AI Quality as Competitive Advantage: Hendrik Reese on the Promise of the Quality Standard
Artificial intelligence is evolving from assistive tools to autonomous agents. This shift raises the bar for quality: providers must demonstrate that their systems are controllable and transparent. Adopters need reliable criteria for investment decisions. The MISSION KI Quality Standard offers both sides a shared framework for the first time.
PwC Germany played a leading role in developing the standard. In this interview, project lead Hendrik Reese explains why quality determines market success and how companies benefit when trust can be proven rather than just promised.
Mr. Reese, PwC led the development of the MISSION KI Quality Standard. What was the driving force behind this initiative, and what does it promise the German economy?
The driving force was to create a European innovation standard that combines competitiveness with trust and enables Germany to actively shape technological leadership once again. We wanted a framework that allows companies to deploy AI systems quickly, securely and at scale without constantly weighing innovation against risk. The promise is clear: quality creates speed, and speed creates strategic advantage for the German economy in the global AI race.
How does AI quality translate into tangible business value for providers looking to sell their solutions?
For providers, AI quality becomes a growth lever. To truly scale, they need to sell more than features; they need to sell trust in complex, increasingly agentic systems. Companies that operationalise quality lower the barrier to entry for their customers, reduce risk exposure and accelerate purchasing decisions. AI quality creates a new kind of product promise: performance plus responsibility. That's a unique selling point that wins in global markets.
And the other side: how can the Quality Standard help companies make better purchasing and investment decisions while avoiding costly mistakes?
For the first time, the standard gives companies a structured, innovation-friendly process to evaluate AI solutions objectively, even when those solutions are highly complex, multimodal or agentic. Investment decisions can now be based on maturity levels, risk profiles and governance capability rather than marketing claims or gut feeling. This reduces failed purchases, speeds up adoption and professionalises technology deployment, particularly in the Mittelstand.
Trust is the big buzzword. As a consultant, what does it mean when trust can actually be demonstrated?
Demonstrated trust means innovation is no longer held back by uncertainty but can be accelerated through governance. When companies can show that their AI systems are controllable, transparent and resilient, they build a solid foundation for disruptive applications, including agentic AI scenarios. Trust becomes an economic asset: it allows companies to innovate faster and more boldly.
Why is now the right moment to view AI quality as a strategic advantage?
We're at a historic turning point. AI is moving from assistive systems to autonomous, acting agents, and these systems create entirely new strategic dependencies. Quality is no longer a compliance issue; it's becoming central to products and business models. Companies that invest in AI quality now are laying the groundwork for scalable innovation, new business models, international connectivity and regulatory resilience.
How does the standard fit into the European regulatory landscape, including the AI Act and standardisation efforts, and what gaps does it fill?
The standard bridges the regulatory logic of the AI Act with the innovation logic of business, closing exactly the gap Europe has struggled with: operational implementation. It provides the foundation for a governance model that interprets requirements dynamically and technology-neutrally, including for new agentic AI forms. This makes Europe capable of acting before markets move on without it.
You developed the standard during the generative AI revolution. What was the biggest challenge?
The biggest challenge was creating a standard that doesn't trail technological developments but anticipates them. Generative AI and agentic AI are fundamentally changing the logic of risk, control and value creation. Traditional AI criteria no longer fully apply. So we developed an architecture that enables continuous innovation through horizontal criteria while ensuring robust governance, one that remains adaptable and extensible for emerging technologies.
What are the typical barriers you see in companies that want to deploy AI responsibly, and how does the standard help overcome them?
The biggest barriers aren't technological. They're about missing governance structures, unclear responsibilities and lack of scalability. Companies often don't know how to organise speed, control and innovation at the same time. The standard offers them a modular, ready-to-use blueprint that resolves these tensions and opens up a secure path into agentic, highly automated AI ecosystems.
