The 5 Hurdles in Building Generative AI Applications (And How to Overcome Them)

GenAI pilots are popping up in every corner of the org chart—product, marketing, finance, ops. The board wants updates. Your peers are posting wins. Budgets are approved. Tools are bought.

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And yet, very few of these pilots make it to production.

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But if you're leading AI or data, you’ve probably hit the same wall: the model is ready, the business is excited... but things stall.

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Sometimes it’s hallucinations. Sometimes it’s data that doesn’t connect. Sometimes it’s orchestration pain so messy no one wants to touch it.

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The truth is that most GenAI projects don’t fail because of bad models. They fail because the data isn’t ready.

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In this article, we’ll walk through the five most common hurdles of building Gen AI apps and show how each one ties back to a core issue: an incomplete or outdated data strategy.

Why every boardroom is chasing the genAI rush

60% of companies that already use AI now run generative models in at least one function. Board members read that number and feel the FOMO instantly. You can see why: a single model can sketch logo variations, draft production-ready code, and nudge every customer with a perfectly timed offer before your latte cools.

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And the payoff is already here:

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• Klarna’s GPT-powered assistant handled 2.3 million chats in a month. That’s the equivalent of 700 agents. Resolution time dropped from 11 minutes to 2. The company expects a $40 million profit bump in 2024
• Duolingo’s GPT-4 tutor, part of its premium “Max” tier, helped boost full-year revenue guidance to nearly $1 billion, surprising even Wall Street
• Morgan Stanley’s internal GPT tool? 98% advisor adoption. It turns hours of doc-hunting into minutes. Reps spend more time with clients instead of buried in PDFs
• Coca-Cola’s “Create Real Magic” campaign shrank iteration cycles from weeks to days. Creative teams now test global campaigns at the speed of TikTok trends

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And this isn’t a passing trend. McKinsey says generative AI could deliver up to $4.4 trillion in annual productivity gains. That’s more than the GDP of Germany.

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So yes, every C-level exec you know is sprinting toward a pilot.

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But here’s the thing they won’t admit in the town hall:

• They’ve started building
• They’ve spent budget
• They’ve picked vendors

And still most haven’t shipped a thing.

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So what’s stopping them from deploying apps that 2X or 3X team output?

The five challenges of building genAI apps

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Every generative AI failure story, from colossal hallucinations to pilots that never got promoted, comes back to one of five root causes. And no, it’s not model tuning or GPU access. It’s the boring stuff—the stuff no one wants to talk about.

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Messy data. No governance. Team silos. Overkill use cases. Misaligned goals.

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Let’s unpack them. Before getting GenAI apps to production, you’ll want to see them coming.

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1. Data quality and integrity: garbage in, gibberish out

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“Data needs to be considered and intertwined with AI and machine learning to really unlock meaningful value. data only becomes powerful when we are able to do that. And conversely, it reaches its full potential when we have high quality data.”

~ Maddie Daianu, Senior Director, Data Science & Engineering, CreditKarma
Driving Financial Freedom with Data

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Your model can’t out-think bad inputs. Poor data quality, such as missing fields, conflicting values, hidden biases, and outdated information, can severely compromise GenAI outputs.

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Zillow thought its pricing model was accurate enough to bet capital on. It wasn’t.

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The company used its Zestimate algorithm to fuel an automated home-flipping business. But the data behind those estimates lacked consistency and context. The result: overvalued homes, flawed predictions, and a complete shutdown of the business unit.

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They lost over $500 million. 

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Deloitte rightly warns that missing values, stale records, and biased samples quietly erode GenAI accuracy, with Gartner estimating that poor data quality costs the average business $12.9 million per year.

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Your model can’t reason past what it’s fed. And you can’t course-correct after deployment without strong data hygiene upfront.

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Also read: Data Quality Management Overview: Definition, Business Benefits, and Tools 

2. Data silos and fragmentation: your model is flying blind

When finance stores numbers in SAP, marketing hoards them in HubSpot, and ops still loves CSVs, GenAI sees only slivers of reality. 

AI systems don’t make intuitive leaps. If the data isn’t there, the model won’t infer it.

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This was one of the root issues behind Healthcare.gov’s early collapse. Data lived across federal and state agencies with no shared structure. The platform couldn’t reconcile identities, eligibility, or application states. Technical debt compounded. Trust eroded. Political fallout followed.

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If your data is fragmented across ERP, CRM, and BI tools, GenAI models are flying half-blind.

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Integration is expensive. But flying blind is worse.

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Also read: Top 10 Data Integration Tools in 2025 [Break Data Silos] 

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3. Lack of data governance: compliance roulette

Models leak what they learn. Without lineage, access controls, and retention rules, you’re one subpoena away from an existential headache. 

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Take Replika’s chatbot that was trained on user interactions with no effective age checks. Italy’s privacy watchdog fined them €5 million for non-compliance. 

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The issue wasn’t just consent. It was the absence of lineage. No clear policies around what data the model could retain, and no audit trail for regulators.

