Enterprise IT was built for standardization - digital colleagues make that obsolete
Written by Jens Eriksvik & Alexander Ekdahl
The rules of enterprise software are being rewritten. For decades, the strategy was clear: standardize processes to cut costs and streamline operations, paving the way for ERP, CRM, and other rigid systems to dominate. These legacy systems belong to an era of fixed processes and centralized control - a model designed for uniformity and efficiency that ultimately locked businesses into inflexible structures.
As we discussed in Enterprise software is dead(ish) - time to move on, today's businesses demand real-time adaptability and the ability to handle fragmented, unstructured data - capabilities traditional enterprise systems cannot provide. Agentic AI changes this. Unlike outdated models, these AI agents thrive in dynamic environments.
“AI agents act as independent problem solvers. They manage tasks, coordinate with one another, and work alongside human teams to bridge gaps in processes, workflows, and systems. The focus has started to shift from optimizing for fewer people to leveraging as many AI agents as possible - each serving as a dynamic member of the workforce.”
- Alexander Ekdahl, AI Leader
This new world introduces challenges around preserving and developing business-specific expertise. As AI agents learn and develop, businesses must rethink career development for these digital colleagues and ensure critical knowledge isn’t lost.
This is a fundamental shift for enterprise IT, where flexibility, scalability, and dynamic intelligence are not just buzzwords, but the very foundation of modern business operations.
From system-centric automation to digital colleagues at scale
Digital transformation isn’t about swapping one tool for another - it’s about reimagining your approach. Instead of relying on rigid, system-centric automation, today’s businesses can scale an AI workforce. These digital colleagues aren’t bound by fixed data models or centralized control; they operate fluidly across systems and bridge process gaps.
Digital colleagues transform enterprise IT by shifting the focus from static systems to dynamic AI resources - essentially treating AI as a form of talent on par with human resources. Just as nurturing human talent is critical for long-term success, developing and managing your AI talent will be key to driving business agility and innovation.
Although digital colleagues are built on technology, managing them mirrors traditional HR practices. From recruitment and onboarding to career development and knowledge retention, organizations must apply the same strategic principles to their AI resources. By doing so, businesses can harness the transformative potential of agentic AI and secure a competitive edge in the new era of enterprise IT. (We’ll explore what this means for IT organizations in future pieces - but needless to say, the shift is monumental.)
The AI talent challenge: retaining digital colleagues
When AI agents are embedded in enterprise software, they become part of the system rather than an enduring element of the organization. The moment that system is replaced, these digital colleagues - and the critical knowledge they carry - disappear. This situation is similar to relying on external consultants: once the engagement ends, so does the valuable insight and knowledge they provided.
The loss of talent problem
Example – AI-Powered Sales Automation in a CRM
Imagine AI agents that analyze customer interactions, prioritize leads, and automate follow-ups within a CRM. When a company migrates to a different platform, the digital colleagues - and the experience they’ve gathered in managing customer relationships - are lost.
To get value from our digital colleagues, businesses must develop strategies for retaining and transferring the knowledge these agents generate. Just as nurturing human talent is critical for long-term success, managing AI talent effectively will be key to sustaining business agility.
(Note: one might wonder what this will mean from a regulatory perspective, will we see regulation enabling AI agent portability across major enterprise platforms similar to data portability rights?)
Agent career planning: managing AI workforce evolution
AI agents need career paths, not just deployment plans. Just as human employees benefit from structured career growth, AI agents should also have clearly defined progression models within the business. Instead of treating them as disposable software tools, companies should develop frameworks that allow digital colleagues to evolve over time. For example:
Junior AI agent: performs basic automation tasks, such as data entry or simple rule-based workflows.
Mid-level AI agent: coordinates across workflows, integrating multiple systems and solving cross-functional problems.
Senior AI agent: handles decision-making and optimizes business processes with greater autonomy.
And maybe even, Executive AI agent: manages entire business functions, overseeing AI-driven decision-making across the enterprise.
“Companies that build robust agent career frameworks will gain a competitive advantage in AI workforce management, ensuring that digital colleagues continue to improve rather than remaining static.”
- Jens Eriksvik, CEO
Effective AI workforce planning must also include strategies for knowledge transfer, ensuring that critical intelligence is preserved even when underlying IT systems change.
You see where this is going right, we need a new way of approaching tech - a way that is more human.
Two strategies for deploying digital colleagues
The challenges of retaining AI agent expertise and managing their ‘career’ evolution put the focus on deployment strategies.
