Enterprise software is dead(ish) - time to move on

Setting the directionBuilding algorithmic solutionsBuilding the foundation
Written By Viktor Ekberg

Written by Jens Eriksvik & Viktor Ekberg

Enterprise software systems were built for a different era - one where businesses operated on fixed processes, structured data, and centralized control. That world no longer exists. Today, companies need real-time adaptability, work with fragmented and unstructured data, and demand flexibility that traditional systems can’t provide.

  • Traditional enterprise software is outdated, expensive, and slow. It struggles to keep up with real-time data, adaptability, and modern business demands. Maintaining these legacy systems costs millions, takes years to implement, and still fails to meet the needs of today’s fast-moving enterprises.

    Enter AI Agents

    AI-driven digital colleagues are faster, smarter, and more flexible than old-school software. They:

    • Automate workflows without rigid integrations

    • Adapt to changing data and business conditions in real time

    • Reduce reliance on costly system integrators and maintenance

    • Scale instantly without the pain of traditional software upgrades

    Why This Matters

    Enterprise software vendors - and the system integrators that profit from their complexity - are facing an existential threat. Businesses that hold onto legacy tech will fall behind as AI-driven workflows outpace them. AI isn’t just an upgrade - it’s a fundamental shift in how enterprises operate.

    The Future of Enterprise IT

    Companies need to ditch the dead weight of enterprise software and embrace AI-powered, decentralized systems. Those who act now will lead the next era of business efficiency. Those who don’t will be stuck in the past.

    Ready to rethink your strategy?

The cost of implementing and maintaining legacy systems is staggering. These software implementations take years, cost millions, and require constant customization and integration. Even then, they struggle to keep up with business complexity. An entire industry of system integrators thrives on this inefficiency, profiting from the need to configure, deploy, and maintain traditional software ecosystems.

Now AI agents, i.e. digital colleagues that are smart, fast and problem-solving, are reshaping the modern workplace. Unlike rigid systems, AI agents work alongside humans, automating workflows, understanding data on the fly, and coordinating business operations dynamically. They don’t rely on static integrations or predefined processes. They adapt, solve problems, and act as an extension of the workforce.

This shift doesn’t just threaten software giants - it challenges the entire system integration business that depends on complexity. As AI-driven digital colleagues take over core enterprise functions, the need for traditional systems - and the businesses built around them - has no place in a modern enterprise.

"Recent analyses indicate that legacy enterprise systems are increasingly challenged by the demands of real-time, unstructured data environments. At Algorithma, our experience shows that AI-driven ‘digital colleagues’ can offer more agile, adaptive, and cost-effective solutions for modern enterprise operations."

 - Jens Eriksvik, CEO Algorithma

The crumbling foundations of traditional enterprise software

For decades,  software giants have dominated enterprise IT with systems designed to bring structure and control to large organizations. Their value proposition was clear: centralize operations, enforce standardization, and integrate business processes into a single system of record. These strengths have now become liabilities in a business environment that demands speed, adaptability, and real-time decision-making.

The legacy giants at risk

Software vendors built their business on the idea that enterprises need a single, all-in-one platform to manage operations. This worked when IT was costly, data was structured, and workflows were stable. That era is over, but has left deep marks in large enterprises: 

  • Rigid architectures: Enterprise software rely on predefined data models that struggle to keep up with changing business needs.

  • Slow, costly implementations: Deployments take years, cost millions, and often bring ongoing integration headaches.

  • Built-in inefficiencies: System integrators profit from complexity, turning maintenance into a permanent revenue stream instead of a solved problem.

First, generative AI changed the game. Now, AI-driven agents are emerging as an alternative. Instead of forcing businesses into rigid systems, AI agents interact dynamically with data, automate workflows without complex integrations, and adapt in real time. Simply put, AI agents enable cheaper, more flexible, and less integrated IT - without the baggage of enterprise software.

The limitations of the traditional enterprise software models

Many enterprise systems were built for a world of structured data, centralized control, and fixed workflows. Today, businesses operate in real time, across fragmented data, with constantly shifting priorities. Traditional systems are struggling to keep up:

  • Changes require months of reconfiguration, costly upgrades, and retraining.

  • They fail to unify the unstructured and scattered data enterprises rely on.

