Designing the AI‑native enterprise, part 2: Leveraging AI agents to offset increasing cost of doing business
Author: Jens Eriksvik, Felix Baart
Margins are under assault. Regulatory pressure, talent scarcity, capital costs, tariffs, geopolitical tensions, and macroeconomic volatility are driving the cost of doing business relentlessly upward. Traditional responses, adding headcount, buying more SaaS, outsourcing, or patching with chatbots, no longer deliver durable relief. Enterprises need a new approach to break the spiral of increasing cost of doing business.
In our earlier articles Designing the AI-native enterprise, part 1 [1], and The new economics of scale [2] we explored how protocols and digital colleagues reshape workflows and create new ways of working. This follow-up moves beyond architecture to show how agentic AI can structurally break the cost spiral, resetting cost curves, unlocking economic advantage, and offering leaders a concrete playbook to scale.
Recent global analyses highlight how enterprises face not just incremental pressures but structural shifts: from the regulatory paradox of innovation to digital debt, from the friction tax on customer loyalty to the rising cost of resilience in supply chains. This piece shows how agentic AI can address these root causes, not through shallow automation, but through deep structural redesign.
For executives looking to reset their enterprise cost base and build resilience, this piece offers a pragmatic roadmap grounded in field evidence, financial models, and design principles.
“The opportunity of agentic AI is not marginal cost savings, it’s breaking the structural cost spiral. By shifting from people-per-process to capacity-per-agent economics, we unlock elastic scale, resilience, and nonlinear advantage in a way no traditional cost lever can match.”
Jens Eriksvik, Algorithma
Cost of doing business is spiraling, driven by multifaceted factors
Enterprises today face a convergence of pressures reshaping their cost structures. Five core forces are driving a relentless rise in OPEX:
Escalating compliance complexity: Regulatory demands have intensified across regions and domains; from DORA, the EU AI Act, and ESG mandates to sector-specific standards. Some studies indicated that the average large enterprise now spends nearly 2 BUSD annually on compliance, or up to a stunning 25 % of revenue [3], with 85% of leaders reporting growing complexity [4]. Manual processes, siloed GRC tools, and reactive audits leave organizations exposed to compliance lag, regulatory fines, and reputational damage [4]. Moreover, technologies meant to help, like AI, introduce paradoxical new risks, including data privacy obligations (e.g., GDPR), environmental impacts (e.g., energy use of large models), and shadow IT risks from low-code/no-code platforms, amplifying governance burdens [5][6]. This creates what some call the “regulatory paradox of innovation”, where AI both alleviates and generates new compliance costs.
Compounding data fragmentation and digital debt: Legacy systems, designed for simpler datasets, are buckling under modern data volumes, varieties, and velocities. Fragmented data landscapes create redundancies, slow queries, inflate storage costs, and undermine decision accuracy [7]. Poorly integrated infrastructures increase cybersecurity risks and erode compliance readiness [5]. Legacy tools struggle to integrate with IoT, cloud platforms, or AI engines, and even physical data center infrastructure is aging into obsolescence, raising operational costs [6][7]. This is not mere IT inefficiency, it’s an accumulating layer of “digital debt” that requires continuous capital reinvestment while constraining innovation [6].
Persistent talent scarcity and rising labor costs: Despite slight softening in salary growth forecasts (3,7% for 2025 vs. 3,8% in 2024) [8], talent markets remain tight, with 36% of companies struggling to attract and retain workers [8], and 63% identifying skill gaps as the biggest barrier to transformation [9]. Nearly 40% of today’s workforce skill sets are projected to become obsolete by 2030, with 59% of workers needing reskilling [9]. Outsourcing, a traditional cost lever, increasingly results in performance and oversight challenges due to inadequate training and technical gaps [Example here in accounting, 10]. Labor costs today are no longer just wages, they reflect the price of maintaining, developing, and aligning human capital to evolving business needs [8][9].
