AI-by-default: hidden enterprise risks

StratEdge Founder Barbara Cresti sees AI-by-default as a forced adoption model that introduces ethical, financial, and governance risk faster than organisations can manage it.

Published by Process Reporter — January 27, 2026.

Key Points

• Microsoft's strategy to bundle Copilot across its 365 suite was a flawed attempt to fix lagging adoption that instead jeopardised trust and created significant operational risks.
• Barbara Cresti, Founder of StratEdge, cautions that imposing tools without transparency may harm long-term adoption and exposes a lack of internal governance.
• Cresti advocates for a top-down playbook that includes clear policies, structured training led by expert users, and careful management of hidden operational costs.

This move is not pushing people to AI. It's not accelerating adoption. It's creating issues because trust is harmed when tools are imposed without transparency.

While Microsoft's recent decision to bundle its Copilot AI across the Microsoft 365 suite is meant to accelerate usage, it introduces major ethical and operational risks. By embedding these systems by default, often without informed consent or clear opt-out paths, industry experts warn that the move could undermine the very trust required for long-term adoption and ROI.

Cresti views AI-by-default as a strategy that introduces risk before earning confidence. As she sees it, the Copilot rollout isn't an act of generosity from Microsoft, but a direct response to lagging adoption across markets — and the execution is ethically flawed.

The AI trap

For many users, especially within small and medium-sized businesses, AI tools are thrust upon them without clear communication. "I think that they will find themselves trapped in this AI that maybe they don't want to use, but that will retrieve all their data," she says. While savvier users may know how to disable these features, the default opt-in model may leave non-technical users feeling stuck.

Discreet data grab

Microsoft's default opt-in model is reminiscent of LinkedIn's quiet move to automatically use the data of certain users to train AI models. When news of the decision came to light, the company faced negative PR and litigation. "They were not transparent with users," Cresti recalls.

Aside from lost trust, AI-by-default strategies can backfire when users encounter the practical limitations of the technology. "I've been using ChatGPT for a long time, and I feel like it's regressing, not progressing," Cresti notes. Users are discerning and likely to notice when an imposed tool introduces errors or makes their work harder. That kind of negative experience, particularly in risk-averse cultures, can trigger a real backlash that further harms adoption.

Governance problems run deeper

External adoption failures are often compounded by deep internal governance problems. Only a quarter of organisations have fully implemented AI governance programs. "Employees start using AI tools without clear guidance from the executive team or the board on what the rules are and what they should be using it for." This exposes companies to risks from shadow AI, where confidential data is input into non-corporate tools.

Structure over chaos

To avoid governance issues and messy rollouts, Cresti advocates for a top-down playbook and an emphasis on data readiness — which 43% of leaders cite as the most significant obstacle to aligning AI with business objectives. She draws directly from her positive experience at AWS when OpenAI tools were introduced: "What happened at AWS is that everything was framed. There were lots of policies around AI integration into the business. They gave us exact tools to experiment with and try." This model requires leadership to outline specific parameters for which tools to use and to regularly audit usage.

The literacy gap

Another major failure point is a cascading literacy gap. "Take prompt engineering, for example. I've spoken with a lot of CXOs and senior managers and they've never had a prompt engineering class." That gap often results in a failure to invest in proper training. Frequently, "IT has been tasked to do AI without knowing AI." She encourages the concept of a "super user" within each team who receives consistent training on how to best use the tool and cascade information down in a functional way.

Costly queries and energy drain

Beyond training, Cresti highlights the hidden operational costs of AI as a major blind spot. Much like the early days of the cloud, many organisations are using AI without a clear understanding of its full financial impact. "Corporations are using AI without really thinking about how much each prompt costs. It's a black box in terms of pricing." Compounding this are the substantial energy impacts of data centres. Unlocking AI's ROI will require a disciplined strategy to manage its true operational cost.

Sovereignty and the global question

Some companies are taking a more sophisticated approach, particularly in Europe. Rather than relying on a single provider, organisations are moving toward an "ecosystem of models," selecting different tools based on the risk and regulatory requirements of a given workload. This is fueling a quick pivot toward sovereign AI models and clouds that give organisations more control over their data.

Something that is getting bigger and bigger in Europe is the interconnection between geopolitics and technology. The complexity for global businesses is, how global can you be?