  • High costs, low efficiency – long lifecycles and expensive contracts keep businesses stuck with outdated tech while competitors move faster.

  • Workflows rely on predefined inputs and approvals, limiting agility in a world where AI can act instantly.

AI agents break the limitations of static enterprise software without rigid integrations. As companies realize their enterprise systems are holding them back, these vendors and their ecosystem of system integrators face an existential threat. AI agents aren’t an upgrade - they’re a fundamental shift in how enterprises organize and operate.

"Enterprise software companies compete for $35B in IT budgets, but the real opportunity is in the $4.6T workforce spend. CFOs scrutinize a $100K software investment but approve million-dollar headcount budgets without hesitation - because people deliver complete business functions. AI-driven Systems of Agents do the same, automating execution end-to-end. This isn’t just about replacing ERP - it’s about redefining how work gets done." - Joanne Chen, General Partner at Foundation Capital

“SaaS is dead” - Satya Nadella, CEO of MIcrosoft

Enter the AI-driven agent architecture

Defining the new digital colleagues

AI agents are autonomous, context-aware digital colleagues that manage workflows, make decisions, and optimize operations in real time. Unlike traditional automation, which follows static rules and scripts, AI agents continuously learn, adapt, and collaborate with humans and systems. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously, and Deloitte predicts that 25% of companies that use gen AI will launch agentic AI pilots in 2025 or growing to 50% in 2027. 

They operate within existing enterprise environments, pulling data from multiple sources, identifying patterns, and taking action dynamically. Whether managing supply chains, customer service, handling financial transactions, or optimizing HR, AI agents execute tasks faster and more accurately than humans or legacy systems.

Cost of labor and operational efficiency

The cost savings of AI-driven agent architectures go beyond reducing headcount- they redefine enterprise productivity:

  • AI agents replace expensive manual processing, cutting labor costs while increasing speed and accuracy.

  • Unlike human workers, AI agents operate 24/7 without fatigue, eliminating downtime and delays, and enabling human workers to focus their time where it matters most.

  • Instead of hiring and training new employees, enterprises can scale AI agents instantly, adjusting workloads based on demand.

  • Traditional enterprise systems require constant integration, updates, and maintenance. AI agents self-optimize and interact dynamically, reducing the need for costly IT interventions.

Data unification and intelligent orchestration

One of these systems’ biggest failures is its inability to effectively unify data across a fragmented IT landscape. AI agents solve this problem without requiring enterprises to overhaul their systems:

  • AI agents access, interpret, and standardize data across diverse sources, including cloud apps, legacy databases, and third-party APIs, without needing rigid integrations.

  • Instead of relying on predefined business rules, AI agents analyze live data streams and make real-time, autonomous decisions.

  • AI agents consolidate and structure enterprise data dynamically, eliminating the need for time-consuming manual reporting processes.

  • AI agents adapt to business conditions in real time, constantly optimizing workflows in ways legacy enterprise systems never could.

Case Study 1: Digital customer service transformation

A large multinational consumer goods company faced increasing customer service costs and slow response times due to reliance on legacy systems and scripted chatbots. To address these challenges, they introduced an AI agent - a digital colleague - to handle routine inquiries in real time with context-aware responses. For complex or sensitive issues, the AI agent seamlessly escalated cases to human agents.

Key Outcomes:

  • 40% reduction in response times, improving customer experience.

  • Lower operational costs by automating routine inquiries.

  • Enhanced customer satisfaction through faster, more accurate responses.

  • Continuous learning and improvement, reducing manual intervention over time.

  • Seamless human-AI collaboration, ensuring efficient issue resolution.

Building the AI agent workplace: Algorithma’s framework

Traditional enterprise software is just a tool, something humans operate and control. AI agents change that. They aren’t just executing tasks; they are active team members. But like human employees, AI agents need a structured workplace to function effectively.

A workplace isn’t just a set of tools - it’s an environment for seamless collaboration, communication, and execution. AI agents need the same. Algorithma’s AI Agent Platform provides this foundation, offering scalable infrastructure that lets AI agents operate within enterprise systems, access data, and scale as needed. A robust orchestration layer ensures AI agents don’t work in isolation - they interact fluidly with human colleagues, external systems, and other AI agents.