Demand for immediate, frictionless customer experiences: The “price of immediacy”, first studied in capital markets [12], now defines the consumer economy. Customers expect seamless, personalized, omnichannel interactions, forcing companies to invest heavily in digital and physical infrastructure [11]. Shallow automation (e.g., chatbots, RPA) frequently creates customer frustration when systems fail to understand nuance or resolve complex issues [16][18]. Disconnected touchpoints force customers to repeat themselves, imposing a “friction tax” on loyalty: companies with weak omnichannel strategies retain only approx. 33% of customers, compared to approx. 89% for those with strong execution [16][19][20].
Geopolitical volatility and resilience premium: Trade barriers, tariffs, and geopolitical shocks are dismantling the least-cost global supply chain model. Tariffs of 50 to 145% in sectors like electronics and appliances [14][15], financial system fragmentation estimated to cost 600 to 5 700 BUSD globally [13], and persistent logistical disruptions (e.g., port congestion, container shortages) are raising costs across sourcing, production, and distribution [14][15]. Companies are now investing in reshoring, nearshoring, multi-sourcing, and inventory buffering, resilience strategies that carry unavoidable premiums [21].
Conventional mitigation strategies, adding headcount, layering on compliance tooling, consolidating platforms, outsourcing, patching workflows with automation, or shifting supply chains reactively, were designed for a more stable era. In today’s landscape, they deliver diminishing returns and sometimes amplify fragility.
Organizations fall into patterns of:
Compliance lag: chasing evolving rules but never gaining foresight [17].
Digital debt: accumulating complexity faster than it can be paid down [4][6].
Human capital misalignment: spending more without closing skill gaps [8][9].
Friction taxes: automating surface interactions but failing on experience depth [16][19][20].
Optimized fragility: creating brittle systems optimized for cost, not resilience [14][15].
Together, these forces form a structural cost spiral, one that demands not incremental fixes, but systemic redesign.
Breaking the spiral with agentic scale
Agentic AI changes enterprises from people-per-process to capacity-per-agent economics, a structural change, not just an automation layer. Traditional operating models rely on fixed labor, rigid systems, and SaaS-per-seat cost curves. However, agentic AI models create elastic capacity [21]: digital colleagues that scale at near-zero marginal cost, work across tools and data, and reshape how work and cost interact. Algorithma’s field evidence shows 5 to 10x cheaper run-rates when tasks migrate to agentic execution, avoiding SaaS mark-ups and flattening cost curves as scale increases.
This change is grounded on three interlocking foundations developed in Algorithma’s thinking:
First, the architectural shift from platforms to protocols [22]. Earlier, we have argued that enterprise SaaS logic is collapsing; AI agents don’t live inside CRM or ERP systems, they act across them. Lightweight protocols like MCP replace heavy integrations, making it possible for agents to traverse silos, orchestrate workflows, and compose outcomes dynamically.
Second, the economic shift from buying tools to owning capacity [23]. Instead of expanding SaaS footprints or adding brittle automations, organizations move to interaction-driven environments where they host and govern their own agent layer. This removes per-seat mark-ups, shifts cost curves away from linear scaling, and creates nonlinear economic leverage.
Third, the organizational shift from treating automation as IT to treating agents as part of the workforce [24]. Digital colleagues are not endpoints or embedded features, they are governed participants with defined scope, feedback loops, and escalation paths. This framing enables span-of-responsibility metrics, embedded compliance, and sustainable, accountable deployment at scale.
Together, these changes directly address the systemic cost pressures identified:
Compliance lag is reduced by embedding auditability and explainability.
Digital debt is cut by bypassing brittle system lock-ins.
Human capital misalignment is eased by pairing elastic agents with skilled teams.
Friction tax on customers is minimized through context-aware, seamless interactions.
Optimized fragility is replaced by adaptive resilience at the capacity layer.
In short, agentic AI scale is not an incremental fix, it is a structural redesign of how enterprises organize capacity, cost, and resilience.
Design principles for an AI‑Native cost structure
Building on earlier Algorithma thinking, we outline five enterprise-level design moves that enable agentic scale to translate into durable cost advantage. These are not isolated tactics, they are interdependent principles that reshape how work, cost, and resilience are structured.
