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The enterprise AI landscape of 2025 presents a striking paradox: despite unprecedented investment and adoption, a significant majority of AI initiatives fail to deliver their promised value. A prevailing executive blind spot is the root cause of this crisis: a fundamental misunderstanding that treats AI as a standalone technology deployment rather than a holistic systems transformation. By delegating AI to tactical-level teams and focusing on technology procurement over strategic impact, leaders are inadvertently creating the conditions for failure, leading to abandoned projects, exorbitant costs, and lost competitive advantage.

There’s no question that large language models (LLMs) such as GPT-4o (and recently GPT-5), Claude, and Gemini have radically transformed how we work, communicate, and imagine the future of business. These powerful systems can draft legal briefs, write code, handle customer support tickets, analyze trends, and even synthesize entire reports, all in seconds. They promise an era of unprecedented automation, personalization, and efficiency.

But beneath the excitement lies a complex and increasingly pressing question: What is the environmental cost of this intelligence?

Algorithma whitepaper series
In 2025, frontier AI progress is hitting a plateau. Models like GPT-5, Llama 4, and Gemini 2.5 deliver impressive benchmark gains, but these are refinements of the transformer architecture, not breakthroughs. Scaling laws are yielding diminishing returns, with rising costs and data scarcity pushing the industry toward efficiency, specialization, and hybrid systems. Fundamental transformer limits in reasoning, long-context handling, and recursion point to the need for new architectures, from state space models to neuro-symbolic AI. The next leap will be architectural, and leaders who invest now in agentic AI, interoperability, and early adoption of emerging paradigms will be best positioned.

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. 

Many organizations have outgrown their Gen AI phase. The chatbots have been demoed. The copilots deployed. The value delivered was questionable, at best. Now the ambition is bigger: autonomous AI agents that can work alongside humans; taking action, owning tasks, and evolving as part of the team. But somewhere between the pilot and production, things fall apart.

This article is for teams already past the novelty curve. It draws on lessons from real-world agent deployments, and the structural, design, and governance failures that quietly derail them. These are systemic problems. If you want your digital colleagues to succeed, you’ll need to treat them less like tools, and more like teammates.

Physical security is no longer just about locked doors and patrolling guards. With AI-driven cameras, patrol robots, and autonomous access control, the modern perimeter now mirrors the complexity and risk profile of the data center. To achieve true end-to-end resilience, organizations must apply zero-trust principles-“never trust, always verify”-across every layer, from silicon to security guard contracts.

AI agents are now in production, but in many cases they’re not ready for the roles we’re giving them. They underperform. They hallucinate. Some invent refunds, cite fake laws, or answer with confidence when they should escalate. The usual fix? Fine-tune the model, rewrite the prompt, upgrade to the next release. Still not enough.

This isn’t a model problem. It’s a design problem. In this piece, we take a step back. What if we stopped treating models as tools, and applied thinking from how we recruit, train and develop our human colleagues. Not just smarter systems, but systems with structure. With protocols, responsibilities, and oversight. That’s where the shift begins.

Chief financial officers are increasingly confronted by the financial burden of new regulations. Each mandate, however well-intentioned, often triggers unplanned expenditures on system overhauls, process redesigns, and increased staffing – resources pulled directly from potential growth and innovation initiatives. This pressure impacts profitability and can even reduce revenue as companies wrestle with compliance complexities. A driver of these costs lies within the detailed, rule-bound, and context-heavy domain of governance, risk, and compliance (GRC). 

AI agents are becoming embedded in organizations as active digital colleagues: copilots, advisors, automation agents, and decision-makers. These agents take part in workflows, influence outcomes, and interact with humans across business domains without drawing salaries, seeking promotions, or needing emotional support.

This shift raises a clear question: if these agents are now part of the team, how do we ensure they are performing well, and how do we manage them effectively?

AI is automating the tasks that once formed the first step into working life. Entry-level roles, from data entry and scheduling to basic coding and document review, are disappearing as organizations pursue efficiency through automation. The bottom rung of the career ladder is no longer stable. This shift reshapes how people enter the workforce and progress within it. Without accessible starting points, those without established networks or prior experience are locked out. The anxiety around AI is not abstract. It reflects a structural shift that removes the foundational roles that careers are built on.