As AI agents take on more responsibility, businesses must shift their mindset. These aren’t disconnected automation tools - they are becoming an integral part of digital operations. Companies that build structured AI work environments now will gain an edge in efficiency, decision-making, and scalability. Those that wait will be stuck with fragmented processes and rigid workflows while competitors refine their AI-driven operations.

Reshaping your enterprise software program with AI agents

Enterprise software systems were designed to standardize and centralize business operations, but this rigidity has become a liability. AI-driven agents offer a fundamentally different approach - one that moves away from monolithic systems toward dynamic, adaptive, and decentralized enterprise operations. By integrating AI agents into your strategy, you can break free from complex, slow-moving implementations and create a more agile, intelligent enterprise.

"Enterprise ERP systems are evolving rapidly, with AI becoming a core part of daily operations in 2025. We’ve moved past the experimental phase into practical use, where AI will manage entire workflows within ERP and supply chain systems." 

- Robert Kramer, Forbes

Transitioning from a software-driven to an AI-driven architecture

AI agents don’t replace these systems overnight, but they gradually take over critical functions, reducing reliance on rigid workflows and costly integrations. Instead of forcing businesses to adapt to limitations, AI agents adapt to the business.

  • Traditional workflows are static, requiring manual updates. AI agents monitor, adjust, and optimize processes on the fly, whether managing procurement, financial transactions, or supply chain logistics. Instead of following rigid approvals and workflows, AI agents prioritize tasks, escalate issues, and trigger actions based on real-time data.

  • AI agents gradually take over key functions, such as order management, financial forecasting, inventory optimization, and HR. Unlike system modules that depend on structured inputs, AI agents understand natural language, analyze live data, and integrate with multiple systems - without complex middleware.

  • Enterprise software programs require heavy IT projects to connect applications. AI agents work across systems without costly integrations, pulling and standardizing data from legacy systems, cloud platforms, third-party applications, and unstructured sources - removing the need for long data migrations and expensive API development.

  • AI agents deliver real-time insights, detect anomalies, and recommend actions before issues escalate. Instead of static reports and dashboards, decision-makers can ask AI agents direct questions and get intelligent, context-aware responses.

  • Expanding legacy systems means more users, more licenses, more infrastructure, adding cost and complexity. AI agents scale dynamically, handling increased workloads without extra customizations or upgrades. They distribute tasks, allocate resources, and ensure operational continuity as the business grows.

AI agents don’t just optimize enterprise software - they render it obsolete by replacing fragmented, expensive processes with dynamic, scalable intelligence.

Case Study 2: Streamlining supply chain risk management

An international manufacturing firm struggled with managing supplier data and staying compliant with evolving regulations. To improve efficiency, they implemented an AI-powered risk management system with specialized AI agents handling different aspects of risk assessment. One agent gathered and structured supplier data, another pre-analyzed risks and categorized them, while a quality assurance agent flagged anomalies for human review. This AI-driven approach streamlined risk assessments and improved data accuracy.

Key Outcomes:

  • Faster risk assessments, reducing delays in supplier evaluations.

  • Improved data accuracy through AI-driven structuring and analysis.

  • Greater transparency across the supplier network.

  • Proactive risk management, helping avoid potential disruptions.

  • Optimized human intervention, focusing efforts only where necessary.

A new strategy: from system-centric to agent-driven

Reshaping your software programs with AI agents means shifting from a system-centric approach to an agent-driven strategy. Instead of any system being a single source of truth, AI agents become the intelligence layer that connects, orchestrates, and optimizes enterprise operations. Businesses no longer need to be trapped in decade-long system upgrade cycles or programs - AI agents enable continuous improvement, faster deployment, and greater adaptability.

“Organizations that adopt AI-driven strategies today will not only reduce costs and complexity but also gain a competitive edge. The future of enterprise operations isn’t another enterprise software upgrade or program - it’s a fully AI-augmented business, where digital colleagues drive innovation and efficiency. “

- Viktor Ekberg, Partner Algorithma

The vision for what comes next

A decentralized AI ecosystem

The future of enterprise operations is shifting from rigid, centralized systems to a decentralized network of AI agents. Instead of a single system managing all processes, modular AI-driven services will dynamically coordinate business functions. This approach moves away from monolithic software toward plug-and-play AI capabilities that enterprises can integrate and scale as needed.