-
DescriptioInstead of stitching agents into legacy applications, use message-based orchestration protocols (such as MCP) that let agents operate across silos without heavy integrations. This shift is critical because agents thrive in distributed, dynamic environments, where they can:
Pull context across tools,
Act without prebuilt flows,
And coordinate with humans and systems in real time.
Protocols don’t replace platforms; they liberate work from platform lock-in, turning brittle systems into flexible enablers.n text goes here
-
Description tTraditional labor, human or machine, incurs fixed costs whether fully utilized or not. In an AI-native model, agents spin up on demand and disappear when no longer needed. There’s no paid bench time, no sunk cost; just elastic, task-matched capacity.
This enables organizations to move away from optimizing headcount or license pools and toward designing for elastic throughput at the task layer.ext goes here
-
Compliance is no longer a post-hoc check or an added system overlay. In an agentic AI-model, explainability, audit trails, and human-in-the-loop checkpoints are built into every workflow. This isn’t just about avoiding fines or managing risk; it’s about creating trustworthy, scalable digital colleagues that:
Can justify decisions,
Hand off when uncertain,
And integrate seamlessly into regulated environments.
Embedded compliance turns governance from overhead into competitive advantage.
-
Traditional automation metrics focus on hours saved or FTE reduction. In agentic AI-operations, a better metric is span of responsibility:
How much workload can an agent autonomously own?
How many domains can it cover?
How much complexity can it handle safely?
Tracking span of responsibility reframes success from efficiency alone to capacity expansion, resilience, and adaptive scale.
-
Finally, pricing models must evolve alongside operating models. Agentic AI-economics are capability-based, not time-based. Fees tied to hours or seat counts misalign incentives and underestimate value. Progressive organizations are experimenting with:
Outcome-indexed pricing,
Consumption-based models,
Or shared-savings contracts tied to agent-delivered results.
The principle is simple: as agentic scale creates nonlinear value, pricing must reflect elastic, not linear, economics.
These moves are practical. Together, they define what it means to build an AI-native cost structure: one that breaks the cost spiral, expands capacity, embeds trust, and aligns economics with adaptive, agentic work.
Where agentic AI delivers: Patterns in practice
Agentic AI delivers measurable impact across critical enterprise functions, not through isolated automation but by addressing core structural pressures. In finance, reconciliation agents reduce compliance lag by embedding auditability and explainability into every transaction. Continuous controls monitor flag anomalies in real time, moving risk management beyond quarterly sampling. In analytics, on-demand data agents bypass digital debt by pulling insights across siloed systems without heavy integrations, producing board-level reports overnight.
In workforce alignment, elastic agent capacity complements skilled teams, easing human capital misalignment by letting people focus on judgment and exception handling. In customer operations, agentic triage layers reduce the friction tax by resolving routine queries seamlessly, as shown in our work with Byggmax. In treasury, liquidity optimizers replace optimized fragility with adaptive resilience, dynamically managing cash-in-transit buffers and freeing 2 to 4% of working capital.
These patterns are field-tested use cases that show how agentic AI reshapes cost, capacity, and resilience. Together, these examples illustrate that agentic scale is not about marginal gains; it’s about systemic redesign.
Operating-model blueprint: Starting small, scaling right
Breaking the cost spiral with agentic AI requires new organizational thinking. But it doesn’t start with councils or factories; it starts with one thing: a forward-leaning sponsor with the leadership courage to open the door for a digital workforce.
In every successful adoption we’ve seen, an executive or business owner steps up to champion a targeted pilot, typically in one or two cost-intensive areas, like finance reconciliation or customer service,, where elastic agent capacity can deliver visible results within months. Early on, governance stays light: a small, cross-functional team defines agent scope, monitors outcomes, and works directly with business owners to tune behavior in context.