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Basically, no data governance.

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Deloitte calls this the “explainability gap”—when you can’t defend why a model made a decision, you're not ready for regulated markets.

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Enterprise-grade GenAI needs traceability, access control, and defensible retention policies from day one.

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Also read: Best Data Governance Tools in 2025 

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4. Overcomplicating the solution: because a fancy model looks cool

IBM’s Watson for Oncology promised AI-powered treatment recommendations. What it delivered was often unsafe or irrelevant.

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Doctors lost confidence. Hospitals backed out. IBM eventually wrote it down as a strategic misstep.

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Why? Because the model wasn’t grounded in real clinical data. And because the use case demanded precision the system couldn’t deliver.

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Not every workflow needs generative reasoning. Start with the problem. Validate the data. Then pick your tools.

5. Misalignment with business needs: the silent killer

Microsoft’s Tay chatbot learned from user interactions on Twitter. It launched with buzz. It was pulled in less than 24 hours on the grounds that it was racist.

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The issue was the absence of a clear purpose. No business case. No risk boundaries. And no alignment with customer or compliance expectations.

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Plenty of GenAI demos impress in the boardroom. Few deliver impact in the wild.

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Before you build, ask:

• What does this change for the user?
• How will we measure success?
• And what happens if it gets it wrong?

The underlying issue: poor data strategy

Notice a pattern? All five blockers trace back to bad data strategy—silos, quality lapses, and zero governance. 

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Zillow didn’t lose $500 million because their model was unsophisticated. Healthcare.gov didn’t collapse because the dev team didn’t care. Watson didn’t fail because IBM lacked budget or brains.

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These projects failed because the data wasn’t ready. Or worse, no one knew it wasn’t ready until it broke things.

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Better AI isn't about more data; it is about the quality of data and its connectivity. We have assigned accountability to make sure that we just don't keep on saying the quality is bad, but keep improving it.

~ Anindita Misra, Global Director of Knowledge Activation & Trust, Decathlon Digital
How Decathlon uses data to optimize in-store operations 

You can’t build reliable GenAI workflows on brittle foundations. You need clear ownership, integration across sources, governance baked in, and visibility across the stack.

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Right now, most enterprises don’t have that. So it’s no surprise that Thomson Reuters calls data fragmentation “the silent AI killer.”

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Also read: How Enterprises Can Connect LLMs to Their Data

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That’s the gap 5X closes. Let’s find out how.

Why 5X is the shortest path from messy data to GenAI ROI

Once you’ve identified the data gaps holding back your GenAI roadmap, you’ll need more than a stopgap. You’ll need infrastructure that removes silos, standardizes workflows, and integrates with the AI tools your teams are actually using. 

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That’s where 5X comes in.

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5X starts with unified data management. The platform connects to over 500 sources and brings them into one governed warehouse, so instead of chasing data across SaaS platforms, spreadsheets, and internal APIs, your team has query-ready access to marketing events, ERP records, and behavioral signals in one place. 

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As a result, models work with full context from day one, and business leaders stop waiting weeks for “one last export.”

1. Unified data management

5X starts with unified data management. The platform connects to over 500 sources and brings them into one governed data warehouse, so instead of chasing data across SaaS platforms, spreadsheets, and internal APIs, your team has query-ready access to marketing events, ERP records, and behavioral signals in one place. 

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As a result, models work with full context from day one, and business leaders stop waiting weeks for “one last export.”

2. Seamless integration; plug straight into your GenAI stack

Integration is equally seamless. Because 5X normalizes data upstream, you don’t need to write glue code to connect to LangChain, Pinecone, Milvus, or any other vector store. You drop in clean tables, and they work.

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That’s why 5X customers have shipped production-grade GenAI use cases in just a few weeks.

3. Built-in governance

Where most platforms fall apart is governance. 5X handles this from the ground up, with column-level lineage, role-based access, and audit-ready logs across every pipeline. 

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This means your compliance team can breathe, and your models can go live without legal blockers.

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Also read: Why Data Governance Matters and How to Do It Right 

4. Orchestration without the migraines

5X also gets orchestration right. You get a smart scheduler and a semantic layer that automates cross-system coordination. Engineers stop chasing DAG failures and go back to tuning prompts and building value.

5. The 48-hour jumpstart for board-level proof

And finally, there’s the Jumpstart. GenAI only works when projects tie to real KPIs. 5X’s 48-Hour Jumpstart delivers exactly that—a complete use case, powered by your own data, deployed in two days. It’s not a prototype. It’s production-ready proof. 

Don’t build GenAI on broken data

GenAI success doesn’t hinge on the model, it hinges on the data. A weak data foundation derails even the most promising pilots. 

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Before investing in another LLM integration, step back and audit your infrastructure. Are your sources unified? Is your data governed? Can your systems support a full GenAI workflow without duct tape?

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If the answer is “not yet,” that’s exactly what 5X is built for. We help you clean, integrate, and operationalize your data fast.

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