As AI agents become more advanced, they will interact with different data sources, vendors, and platforms - removing the need for predefined system integrations. This shift opens the door for an AI marketplace, where businesses can select specialized AI agents for finance, logistics, HR, and other functions, mixing and matching capabilities without being locked into a single vendor ecosystem (more on this later from the Algorithma team to follow).

Radical shift in enterprise IT spending

AI-driven ecosystems fundamentally change IT budgeting. Instead of massive investments in software programs, enterprises will allocate IT spending toward pay-as-you-go AI services. This transition reduces expensive multi-year implementations and maintenance contracts, reallocating resources toward continuous innovation in AI and automation, and ultimately shareholder value. 

Enterprises are already moving away from static licensing models toward AI services. As AI agents prove their ability to outperform traditional functions, businesses will stop investing in customization and integration, freeing up budgets for AI-driven operational intelligence and automation.

Counter-arguments and survival strategies

Hybrid AI models

Software vendors won’t go down without a fight. Major players will embed AI features into their existing platforms, offering hybrid solutions that combine software structures with AI-driven automation. While this approach may provide a smoother transition, it does not address the fundamental limitations of these systems  - high maintenance costs, rigid data models, and slow adaptability.

For enterprises considering AI-augmentation, the key question is: will adding AI to a legacy system improve agility, or will it just make a slow system slightly faster? AI agents work best when unrestricted by constraints, meaning hybrid models may delay transformation rather than drive it.

Enterprise resistance to change

Large enterprises are deeply invested and locked-in. They have spent years standardizing processes, training employees, and building integrations around these systems. Moving to an AI-driven approach isn’t just a technical decision - it’s a cultural shift. Common barriers include:

  • Companies hesitate to abandon systems they’ve invested millions in.

  • Enterprise software may be inefficient, but it’s familiar. AI-driven models require a new way of working.

  • Replacing systems with AI agents means rethinking roles, workflows, and decision-making structures.

While resistance is natural, the economic pressure to adapt will eventually override hesitation. Enterprises that hold onto legacy for too long will face higher costs, slower decision-making, and reduced competitiveness compared to those embracing AI.

Regulatory and compliance factors

AI-driven operations raise new challenges in compliance, security, and governance. Industries with strict regulations - like finance, healthcare, and government - may be slower to abandon the legacy approach (e.g. consider the industries where mainframes are flourishing), as these systems provide structured audit trails and compliance frameworks.

However, this advantage is eroding. AI-driven enterprise systems are evolving to include explainability, auditability, and security features that meet regulatory requirements. The reality is that compliance does not require enterprise systems - it requires well-managed, secure data, something AI agents can deliver without the overhead of an aging platform.

The future of enterprise operations: from enterprise software to AI-driven agility

The shift is unavoidable. Market forces, cost pressure, and advancing technology are pushing enterprises toward AI-driven operations. Software vendors can add AI, but they can’t escape their foundations and legacy.

As AI solutions prove faster, more adaptable, and cost-efficient, businesses will gradually reduce their dependence - first with AI-driven enhancements, then moving toward fully decentralized AI ecosystems. These softwares won’t vanish overnight, but it will shrink, no longer the backbone of enterprise operations.

Many enterprise software  systems were built for a different era. Their rigid structures, high costs, and slow adaptability no longer fit today’s business demands. AI-driven agents offer a more flexible, dynamic alternative.

This isn’t just about improving automation - it’s about redesigning enterprise operations from the ground up. AI agents eliminate complexity, drive continuous optimization, and make decisions in real time - without the baggage of legacy integrations.

So, how do businesses move forward?

  1. Start small, scale fast - Identify high-friction workflows and introduce AI-driven agents where they create immediate value.

  2. Prioritize interoperability – AI agents should work across existing systems, not require rip-and-replace overhauls.

  3. Reframe IT investments – Shift from long-term software projects to agile AI adoption, where solutions evolve with business needs.

  4. Build AI-native governance – AI-driven enterprises need frameworks for transparency, compliance, and risk management, ensuring AI agents operate responsibly and effectively.

The choice for enterprises is clear: stay locked in costly, rigid software platforms or build a dynamic, AI-driven enterprise that adapts in real time. Those who act now will define the future. Those who hesitate will find themselves maintaining outdated systems while the competition moves ahead.

Viktor Ekberg
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