Contributing expert: Vittesh Sahni,
Sr. Director of AI at Coherent Solutions
 

In recent years, AI has been framed as a business cure-all; a generator of insights, a productivity booster, a cost-cutting machine. The promise is everywhere: plug it in, and the transformation begins. In fact, 25% of applications include AI, but only 2% of enterprises are “highly ready” to leverage its benefit.

 

But here’s the truth: AI only works when the organization is ready.

It won’t clean messy data, align siloed teams, or modernize outdated systems. It won’t take half-formed ideas and turn them into strategies. What it will do, often quickly and with precision, is expose everything that’s not working. Teams that invested in data, systems, and alignment saw results. Those that rushed into their AI implementation strategy often hit blockers, missed ROI, and walked away disillusioned. Not because the tech failed, but because the foundation of organizational AI readiness wasn’t there.

AI isn’t a shortcut. It’s an amplifier. If the fundamentals are strong, AI helps you scale. If they’re weak, it highlights the gaps.

So where do you start? With the grind layer. It's the foundation beneath the model: clear ownership, clean data, aligned teams, modern infrastructure. It’s the quiet, often invisible work that makes AI stick not for a demo, but for the long haul.

Get that part right, and AI doesn’t just perform, it becomes part of how you grow. 

 

AI is not plug-and-play it exposes the truth


It’s tempting to think that once the groundwork is in place, you can bring in AI and immediately start generating results. But scalable AI systems aren’t plug-and-play, they reflect the complexity and quality of the systems they’re placed into.

In fact, 85% of AI projects fail due to poor data quality or a lack of relevant data. Additionally, 58% of AI leaders cite disconnected systems as a top blocker to successful AI deployment.

Without the right architecture, governance, and organizational readiness, even the most advanced AI models will fall short in production. 

of AI projects fail due to poor data quality

of AI leaders cite disconnected systems as a top blocker

 

AI doesn’t replace weak processes: it exposes and amplifies them.


One of the most common misconceptions is that AI can be “dropped in” to magically fix broken systems. In reality, AI doesn’t solve chaos but surfaces it. It depends entirely on the integrity of your inputs: your data, your systems, your structure, and your workflows. If these are outdated, siloed, or inconsistent, even the most advanced AI model will struggle, not because the model is flawed, but because it’s faithfully reflecting the dysfunction it’s been fed.

 

This plays out in familiar, day-to-day scenarios:

  • Duplicate or incomplete CRM records → inaccurate forecasts
  • Poorly labeled support tickets → inconsistent routing and delays
  • Conflicting KPIs across dashboards → contradictory or confusing recommendations


AI doesn’t fix these problems, it highlights them. That’s not a failure. That’s feedback.

The real value of AI isn’t just in generating answers it’s in forcing better questions.


And for organizations ready to act on that feedback, it becomes the starting point for real, sustainable transformation.

But here's where many teams go wrong: they treat AI as a shortcut, a way to leapfrog process redesign, system cleanup, and internal alignment. Why fix what’s broken when a smart model might “just solve it?”

In practice, it doesn’t work that way.

AI doesn’t eliminate the need for clarity and structure; it raises the bar for both. It’s not a substitute for transformation; it’s an extension of it.

That’s why, before launching any AI model, you need to answer foundational questions:

What exactly are we trying to improve: A workflow, a decision process, a bottleneck?

  • How will we measure success?
  • Where does the relevant data live, and who owns it?
  • Is that data clean, accessible, and compliant?
  • Are teams ready to act on AI output and adapt processes accordingly?


When these questions go unanswered, AI typically ends up in one of two places:

→ A novelty: impressive, but unused.

→ A black box: generating decisions no one fully understands or trusts. The reality is simple:

Good AI rests on good architecture.


Not just technical architecture, but organizational: alignment, accountability, data readiness, process clarity. It’s not about building the flashiest model, it’s about designing systems that are understandable, traceable, and built to evolve.