As traditional systems give way to AI-driven workforces, businesses need a clear plan for managing this talent. In essence, there are two broad, illustrative approaches:
Embedding digital colleagues within existing enterprise software, versus
Building an independent AI agent platform - a dedicated office for your AI team.
Each strategy comes with its own advantages and trade-offs, and understanding these options is key to developing an AI-first approach that is as human-centric as it is technologically advanced.
Strategy 1: Embedding digital colleagues inside enterprise software
In this approach, AI agents are deployed directly within an existing ERP or CRM platform, working alongside human teams to enhance operations. The key advantage is that it’s easier to implement since it leverages the current IT architecture and fits naturally into established enterprise workflows. However, there’s a significant trade-off: these digital colleagues remain tethered to the limitations of the underlying platform. This means if the platform changes, you risk losing your AI workforce altogether due to vendor lock-in.
Scenario 2: Building an independent AI agent platform
Alternatively, businesses can build a dedicated AI agent platform - essentially creating an independent “office” for their digital colleagues. Here, AI agents serve as an orchestration layer across multiple enterprise applications, operating independently of any single system. This strategy retains digital colleagues even when switching platforms, as they are not confined to a specific vendor’s ecosystem. It also allows for seamless integration across diverse systems, enabling AI-native operations rather than forcing AI into outdated, legacy software. The trade-off, however, is that this approach demands an AI-first IT strategy, requiring a more significant initial setup and a shift away from traditional IT patchwork.
Reimagining enterprise IT with digital colleagues
Adopting hybrid operating models will reshape how work is allocated. In these models, humans and digital colleagues form integrated teams where AI handles routine, data-driven tasks while humans focus on strategy, creativity, and complex decision-making. This shift redefines leadership - modern leaders must blend traditional management with an understanding of AI capabilities to facilitate human-AI collaboration.
This requires a thoughtful strategy on several fronts. Businesses must reimagine their physical and digital workspaces to support interaction between humans and AIs. Talent management must evolve, with clear career paths for digital colleagues and mentorship programs that integrate AI training into overall development plans. Businesses need to determine whether to build their own AI talent (separate AI agent platform), engage digital consultants (embedded in enterprise software), or continue relying on human expertise.
Transitioning to an AI-first operating model will involve cultural shifts, new workflows, and continuous adaptation. Leaders must guide their teams with clear communication, targeted training, and change management. In summary, deploying digital colleagues at scale is not just a technological upgrade - it’s a shift that demands a comprehensive rethinking of how work is structured, managed, and led.
The future: AI as the enterprise operating system
AI agents will replace significant portions of traditional enterprise software. Instead of managing a stack of rigid applications, companies will lead a dynamic workforce of digital colleagues - where IT shifts its focus from cost control and standardization to scalable intelligence and adaptability. The choice is stark:
Stick with costly, inflexible software that forces standardization, or
Embrace a world where digital colleagues dynamically handle enterprise complexity.
To embark on this transformative journey, follow these clear steps:
Assess your current landscape: Evaluate your business workflows to identify where agentic AI can deliver the greatest impact.
Select a deployment strategy: Decide whether to embed digital colleagues within your current enterprise software or build an independent AI agent platform.
Develop an AI talent management framework: Treat AI agents as you would human talent. Create structured career paths, implement mentorship models, and set up robust knowledge retention and transfer processes.
Create a change management plan: Prepare your organization for a cultural shift by designing a comprehensive change management strategy. Communicate the vision, train teams, and establish new workflows that integrate human and AI collaboration seamlessly.
Pilot and validate: Launch a pilot project in a high-impact area to test the new approach, gather insights, and refine your strategy based on real-time feedback.
Scale: With validated results in hand, expand your AI-driven operations incrementally, ensuring continuous adaptation and improvement as you transition to a fully AI-native enterprise model.
By following these steps, you can move confidently toward a future where digital colleagues transform your business, driving agility, efficiency, and competitive advantage.
In our upcoming articles, we’ll dive deeper into several critical areas:
Governance and security: We’ll explore how robust governance frameworks can be established for AI-driven operations, along with a detailed look at security and ethical considerations in this new paradigm.
Implementation challenges: While independent AI agent platforms offer advantages, we’ll discuss the practical hurdles of implementing these systems and share strategies for overcoming them.
Human-AI collaboration: We will examine how humans and digital colleagues can work together effectively, outlining best practices for seamless integration and cooperation.
The evolving role of IT and HR professionals: Finally, we’ll analyze how the IT department will transform in this new landscape, and what skills and strategies will be necessary to thrive in an AI-native enterprise.
