The AI platform era is here and most enterprises aren’t ready

The AI landscape is shifting faster than most organizations can keep up. What was once about experimenting with cutting-edge models is now about re-architecting entire businesses around AI platforms. That urgency took center stage at WAVE 2025, where Malte Kosub, CEO & Co-Founder of Parloa and Laura Modiano, Startups EMEA, OpenAI, shared a candid conversation on the market shifts already reshaping industries, and why most enterprises aren’t ready for what’s next.

The shift isn’t coming. It’s already here

The conversation opened with a reality check. AI is no longer a side project or an innovation lab experiment. It’s becoming the backbone of how competitive companies operate.

As Laura Modiano said:

Both Malte and Laura highlighted three major transitions already underway:

• The end of model supremacy: The era of chasing “the biggest model” is fading. What matters now is how you deploy, integrate, and orchestrate AI across the enterprise.

• Massive platform shifts: The next competitive edge will be owned by platforms and ecosystems, not one-off point solutions.

• Fragmented global ecosystems: Diverging regulatory frameworks and national strategies mean companies must operate with regional nuance, not a single global template.

Malte underscored the risk of staying stuck in experimentation mode, warning that without a clear strategy for scale and orchestration, “pilots become a dead end instead of a launchpad.”

Why most organizations are underprepared

The enthusiasm around AI doesn’t match enterprise readiness. As both speakers made clear, there’s a widening gap between ambition and execution, and it’s not a question of technology maturity. It’s about whether companies are equipped to operationalize that technology at scale.

This underpreparedness isn’t just an operational hiccup. It’s fast becoming a competitive fault line. Companies that can’t translate AI experiments into production-grade impact risk being left behind by those that can.

Cultural inertia

Many organizations still treat AI as a tool to be experimented with, not a foundational layer of their business. That mindset slows transformation and keeps AI siloed in innovation teams instead of embedding it in everyday operations.

This cultural hesitation creates an execution gap: while some companies are moving from pilot to platform, others remain trapped in endless “proofs of concept” with no clear path to value.

Legacy infrastructure

Even when enterprises adopt advanced models, they often lack the operational plumbing to integrate them into live workflows, data streams, and decision-making.

Most core systems weren’t built for real-time orchestration. Without modernized infrastructure, enterprises end up with fragmented deployments — a collection of clever demos rather than scalable solutions.

Governance and trust gaps

Regulation and public trust are not side issues anymore. Building AI responsibly is as critical as building it fast. Data residency, security, explainability, and compliance now shape whether deployments can scale globally or stall at the legal department’s desk.

This is where leaders have to think beyond capability to accountability. A strong governance layer turns AI from a risk to a strategic asset.

Lack of platform thinking

Too many teams stitch together isolated tools instead of developing scalable agentic systems that can act autonomously and continuously improve. This short-term mindset might deliver quick wins but creates technical debt that’s hard to unwind later.

Turning readiness into competitive advantage

AI isn’t just another technology wave, it’s a structural shift in how value is created and delivered. Companies that close this readiness gap will:

• Unlock compounding value, because well-orchestrated AI improves itself over time.

• Scale faster, moving from experimentation to production across multiple functions.

• Strengthen trust and resilience, positioning themselves to navigate regulatory and operational complexity confidently.

• Differentiate competitively, not by having access to a model (which anyone can) but by building the orchestration layer others can’t replicate.

Failing to act, on the other hand, risks creating a widening performance gap, where slow adopters aren’t just behind on innovation, but on growth, talent attraction, and long-term market relevance.

Europe’s moment in the AI story

One of the most energizing threads of the conversation focused on Europe’s unique position in the next AI chapter.

Built for trust, positioned for growth

European startups can lean on a foundation of privacy and trust, which is increasingly becoming a differentiator globally.

Domain expertise meets orchestration

Malte emphasized that the real opportunity lies at the intersection of domain expertise and orchestration platforms. Rather than competing on model size, European innovators can win by solving specific enterprise problems with precision.

Scale remains the challenge

Access to data, regulatory clarity, and deeper integration with enterprise workflows will determine whether European AI startups can move from promising pilots to global platforms.

A 5-point playbook for enterprise leaders

Malte and Laura emphasized that while AI adoption can feel daunting, there are clear steps enterprises can take to move from experimentation to real impact.

Here’s a more detailed framework inspired by their conversation:

1. Treat AI as a core strategic initiative

AI shouldn’t be a side project tucked away in R&D. It requires executive sponsorship to signal its importance across the organization. Leadership must prioritize AI as a driver of tangible business outcomes, not just a technological experiment.

Malte highlighted that executive support is essential to get teams aligned and ensure projects have the resources and authority to scale.

2. Define KPIs that tie directly to outcomes

It’s not enough to track outputs like completed tasks or interactions; success metrics must reflect the broader goals they enable — revenue, efficiency, or customer satisfaction.

Laura stressed that companies should always step back and consider “what you’re actually trying to affect as a result”, ensuring AI is optimizing real business outcomes rather than isolated processes.

3. Build empowered, cross-functional teams

AI projects thrive when teams own the journey end-to-end. Combining product, engineering, data, and domain experts ensures collaboration across perspectives and faster problem-solving.

Malte pointed to IKEA and Booking as examples of enterprises where highly motivated teams drove successful adoption, underscoring that ownership is critical to moving from pilot to scale.

4. Embed compliance and trust at the foundation

Trust and governance are not optional. Global enterprises need AI systems that are compliant with local regulations, privacy requirements, and ethical standards.

Malte explained that European companies must “build products for different languages, for different regulatory foundations,” reflecting the need to think globally from day one. Proactive compliance safeguards adoption, strengthens customer trust, and reduces operational risk.

5. Design for scale and systemic integration

AI pilots are easy; enterprise-scale AI is challenging. Organizations should approach adoption as a platform-level transformation rather than isolated experiments.

Malte explained that companies must develop infrastructure and orchestration to support AI agents across functions and geographies. By designing systems that can operate autonomously yet integrate seamlessly with human teams, enterprises can unlock exponential value over time.

The race is on

The message from the WAVE stage was clear: we’re entering the agentic era of AI. Models won’t just assist — they’ll act. Competitive lines are being redrawn, and the companies that build for orchestration, platform scale, and regulatory resilience will win.

Europe, in particular, has a window to lead. But that window won’t stay open forever.

For enterprises still stuck in pilot mode, now is the time to make the leap. For startups, it’s a chance to shape the platforms that will power the next decade of intelligent business.

The platform era has arrived, and readiness will be the ultimate differentiator.