From proof of concept to production: why AI governance and evaluation matter

AI has become globally recognised as a way to speed up both business operations and software development. Yet during governance and evaluation processes, we often see companies fall into the trap of asking the wrong questions and assuming AI already knows things it doesn’t, two of the biggest drains on AI productivity. Both governance and evaluation phases can be supported by consultancy firms that can guide the process and conduct security testing on new tools. 

Organisations in every sector are moving quickly to build AI into the way they work. However, research from Boston Consulting Group shows that despite continued heavy investment, just 26% of companies make it past the proof-of-concept (POC) stage and go on to generate real value at scale.

Governance first: grounding AI in business reality 

There is a common misconception that when organisations deploy AI, the technology will instinctively grasp how the business runs, from its workflows and talent culture to its data management and operational security. AI can process information remarkably quickly, but it has no awareness of the unwritten context that shapes how an organisation actually works.  

Without that grounding and the right level of governance, even the most sophisticated tools built to assess issues can miss what the organisation really needs. According to the AI Governance Index report, 93% of companies have used AI tools, yet only 8% have built governance into their processes. That makes governance the first thing to get right.

Leaving AI unguided and without operational context can lead it to make assumptions and propose solutions that seem efficient, but in reality contradict organisational norms such as data handling policies. Governance provides the essential structure required to bridge this gap, ensuring that what is created directly aligns with the actual problem to be solved. Put simply: spend less time talking about AI and more time talking about the problems it’s meant to solve.

The starting point is a clear understanding of the specific risks an organisation faces. For instance, the operational and regulatory risks of a marketing agency differ significantly from healthcare providers or financial institutions when handling sensitive data or transactions. In this sense, defining accountability and risk appetite across different operational scenarios can lay the groundwork for a strong governance approach. 

Leadership teams must establish a clear risk appetite across operational scenarios by defining their tolerance for high-impact events like data leakage and regulatory non-compliance. Rather than acting as a bottleneck that halts progress between the POC and production, effective governance integrated within AI can support continuous review of documentation and flag potential vulnerabilities. Clear ownership and practical development standards are what allow organisations to move smoothly from governance into evaluating their POC, giving clear direction on two key areas: how data is stored, and how software is written and used.

Evaluation: focusing on real-world situations 

While governance establishes the boundaries within which AI must operate, evaluation determines whether those systems deliver accurate and valuable outcomes. This is the point where organisations check that their initial inputs were right, and that the solution meets the needs of both stakeholders and the end-users who work with the software everyday.

Technical teams often concentrate on building impressive features before proving their real-world value, partly because they may lack the wider operational context needed to tell technical noise apart from what customers genuinely need. External consultancies such as Parallax help close this gap by streamlining the evaluation process, objectively highlighting the relevant insights stakeholders need to see while filtering out unnecessary complexity. 

Once evaluation metrics are in place, teams need to keep a close eye on the tool’s performance and quality. In a live environment where things change frequently, maintaining quality depends on a systematic feedback mechanism that tracks decisions and feeds insights back into the system.

In our experience at Parallax, the speed at which AI is being adopted across business operations means organisations are making significant changes, and they need real-time feedback loops to quickly see whether those changes are delivering the right results. However, internal teams often make the mistake of conducting evaluations just once, rather than on a semi-regular basis. Keeping a human-in-the-loop is essential for refining system instructions, updating the contextual knowledge base and strengthening the guardrails that help the technology improve over time.

When tools fail during their quality evaluation, the cause is usually a task that was ambiguous or lacked context and relevant data, both of which increase the likelihood of an inaccurate response. For more consistent results, organisations can break complex tasks down into a series of smaller decisions and parameters rather than handing them over all at once. This lets the system filter raw data and work from targeted instructions, so each step is easier to evaluate and correct before moving into production.

Scaling up to live production 

After setting up objective evaluation metrics for overseeing how the system operates, teams need a realistic picture of how AI will run in their daily work. AI can be a valuable tool for improving performance, but expecting an automated model to perform as flawlessly as an expert tends to backfire; instead, teams could treat the system as an intern that has the potential to deliver exceptional work.

When onboarding new developers, organisations set clear guardrails around working standards, including limits on deploying code. AI should be treated the same way: enforcing these predefined standards guarantees the protection of sensitive information and paves the way for a smooth transition to live production.

Deciding when a system is ready for live deployment calls for human judgement, alongside an analysis of operational costs. While absolute perfection is rare, if an automated system reliably matches human accuracy with at least 70%, it’s often ready for deployment, meaning that a human factor will still be essential during and after this phase.

AI tools must be subject to the same standards of traceability and auditability as legacy enterprise software. By establishing clear governance and evaluation pathways, alongside human analysis, organisations can make the leap from proof of concept to meaningful systems that add value to their day-to-day operations.