From AI Proof of Value to production

Bridging the implementation to accelerate your AI potential

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Your AI proof of concept worked brilliantly during its demo. Six months later, it's still sitting in a development environment, unused by your team and delivering zero business value. Sounds familiar? 

This scenario plays out in a lot of organisations, where technical success fails to be translated into operational value. This gap represents one of the most significant challenges in AI implementation today. Understanding why this gap exists - and how to bridge it systematically - can turn your organisation's AI potential into genuine business value. 

This is the second article in our AI implementation series. While our previous article explored how to move from initial ideas to successful proof of concepts, or rather proof of value, this piece addresses the critical next step: transitioning from technical validation to production systems that deliver measurable business impact in the long run. 

The PoC paradox: when technical success meets implementation reality

Most AI initiatives succeed brilliantly at the proof-of-concept stage: your algorithms achieve their target accuracy rates, stakeholders are impressed with demonstrations, and technical teams celebrate successful validation. Yet despite this technical triumph, a substantial number of these projects never reach production deployment. 

This paradox occurs because technical feasibility alone doesn’t guarantee successful implementation. Production deployment requires orchestrating complex stakeholder relationships, navigating regulatory requirements, and ensuring sustainable adoption. These kinds of challenges extend far beyond pure algorithmic performance. 

Every AI proof of concept leads to one of three distinct outcomes. Understanding which path your initiative is on provides crucial insight into the preparation required for successful production deployment: 

Path 1: strategic misalignment

  • The initiative seemed promising initially but doesn't align with the core business strategy. 
  • Without clear business ownership and involvement from senior leadership, it becomes a 'nice to have' rather than a 'must have', ultimately leading to project termination.

Path 2: technical success, implementational struggles

  • The proof of concept works technically, but significant effort is required to move to production.

  • Organisations often underestimate the complexity of enterprise deployment, leading to stalled projects and frustrated stakeholders.

Path 3: value-driven success

  • Clear business value is demonstrated, stakeholders remain aligned, and a systematic approach to production readiness ensures successful deployment.

The key differentiator between these paths is the systematic preparation for implementation challenges that occur after technical validation. 

The three critical success factors for production deployment

Production success of your AI initiatives requires you to master three interconnected elements: strategic alignment, stakeholder orchestration, and governance readiness. These areas often represent significant capability gaps within organisations, yet they're more critical to success than technical development itself. 

1. Strategic alignment: maintaining business focus

Your AI initiative must maintain clear alignment with your business objectives throughout the implementation journey. What seemed strategically important during the proof-of-concept phase can become less relevant as business priorities evolve or as the true complexity of implementation becomes apparent. 

Strategic coherence requires continuous validation. At each implementation milestone, successful organisations reassess business value, confirm resource allocation, and ensure the initiative continues supporting core objectives. This step is essential for maintaining the stakeholder support necessary for successful deployment, it's about more than just compliance.

2. Stakeholder management: the hidden implementation challenge

The transition from proof of concept to production multiplies your stakeholder complexity exponentially. What begins as a technical demonstration involving IT and business users evolves into an enterprise-wide initiative requiring coordination across legal, compliance, security, procurement, and operational teams. 

Each stakeholder group brings distinct requirements, timelines, and success criteria. Legal teams need comprehensive privacy assessments. IT security requires detailed security protocols and access controls. Risk management demands thorough assessments aligned with frameworks like ISO 27001. Business operations need change management support and user training programmes. 

Stakeholder management is more critical than technical development during the production transition. The most successful implementations treat stakeholder orchestration as a project management discipline, with dedicated resources, clear communication protocols, and systematic tracking of requirements across all involved parties. 

3. Governance readiness: preparing for compliance

AI systems in production operate within significantly more complex regulatory and compliance environments than proof of concepts. Belgian and EU organisations must navigate GDPR requirements, the emerging AI Act, industry-specific regulations, and internal governance frameworks. 

Compliance preparation must begin before your proof of concept concludes. Waiting until after technical validation to address governance requirements introduces substantial delays and can fundamentally alter your implementation approach. 