ESG reporting has moved from a compliance task to a central business function. With new regulations like the CSRD in force and investors demanding greater transparency, companies are expected to produce data that is accurate, auditable, and real-time. It’s not just about what you report, but how fast, how deep, and how defensible.

This is where AI agents come in. They don’t simply assist with ESG tasks. They operate within them. These agents gather data, detect anomalies, generate draft reports, flag compliance gaps, and even initiate follow-up actions. Unlike tools that need user input, agents act on their own. They are not dashboards. They are digital coworkers.

For decades, insurers have pushed for straight-through processing to cut cost and cycle time. Now AI agents that can parse damage photos, draft settlements, and auto-deny claims, we’re entering the STP endgame. The upside is obvious: speed, margin, and scale. But so are the new risks. In AI just broke your trust flow: humans are back into the loop, we argued that generative AI has broken the old trust flow, In claims, the handover between AI and human isn’t a technical issue; it’s a legal, ethical, and operational one, and it calls for a new playbook.

One of the most profound shifts AI introduces isn't just what can be automated, but the fundamental economics of how work scales. For generations, growing operational capacity primarily meant growing headcount, with costs scaling more or less linearly. AI agents operate under a radically different economic model, particularly concerning replication and operational expenses.

The most disruptive labor transformation in decades isn’t happening in HR. It’s happening in code. In barely six years the “brain size” of frontier language models has exploded while the meter to run them has gone into free‑fall. Meanwhile, the cost of letting that capacity loose on real work has collapsed: researchers find the per‑token price of GPT‑4‑level performance sinking by 40x each year, and OpenAI’s new GPT‑4.1 API launched in April at 75 % lower rates than last year’s offering.

Enterprise AI isn’t measured by how much math it can do. It’s measured by how much work it’s trusted to carry. Accuracy and latency tell us what a model could do. They don’t tell us what the organisation actually lets it do. The real milestone is when an AI agent takes on a step that used to require human oversight, and completes it end to end. That shift, its Span of Responsibility (SoR), is the best sign of AI maturity.

As AI keeps getting smarter, the old way of buying software by sticking to one vendor’s platform just doesn’t make sense anymore. When digital colleagues can handle tasks across different systems without a human lifting a finger, it’s the result that matters, not the logo on the app. Instead of thinking about which brand the AI comes from, it’s time to focus on how well it gets the job done. In this new world, the smartest move isn’t buying software that happens to have an AI agent, it’s picking the agent that actually delivers the best outcomes, no matter where it comes from.

Enterprise IT has historically revolved around standardization, consistency, and control. Teams were organized to manage platforms, enforce governance, and ensure uptime. However, the emergence of digital colleagues, agents capable of reasoning, acting, and collaborating, requires a significant shift. The IT management team must reimagine their roles, moving from custodians of technology to strategic designers of intelligent ecosystems.

As digital colleagues join the workforce, the platform-first model of enterprise IT is giving way to a protocol-based architecture that enables intelligence, agility, and coordination across human-AI teams.

Enterprise IT was built around the idea of standardisation. To scale efficiently, organisations invested in platforms that could centralise data, enforce process discipline, and act as the single source of truth. This logic shaped the rise of ERP, CRM and workflow tools - and created a generation of transformation programmes focused on digitising the past.

But that model is starting to collapse.

You were promised automation. Slick digital workflows that handle expense claims, insurance reports, onboarding, whatever, all without human friction. Upload a photo. Scan a receipt. Auto-approve. Done. But then AI happened. Not the helpful kind that suggests headlines or organizes your calendar. No, the kind that forges receipts so well your finance system says “looks legit.” The kind that adds fake dents to cars, generates x-rays of non-existent fractures, and drafts medical notes that never came from a doctor.

Building an on-premise AI infrastructure is an important task that requires careful planning, investing in the right technology, and following best practices. Unlike cloud-based solutions, an on-premise setup gives you more control over your data, better security, and the ability to customize the system to meet your specific needs. However, it also requires technical knowledge, resources, and regular maintenance to work effectively.