From proof of concept to production: the role of AI governance and evaluation

Artificial intelligence is widely recognised as a powerful accelerator for business operations and software development. However, during governance and evaluation processes, we’ve noticed that companies usually fall into a hole in which asking the wrong questions and assumed knowledge around the information that AI has, can be big killers of 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.  

Companies across all sectors are rapidly integrating AI into their operations. Nevertheless, research by Boston Consulting Group reveals that while investments remain high, only 26% of companies manage to move beyond the proof-of-concept (POC) stage to create tangible value at scale. 

Governance first grounding in business reality 

When organisations deploy AI, there is a common misconception that the technology will automatically understand how the business operates, including its workflows, talent culture, data management, and operational security. While AI can analyse information at incredible speed, it lacks an awareness of unwritten organisational context.  

Without this grounding and level of governance, even highly sophisticated tools created to assess issues risk missing the mark on what the organisation truly needs. In fact, even though 93% of companies have make use of AI tools, only 8% have applied governance within their procedures, as reported in the AI Governance Index report. Hence, this factor is the first point that we should focus on.  

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. In short: talk less about AI, and more about the issues it will tackle. 

The journey must begin by understanding 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 foundations 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. Establishing clear ownership and practical development standards is crucial for enabling organisations to progress seamlessly from the governance phase to evaluating their POC, by providing clear guidance on two main areas: how you store data and how the software is being written and used. 

Evaluation focus on real-world situations 

While governance establishes the boundaries within which AI must operate, evaluation determines whether those systems deliver accurate and valuable outcomes. It is at this stage that organisations validate whether their initial inputs were correct, confirming that the solution aligns with the needs of both stakeholders and the end-users who rely on the software daily. 

It is common for technical teams to focus on building impressive features before fully validating real-world value, as they may lack the broad operational context needed to separate technical noise from what customers truly need. External consultancies, such as Parallax, help bridge this gap by streamlining the evaluation process—objectively highlighting the relevant insights stakeholders need to see while filtering out unnecessary complexity. 

After defining a series of evaluation metrics, teams need to supervise performance and quality of the tool. Maintaining quality in a live environment with frequent changes, requires a systematic feedback mechanism that monitors decisions and feed insights into the system,  

At Parallax, we’ve evidenced that given the rapid pace of AI adoption across corporate operations, organisations are driving significant changes that need real-time feedback loops to quickly determine whether those updates 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 the Human-In-The-Loop is crucial to refine system instructions, update contextual knowledge base, and strengthen those guardrails that will improve the technology over time.   

When tools fail during their quality evaluation, it’s usually because the assigned task was ambiguous or lacked context and relevant data, which increases the likelihood of an inaccurate response. To achieve consistency, rather than assigning complex tasks all at once, organisations can deconstruct instructions into a series of small decisions and parameters. This allows the system to filter raw data and operate on targeted instructions, making every step easier to evaluate and correct before entering the production phase. 

Scaling to live production 

After setting up objective evaluation metrics for overseeing how the system operates, teams need a realistic view of how AI will run in their daily work. Despite AI being a useful tool for performance improvement, expecting an automated model to act as flawless expert usually backfires; instead, teams could treat the system as an intern that has the potential to deliver exceptional work.  

When onboarding new developers, organisations establish clear guardrails that define working standards, including limits on code deployment. The same principle applies to AI: enforcing these predefined standards guarantees the protection of sensitive information and paves the way for a smooth transition to live production. 

Determining when a system is ready for live deployment, requires human intel as 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 audibility as legacy enterprise software. By establishing clear governance and evaluation pathways, alongside human analysis, organisations can successfully cross from the proof of concept and build meaningful systems that add value to its daily basis and realities.