The teams that make AI stick are the ones that treat AI as a strategic capability, not a silver bullet. They lay the groundwork first, and as a result, they see outcomes that are not only measurable but sustainable.

Those that skip this step often find themselves starting over. Only this time, with less trust and more hesitation.
 

The hidden layer of AI projects that actually matters


It’s not flashy. There are no grand predictions or buzzwords. But in real-world AI deployments, the most important ingredient for AI success is often the most overlooked: preparation.

That preparation starts with alignment.

1. Cross-team alignment


Before you ever train a model, your team's product, ops, IT, and marketing groups need to agree on what “success” looks like. If every department is optimizing for different outcomes, AI will struggle to deliver value for any of them. It’s like giving four people one steering wheel; no matter how good the vehicle, it’s not going far.

2. Integration with reality


AI doesn’t run in isolation. It has to plug into your current stack, including CRM, ERP, APIs, customer journeys, and support systems. If those systems are outdated or siloed, they’ll drag everything down. We’ve seen strong models fail not due to modeling flaws, but because they couldn’t integrate with the business.

3. Governance, Risk & Compliance (GRC)


Transparency is non-negotiable, especially in regulated industries. Can your team explain how the AI model works? Can decisions be audited? Is data handled responsibly? These aren’t side quests; they’re foundational to trust and sustainability.

4. Change management


Even the best AI model won’t drive impact if no one uses it. People need to understand what the model is saying, trust its outputs, and know how to act on them. That takes onboarding, communication, and training. AI adoption doesn’t happen by default; it happens when people feel supported, not replaced.

AI works best when it's not positioned as a revolution, but as a refinement.


Sometimes that means starting small: one workflow, one team, one problem worth solving. Measuring impact, learning, then scaling up. 

 

An AI proof-of-concept is the spark, but production is the fire


On paper, an AI prototype can come together in a matter of weeks. A small, focused team builds a model, connects a few data sources, runs some tests, and voilà, a working demo that looks great in a slide deck.

But here’s what that demo doesn’t show: The hard road between prototype and production.

At Coherent Solutions, we've seen AI development projects that looked promising in sprint reviews take six months or more to fully integrate. Not because the model was wrong, but because everything around it wasn’t ready.

  • A demo that runs smoothly in a sandbox, chokes on real-world data riddled with inconsistencies.

  • A model that predicts with 90% accuracy can’t be deployed because nobody clarified who owns the data or who’s responsible for acting on the output.

  • The tech works, but operations stall because legacy systems don’t talk to each other or to the AI layer.


This isn't a failure of AI. It’s a reflection of how much successful deployment depends on what happens outside the model.

Here’s the part that doesn’t make headlines: machine learning itself is usually only 20% of the total effort. The other 80%? That’s everything else:

  • Building reliable, compliant data pipelines

  • Integrating with legacy systems and APIs

  • Creating audit trails and governance structures

  • Designing workflows that people actually use

  • Training teams and managing change


If your roadmap doesn’t account for that 80%, delays aren’t just possible, they’re inevitable.

And that’s why the real difference between AI that delivers and AI that fizzles isn’t found in the algorithm. It’s in the architecture, the accountability, and the patience to build things right the first time.

Great AI isn’t just built, it’s adopted. And adoption starts when expectations meet reality.

Transform your business with scalable AI solutions

Ready to take your AI projects from proof-of-concept to production? Discover how we can help you achieve seamless integration and long-term success.

 

Final thoughts: AI is not the beginning it’s the result of doing the hard stuff right


When AI projects stall, it’s rarely because of the technology. More often, it’s a signal that something foundational hasn’t been addressed: unclear goals, weak data, fragmented ownership, or siloed teams.

Each challenge surfaces where structure, clarity, or collaboration needs to mature. And that’s not a setback; it’s an opportunity. An opportunity that, when acted on, creates the resilience needed to make AI scalable, sustainable, and truly valuable.