As momentum builds, organizational maturity evolves. E.g. something akin to a Digital Workforce Council begins to take shape to oversee the growing agent portfolio, define risk limits, and align deployments to strategic goals. Delivery-wise, an AI agent factory emerges to industrialize delivery, combining MLOps and PromptOps practices to ensure robustness, safety, and adaptability. And perhaps more importantly, business-embedded trainers, working side by side with frontline teams, accelerate tuning and trust-building (we call this the “gladiator” methodology, while a hybrid talent model balances a lean internal AI team with flexible partner capacity.
This phased approach transforms agentic AI from one-off experiments into an adaptive operating model, where we systematically address compliance lag, digital debt, human capital constraints, customer friction, and resilience challenges. The result isn’t just cost savings; it’s a fundamentally new way of organizing capacity, resilience, and competitive advantage.
Economic model: Where the cost advantage comes from
Agentic AI transforms the underlying cost infrastructure of the enterprise. Traditional operating models tie capacity directly to headcount and seat-based SaaS fees, scaling costs linearly with growth. Agentic models break this linkage. Instead of “people per process,” they operate on capacity per agent, where the key costs are base cloud infrastructure and LLM token consumption, both of which scale elastically.
This enables the emergence of a digital workforce:
Agents that can scale up or down on demand, without fixed salaries or license fees.
Near-zero marginal cost for additional workload once deployed.
Declining unit costs as LLM token prices drop (low estimate over 30% YoY) and as agents expand their span of responsibility, owning broader, more complex task bundles.
Capacity substitution, not just automation: agents don’t just make humans faster; they replace entire categories of work (e.g., continuous controls, reconciliations, first-line triage) that would otherwise require new hires or vendors.
Break-even points typically come under 4 months for functions, and the returns improve over time. As the digital workforce grows, enterprises unlock compounding gains: agents trained in one domain often extend/get promoted to adjacent areas, and the underlying cloud/LLM ecosystem improves in both cost and performance.
The result is a rethink in how to manage capacity and resilience. The digital workforce becomes a new economic layer, sitting alongside human teams, absorbing variable workload, and reshaping the OPEX curve from brittle and linear to elastic and adaptive.
“The real breakthrough is that agentic AI lets organizations embed compliance, auditability, and explainability directly into operations, turning regulatory cost pressures into drivers of resilience and trust. It’s a shift from cost as burden to cost as opportunity.”
Felix Baart, Algorithma
Action roadmap for CXOs: from pilot to scale
Agentic AI scale doesn’t require years, it needs focused leadership and weeks-to-months execution. Based on Algorithma’s field approach, the roadmap looks like this:
1–3 weeks: AI inception
Align executive sponsorship and business leadership on the agentic AI vision.
Identify 1 to 2 high-impact, feasible use cases (e.g., reconciliation, customer service, controls monitoring).
Build shared understanding of value potential, readiness, and governance basics.
6–9 weeks: First agent project
Stand up the first agent in production, using lean governance and fast iteration.
Deliver early value, e.g., automate 70% of manual touchpoints, reduce error rates to less than 10%.
Capture learnings and tune agent behavior in live context, guided by business owners.
Post-9 weeks: Scale as per priority
Expand into additional domains: AI assistants, analytics, treasury, risk.
Grow the digital workforce while evolving light governance (span-of-responsibility KPIs, agent portfolio management).
Build hybrid talent and scaling practices, combining internal teams and partner capacity.
Elastic labor beats fixed labor
In part 1 [1], we explored how digital colleagues transform work and reshape enterprise architecture. Here, we show they also transform cost, not through marginal savings, but by changing the financial architecture and resilience of the enterprise itself.
In a world of escalating compliance demands, mounting digital debt, human capital misalignment, friction taxes, and broken supply chains, agentic AI delivers something deeper than efficiency: it delivers adaptability. It replaces fragile, optimized-for-the-past models with elastic, scalable resilience.
The last barrier is no longer technology, it is leadership will. How fast can we redesign work, governance, and economic models to capture the advantage? Those who move first won’t just free capital for growth, they’ll build the adaptive muscle needed to survive and thrive in a volatile world.