Navigating the Next Stage of Corporate AI
Beyond Microsoft Copilot: How advanced AI models are reshaping work
Ask any self-proclaimed expert what’s all the rage in AI as of 2025, and they will be quick to respond—AI agents. You press on, but what about corporate AI? Most likely, you’ll hear the instinctual reply: "We've moved on from pilots and testing, to full-scale implementations." Now, you might find yourself where many others stand—with a large-scale roll-out of Microsoft Copilot. Your one and only AI-bet, infrequently used, and struggling with everything inexperienced employees throw its way. Then you wonder—is this it? Is this the AI revolution? And crucially, where do we go from here?
Chances are your situation might be even worse. If you're based in the EU, or like me, in Sweden, it's probable that you haven't rolled out any significant AI initiatives at all. Now, you're quietly ashamed to mention that pilot project which has been dragging on for over a year and a half—with its main outcome being a painful understanding of the phrase "pilot purgatory."
According to BCG’s GenAI in the Nordics report (January 2025), the overall situation in Europe is challenging, and even more pronounced in the Nordic region. Companies here haven’t unlocked the AI use cases that generate substantial value. The share of white-collar workers using GenAI weekly at work stands at a modest 18%:
The situation is not quite as bad as it seems. AI usage isn't limited to employees directly interacting with an AI chat. Most AI adoption will happen quietly in the background—many won't even notice it. AI is increasingly embedded within our infrastructure, processes, and everyday tools. Nevertheless, direct interaction with advanced AI models is emerging as a powerful source of competitive advantage—and recently, these capabilities have improved significantly.
Rapid Advances: Approaching the AI Tipping Point
Consider the utility of AI not as a linear progression that steadily generates incremental value, but as a step-change—moving rapidly from delivering minimal impact to being fundamentally indispensable. We are nearing this tipping point. For example, at Anthropic, one of the leading AI labs, AI already writes 80–90% of the company's code.
Anthropic is likely using a next-generation model for this, superior to anything publicly available. However, the gap between their internal model and publicly accessible alternatives isn't as large as the difference between standard models (such as OpenAI’s 4o) and their state-of-the-art model, o3. [If you find the model naming confusing, you're certainly not alone—the major AI labs are notoriously poor at naming their products. For instance, OpenAI’s most basic model is named "4o-mini," while their second-best model is called "o4-mini."]
The trend is clear—the gap between standard AI models and the frontline, cutting-edge offerings is widening. Anthropic’s new Claude Opus 4 reportedly is capable of single sessions lasting up to seven hours, moving us closer to AI agents capable of continuous, 24/7 operation. However, such advanced capabilities won’t be included in the basic $20-per-month subscription.
Even today, gaining full access to the most advanced AI models is costly. OpenAI costs $200, Google Gemini costs $250, and Anthropic’s Max subscription is priced at $100 per user per month. Include a few lesser-known AI tools, and the total cost per fully equipped employee quickly climbs to the $700–800 range. That's substantial—particularly for companies with many employees. But it might be worth it. Or, it might be a total waste of money.
A challenge is that fully leveraging these AI models isn't straightforward. It takes extensive knowledge about each model, training in prompting techniques, and years of business experience to effectively identify valuable applications. Some employees will excel, but many will struggle.
Two Competing Visions
There are two distinct visions for the future of corporate AI. Since GPT-3.5 emerged in November 2022, there has been an ongoing debate about whether corporate AI will be dominated either by AI copilots that support humans or by AI agents that replace them entirely. We can now refine this debate further. For applications at the lower levels of the technology stack, there's little doubt these will increasingly involve AI solutions acquired, integrated, and managed centrally by the company, primarily driven by efficiency. This shift will likely reduce the demand for employees in those areas.
As for applications where users directly interact with AI, such as chatbots, the situation is less straightforward. Companies are likely to offer a standardized and centrally approved set of AI tools to most or all of their employees. For companies already invested in Microsoft, the simplest path might be to enhance their existing MS365 suite with Microsoft's AI offering, Copilot. An alternative approach is to adopt the enterprise version of OpenAI's ChatGPT, available on EU-based servers within a Microsoft Azure environment, ensuring compliance with security and GDPR standards, for European companies. However, this route raises more complex considerations compared to simply activating Copilot within MS365. Companies that proceed overly cautiously or allow inexperienced legal teams veto power, risk becoming stuck in indecision.