AI is not magic. And it’s certainly not plug-and-play. But it is transformative, for organizations willing to treat it as a capability to grow, not a shortcut to instant results.

Because the real return doesn’t come from the first model, or even the tenth.
It comes from building a system, a structure, and a culture that asks better questions, acts on the answers, and improves over time.

That’s the hard part.
And that’s exactly what makes it worth it.

 

FAQs on AI architecture best practices

  • Good architecture is crucial for AI because it provides the robust, scalable foundation needed to handle complex AI models, large datasets, and continuous learning. Well-designed AI architecture ensures that data flows efficiently between systems, integrates seamlessly with existing infrastructure, and can scale as AI solutions grow. It also ensures that AI models can transition smoothly from proof-of-concept (PoC) to production by addressing issues like model deployment, version control, and system performance. Proper architecture supports seamless data processing, decision-making, and the real-time capabilities that AI demands.

  • While AI can automate certain data cleansing processes and identify patterns in messy data, it cannot fully resolve the underlying issues of outdated or fragmented systems by itself. AI depends on well-structured, accurate, and consistent data to function effectively. This means that organizational AI readiness plays a key role. Teams need to invest in modernizing data systems, ensuring data quality, and setting up the proper infrastructure for AI to thrive. AI can help clean up data inconsistencies, but it cannot fix deeply ingrained systemic inefficiencies without human intervention.

  • Governance, risk, and compliance (GRC) are critical to AI adoption, ensuring that AI projects meet ethical, legal, and regulatory standards. In AI, GRC frameworks help organizations manage risks like biased algorithms, data privacy violations, or unintentional harm caused by AI decisions. Organizational AI readiness includes ensuring compliance with standards and aligning AI initiatives with strategic goals while minimizing risks. Without a strong GRC framework, AI projects might face legal and reputational challenges, making it harder to move from AI proof-of-concept to successful AI production.

  • AI projects fail when transitioning from proof-of-concept to production for several reasons:

    • Scalability issues: A prototype often works in a limited environment with clean, well-prepared data, but production environments deal with real-world complexity and large-scale data.

    • Lack of integration: Prototypes are often isolated from existing business systems, and integration with legacy infrastructure can be challenging.

    • Data quality and consistency: Prototypes may work with ideal data, but production systems often struggle with messy, inconsistent data.

    • Inadequate testing: The rigorous testing needed to ensure AI models work reliably at scale may be overlooked in the PoC phase. 

    Successful AI implementation requires organizational AI readiness, including clear goals, a scalable architecture, governance practices, and ongoing monitoring. 

  • To ensure a successful AI launch, organizations should:

    • Assess organizational AI readiness: Evaluate the technical, cultural, and procedural aspects to determine whether the organization is prepared to adopt AI.

    • Develop AI skills: Train teams in both technical AI knowledge (such as data science and machine learning) and business applications of AI to bridge the gap between technology and strategy.

    • Establish strong governance: Set up a governance framework that ensures responsible AI use, compliance, and accountability.

    • Ensure data quality: Prioritize data collection, cleaning, and organization to make sure that the AI models have the accurate and clean data they need to operate effectively.

    • Plan for integration: Ensure that AI systems can be smoothly integrated into existing IT and business infrastructures.

    This thorough preparation helps avoid the common pitfalls that make transitioning from PoC to AI production difficult. 

    • AI as a shortcut: In this approach, AI is seen as a quick fix to a specific business problem, often deployed in the form of a proof-of-concept. The goal is typically to demonstrate value quickly, but it may lack long-term scalability, integration, and alignment with broader business goals.

    • AI as a strategic capability: When AI is viewed as a strategic capability, it becomes a core part of an organization's long-term vision. It involves investing in scalable AI infrastructure, embedding AI into key business processes, and continuously improving AI models to drive competitive advantage. This requires organizational AI readiness, ensuring that both the technical and organizational aspects are aligned for AI to deliver sustained value over time.