At BDO, our experience of implementing AI solutions across various regulatory environments taught us that proactive governance preparation dramatically reduces implementation timelines and prevents costly redesign efforts. 

No AI project has ever succeeded purely because the technology worked. Successful projects succeed because they create measurable business value.

Jan VermeerschPartner Digital with BDO Belgium

The timing trap: why momentum matters more than perfection

One of the most critical yet underestimated factors in AI implementation success is timing. The enthusiasm and organisational energy that surrounds successful proof of concepts has a limited lifespan. Extended delays during the transition to production can kill otherwise promising initiatives. 

Momentum is everything in AI implementation. Organisations that maintain continuous progress from proof of concept through production deployment achieve significantly higher success rates than those that pause for extended planning or approval cycles. 

Set a maximum 30-day decision window post-PoC completion. This timeframe forces you to address critical requirements systematically while maintaining the momentum necessary for successful implementation. Longer decision periods typically indicate insufficient preparation during the proof of concept phase. 

For a major telecommunications provider we worked with, maintaining momentum meant implementing comprehensive data quality surveillance solutions that created the foundation for future AI initiatives. By investing in data governance and quality proactively, they positioned themselves to deploy AI solutions with significantly greater value when their production systems were ready. 

Value measurement: the production imperative

If you can't measure business value clearly and consistently, your proof of concept isn’t ready for production deployment. Value measurement represents the foundation for stakeholder alignment, resource allocation decisions, and ongoing optimisation efforts. 

Production-ready value measurement encompasses four critical dimensions: financial impact through revenue increases, or cost reductions; operational efficiency via time savings or error reduction; strategic advantage including market positioning and competitive differentiation; and risk mitigation covering compliance improvements and security enhancements. 

Your board-level communication should focus on the EBITDA impact, margin improvement, and measurable business outcomes rather than technical performance metrics. While accuracy rates and processing speeds matter for operational teams, your executive stakeholders need a clear understanding of the business value you are creating with AI. 

For comprehensive guidance on measuring AI value across multiple dimensions and timeframes, see our detailed approach in our previous article on AI ROI measurement. The key principle remains consistent: value measurement criteria must be established before implementation begins, not after deployment completes.

Common pitfalls to avoid

Learning from others' experiences can accelerate your implementation success while avoiding costly delays and redesign efforts. The most frequent implementation failures follow predictable patterns that can be prevented through proper preparation. 

The perfectionist trap

  • Happens when you endlessly refine technical solutions without deploying them. While continuous improvement is valuable, production deployment creates the real-world feedback necessary to meaningfully optimise your AI initiatives. 
  • Adding to this, the key is also in being transparent about this feedback to your users. 

The compliance surprise

  • Occurs when regulatory requirements are discovered late in the implementation process, forcing you to substantially redesign and significantly extend your project timelines. 

The adoption gap dimension 

  • Results from building solutions without sufficient user involvement, leading to technically sophisticated systems that teams avoid using in practice.

The resource shortage

  • Happens when you underestimate the expertise and time required for production deployment, particularly around stakeholder coordination and compliance preparation. 

The strategy drift

  • Involves losing sight of your original business objectives as implementation complexity increases, resulting in solutions that work technically but don't deliver intended business value. 

BDO Belgium's AI Services

BDO's experts can help you implement this pragmatic, results-focused approach to ensure you not only measure AI value but maximise it through proper implementation and governance.

Our multidisciplinary team combines financial expertise with practical AI implementation experience to deliver solutions tailored to your organisation's specific needs. Contact our team today to discuss how we can help you navigate the complex landscape of AI investment and value measurement.

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Ready to move from the lab to the board? 

The transition from AI proof of concept to production deployment is a critical moment in your organisation's AI journey. Technical success provides the foundation, but business value emerges only through systematic implementation that addresses your stakeholder alignment, governance requirements, and organisational readiness.

Our AI team can help you navigate the complex transition from proof of concept to production deployment. Contact BDO to discuss your AI implementation challenges and discover how our multidisciplinary approach ensures successful outcomes.  

Interested in other AI related topics?

Discover our other AI articles here!