Simultaneously, employees increasingly desire access to the most advanced AI models to excel in their roles. Employees thus have a strong incentive to secure these models—even if it means covering the costs themselves. Today, certain roles—especially software development, marketing, customer support, research, and business intelligence—are already witnessing employees ‘10x’ their productivity through increased output volume, speed, and quality. Additionally, any role heavily reliant on critical decision-making stands to benefit substantially.
In many cases, standardized corporate solutions simply won't suffice. High-performing individuals will have strong incentives to develop personalized toolsets comprising specialized AI models and agents. Hiring such an individual could effectively be equivalent to onboarding an entire small team. To enable this effectively, companies will need flexible guidelines governing AI usage and onboarding procedures for both the employee and their AI agents.
Thus, the evolution of corporate AI usage can be driven both by company strategy and individual initiatives. In either scenario, fewer employees will be required to perform tasks that currently exist. Many in the AI industry have hesitated to openly acknowledge this impact. However, as the technology matures and the implications become clearer, more industry leaders are speaking out explicitly—such as Anthropic CEO and co-founder Dario Amodei, who stated in a May 2025 interview:
“AI could wipe out half of all entry-level white-collar jobs — and spike unemployment to 10-20% in the next one to five years”
Historically, new job categories have emerged to replace those eliminated by automation, from agriculture to manufacturing. However, these transitions do not occur instantly. Even if new roles eventually appear, the transition period can be particularly challenging.
A Pragmatic Path Forward
While it’s easy to speculate about long-term visions, the more pressing issue is what we should do right now. Should we provide AI subscriptions to our employees—and if so, to whom? How should we decide who receives access? At first glance, it might seem necessary to choose between the two scenarios: AI managed centrally by the company, or AI tools managed independently by employees. Fortunately, this presents a false dichotomy. We can—and should—embrace both strategies, leveraging the strengths of each. Let's therefore explore a pragmatic, bimodal approach guided by business cases.
Empowering Employees with Personalized AI Access
There's no need to overcomplicate the process of granting access to advanced, costly AI models. Employees requesting such access should clearly justify in writing how the model will benefit their work, including a financial rationale. Approved employees could then receive the tool for a three-month evaluation period. There's an additional psychological benefit here: requiring written justification and offering time-limited access creates a powerful incentive for employees to fully leverage the model’s capabilities.
Building a Centralized AI Capability
At the same time, we want all our employees to benefit from the potential offered by state-of-the-art AI models. To achieve this, a centralized AI function or team is needed.
You might already have a foundation for this centralized function from previous initiatives: perhaps an existing AI team, a research or business intelligence group, or a data science team.
Start small—it might initially involve just a single person, possibly on a part-time basis. Then grow the team organically in alignment with the value it creates and the internal demand generated. Initially, there may be little natural demand for the team’s offerings, so proactive efforts will be required to establish interest. An internal awareness campaign can help clarify what the team can offer. Regular short weekly check-ins with project teams and individuals can facilitate collaborative brainstorming to identify opportunities for AI-generated value.
The centralized AI team should be able to provide support in research, strategic planning, data analysis, and developing small-scale applications. Ultimately, the team's impact will heavily depend on its members' capabilities. To maximize effectiveness, team members should have extensive business experience, advanced prompting skills, and deep familiarity with various AI models and use cases. While it might be tempting to select a junior, tech-savvy "AI-native," genuine business experience will significantly enhance the team's effectiveness.
Preparing for an AI-Enabled Workforce
As AI matures, we can expect to see more advanced AI agents handling a larger share of work. The centralized AI team will then shift toward orchestrating these AI agents, reducing direct support for individual employees. Consequently, two distinct skillsets will be increasingly needed: one focused on technical expertise and another on business strategy and operations.
Employees will likely transition into one of two primary roles. They will either perform tasks that AI cannot yet handle reliably or is legally prohibited from performing, or they will extensively manage and collaborate with specialized AI agents. Those managing AI agents will effectively lead personalized teams of tailored AI resources, operating with capabilities comparable to running their own small companies. This shift will blur traditional distinctions between employee roles and external suppliers or service providers.
Returning to where we started, is this really "it"? Is this the transformative AI revolution we were promised? Not yet. But we're undoubtedly on the brink. By navigating carefully between centralized strategies and individual empowerment, companies can shape the next wave of corporate AI, turning cautious investments into substantial competitive advantage. The revolution isn't here yet, but its first signs are unmistakable—and smart organizations are already preparing for what comes next.