Despite record investment in AI, many companies are still struggling to deliver business value from their initiatives. The promise is real, but so is the frustration.
You might have an AI use case scoped. Maybe even a model trained. But putting it into production? Scaling it? Proving ROI to leadership?
That’s where things fall apart. Industry estimates suggest that as many as 70-85% of AI projects never reach successful deployment, a failure rate considerably higher than that of traditional IT projects. Gartner likewise predicts a large chunk of experimental AI initiatives will be abandoned after proof-of-concept.
It’s common for AI projects to stall. Not because the technology isn’t ready, but because the organization isn’t. Data bottlenecks, siloed teams, governance gaps, and immature infrastructure make it difficult to move from experimentation to execution.
The good news? With the right architecture and operating model, AI doesn’t just scale, it transforms how your business works.
Nearly every organization today claims to be “doing AI.” They’re running proofs-of-concept, fine-tuning models, and launching GenAI pilots. But few are actually deploying these solutions at scale.
Why? Because most companies underestimate the complexity of operationalizing AI in the enterprise.
Consider these common blockers:
1. Data Readiness Isn’t There
AI is only as strong as the data behind it. And most enterprise data ecosystems weren’t built with AI in mind.
Teams are dealing with:
This slows everything down and undermines trust in the outputs.
2. Model Accuracy Isn’t Enough
Data science teams often optimize for accuracy. But in the real world, that’s not the only metric that matters. While model accuracy remains a key metric, the focus has shifted towards a broader concept of AI trustworthiness and reliability. An accurate model that is not trusted by users or is not resilient in the face of real-world complexities is of little
Key considerations now include models that are:
An accurate model that never makes it into production delivers zero business value.
3. Governance Is an Afterthought
AI introduces new layers of risk: regulatory compliance, IP leakage, ethical concerns, explainability gaps…
Without a governance model that covers data , models and prompts, organizations face mounting exposure – and internal resistance to AI adoption.
The key pillars of modern AI governance include:
4. Teams Are Out of Sync
AI projects often fail not because of bad models, but because of poor collaboration. Data scientists, IT, analysts, and business stakeholders operate on different timelines, tools, and success metrics.
This misalignment shows up as:
Smart companies fix the people problem before throwing more tech at it.
At Concord, we help organizations overcome these hurdles by building AI foundations that are scalable, governed, and enterprise-ready.
That’s why we partner with Databricks.
Databricks offers a unified platform that integrates data engineering, ML development, GenAI workflows, and governance – all in one collaborative environment It solves the foundational problems that cause most AI projects to stall:
Unified Data + AI Stack
Databricks breaks down silos between data engineering, analytics, and ML by unifying them on a single lakehouse platform. This reduces latency, duplication, and complexity so teams can move faster with confidence.
Production-Grade ML + GenAI
Databricks simplified the full AI lifecycle, from data prep to deployment.
These capabilities help teams move beyond notebooks and prototypes and build real, reliable applications.
Governance Built for AI
Governance isn’t bolted on, it’s baked in.
This makes Databricks especially valuable for regulated industries like healthcare, finance, and retail.
Developer Experience That Scales
AI systems are software systems. And Databricks treats them that way.
This reduces friction and allows organizations to treat ML/AI like any other part of their product stack.
Choosing the right platform is critical, but it’s only the beginning.
Concord helps enterprise teams operationalize AI by embedding the right practices, workflows, and metrics so you don’t just build models, you build momentum.
Our AI and MLOps experts help you:
We’ve worked with some of the largest companies in retail, finance, and healthcare to move AI out of the lab and into daily decision-making.
If your team is stuck in the pilot phase, or unsure how to scale AI responsibly, you’re not alone. We’ve helped clients at every stage of the journey – from data readiness assessments to full-scale MLOps implementations.
Together with Databricks, we’ll help you unlock your AI strategy and get results you can measure.
Not sure on your next step? We'd love to hear about your business challenges. No pitch. No strings attached.
