factors such as fiber and 5G rollout completion and contractor roll-off. Even so, the company popped up in my research because the absolute scale of targeted labor reduction is among the largest in UK public markets, and AI is clearly part of the stated efficiency logic.
KT Corporation (KRX: 030200)
KT’s “AICT” pivot is another example of telecom restructuring under an AI banner. The source material cites reassignment of about 1,700 employees into subsidiaries and a total headcount reduction of roughly 4,500. The company is attempting to reposition itself as an AI-enabled and IT-driven business rather than a conventional telco, which makes the labor program strategically important even if the margin outcomes are not yet fully disclosed.
Proximus Group (EBR: PROX)
Proximus appears as a smaller but explicit European telecom efficiency case. The company plans to eliminate 1,200 jobs by 2030, around 15% of the workforce, and reduce external workforce-related expenses by €25 million by 2028, with the source material attributing these improvements to AI-driven efficiency gains. This is not among the largest or cleanest cases, but it reinforces that telecom is becoming a meaningful vertical for agentic labor substitution.
Cross-Sectional Takeaways
This report is designed to surface details of how widespread agentic AI deplyments are becoming and the labor cost (and workforce) impacts they can potentially have across companies. There are a number of key takeaways.
First, it is striking how in so many of the discussed case studies, the most agressive agentic AI adopters at this early stage are companies facing singificant potential AI disruption and falling share prices. There is a clear observation here that these delpoyments are actually not coming from a position of strength but that of defense. Time will tell how that strategic catalyst, being different to typical internal R&D led offensive product strategies, translates into success or operational problems.
Second, across the researched companies, there are certain industries that appear to have larger opportunities from agentic AI than others. Some companies, mainly within software and fintech spaces, have taken a blunt force, “grab the whole opportunity” approach while others, particularly within more regulated industries like banking and financial services need to move slower and more methodically. Without doubt, the fastest and most aggressive “AI-first cost base reset” stories are currently Block and WiseTech, both because of scale and because management has explicitly tied the labor reset to a redesigned operating model. Both face material opportunities (margins and earnings) and risks (reputation, labor disputes, customer backlash and operational missteps). However their agentic AI deployments, being market leading, starkly highlight the trend amongst companies that has only just started and is expected to get louder.
A third observation is how agentic AI is reshaping workforces, remuneration structures and unit economics. Klarna’s example adds another facet to our understanding of agentic AI where they have used the technology so far to rebuild a workforce of fewer, more highly paid employees. They have translated higher revenue per employee to a more senior, flatter structure. We are seeing this trend now in Indeed job stats. Software engineering job ads are actually surging on the Indeed job platform since November 2025, however the composition of those ads are vastly different than before.
They are concentrated in senior AI engineering roles to allow companies to push ahead with the agentic deployments. That job ad trend is perhaps one of the best leading indicators suggesting that more and more announcements like those of Wisetech and Block are forthcoming across industries. Some companies have acted ahead and others are following their progress and learning from what they get right and wrong, but the expertise to deploy mass agents and restructure workforces is being strapped on right now globally.
Lastly, agentic AI implementations are leading to equally large opportunities for both small and large businesses. Fiverr, a relatively small business, is cutting 26% of its entire workforce while BT Group, a massive telco is cutting 30% of its headcount out to 2030 albeit a smaller 10% is disclosed as directly related to AI.
Andy West
The Inferential Investor
