Introduction

Building an AI solution in-house might sound like the ultimate way to tailor technology to your business. CIOs and CTOs often consider developing AI for knowledge management or intelligent agents internally, thinking it will be cheaper or give them more control. Yet the reality is sobering: most AI projects struggle to deliver value. In fact, only 18 % – 36 % of organisations achieve their expected benefits from AI, and as many as 80 % of AI initiatives end up failing or getting stuck in pilot mode. Many well-intentioned internal AI projects overshoot budgets, miss deadlines, or never reach production – resulting in negative ROI and lost time. Before you green-light a do-it-yourself AI build, it’s crucial to understand the common pitfalls that have tripped up others.

This post will explore the key pain points companies face when attempting to build AI solutions internally, and why outsourcing to “low-cost” partners often doesn’t eliminate those issues. We’ll look at real examples of AI projects that went awry – with time delays, cost overruns, and lessons learnt. Finally, we’ll see how opting for a pre-built AI platform (like MyFalcon’s Knowledge Management Engine) can sidestep these challenges, accelerating your time-to-value in a secure, scalable way. Let’s dive in with a clear-eyed look at the risks of the DIY approach.

The Risks of Building AI In-House

Developing an AI system internally is not like a typical IT project – it’s harder. From finding specialised talent to integrating with legacy systems, in-house AI builds come with multiple challenges. Below are some of the most common (and costly) risks organisations encounter when they go it alone:

• Talent and Hiring Challenges: AI expertise is in high demand and short supply. Many organisations lack the skilled professionals (data-scientists, ML engineers, etc.) needed to develop and maintain AI systems. Hiring a capable team is expensive and time-consuming, and even then you risk gaps in expertise. This talent crunch often leaves internal projects understaffed or reliant on generalist developers who may not fully grasp the complexities of machine learning. The result? Slow progress and higher error rates as teams learn on the job.
• Long Time-to-Value and Project Overruns: It’s very common for in-house AI projects to run over schedule and budget. Nearly half of IT projects exceed their initial budgets, 49 % take longer than expected, and 14 % fail altogether. AI initiatives are no exception – they often start as exploratory pilots that drag on for months without tangible ROI. Building a knowledge-management system or AI agent from scratch can take “several months to a few years” to move from prototype to a reliable production tool. Those delays translate to missed opportunities and frustration for the business. Meanwhile, costs mount: you’re paying salaries, infrastructure, and tools for an extended timeline, sometimes only to end up with a partial solution.
• Integration Complexity and Compliance Burdens: A home-grown AI system rarely lives in isolation – it must plug into your existing data and workflows. Companies frequently struggle to integrate AI with legacy IT infrastructure, which may not handle the heavy processing or data flows AI requires. Connecting diverse data sources (databases, documents, cloud apps) and ensuring they stay in sync is hard work and introduces significant technical debt. On top of that, there are security and compliance requirements: any AI that uses sensitive company or customer data must adhere to data-protection laws and internal policies. Ensuring privacy (e.g. GDPR compliance) and guarding against data leaks becomes an internal responsibility. This burden is non-trivial – failing to handle data properly can lead to massive fines (Amazon was fined $900 M by the EU in 2021, Meta $1 B in 2022) and reputational damage. In short, internal teams must invest heavily in governance, encryption, access controls and audits to manage the risks, often slowing the project.
• Maintenance and Overhead: Let’s say your team finally builds and deploys a custom AI tool – the work doesn’t end there. AI systems require continuous maintenance, updates, and monitoring. Models need retraining on new data, software libraries evolve, and underlying data sources or APIs may change. It’s estimated that simply keeping a software solution running costs ~17 % of its original build cost per year in maintenance. That doesn’t even include adding new features – it’s purely bug fixes, updates, and infrastructure upkeep. Many businesses underestimate this ongoing burden. If your top engineers move on too soon, an in-house AI platform can quickly degrade in performance, leaving users dissatisfied and the system “out-of-date and under-performing” unless you pour resources into upkeep. For a small IT department, this technical debt can become overwhelming.

In summary, building AI internally comes with significant risks: scarce (costly) talent, lengthy development cycles, complex integration and compliance hurdles, and permanent maintenance liabilities. Even large organisations have felt this pain – a Deloitte study found only about a third of companies achieved their AI goals despite heavy investments. The odds are stacked against DIY AI delivering a timely win.

Outsourcing to Low-Cost Partners – Similar Pitfalls Persist

Recognising the difficulties of in-house development, some companies outsource their AI projects to external development firms, often in lower-cost regions. The assumption is that a vendor has the expertise and can deliver faster or cheaper. Unfortunately, many of the same challenges persist (or new ones appear) when outsourcing AI, especially to the lowest-cost bidder. Key issues include:

• Communication Gaps and Misalignment: Offshore or external teams introduce language, cultural, and time-zone barriers that can hinder a project. Miscommunication is a leading cause of failure – studies show about 70 % of outsourcing projects fail due to communication problems. Requirements get misunderstood or lost in translation. One analysis noted how “unclear instructions and cultural differences turned planned features into bugs”, pushing the project 20 % over budget due to rework. Time-zone gaps mean days of delay for simple answers, dragging timelines. Without tight coordination, you risk building the wrong thing and erasing cost savings.
• Hidden Costs and Rework: Low hourly rates often hide extra costs. If the vendor’s quality is poor or they lack context, you might receive deliverables that technically meet the spec but aren’t fit for purpose – requiring substantial rework. Initial savings vanish through “hidden costs of rework, missing documentation, or inflexible architecture”, delaying launch and raising total spend. Onboarding new teams, managing time zones, and currency fluctuations further inflate costs, leading to technical debt and higher long-term expenditure.
• Unclear Ownership and IP Risks: External builds raise questions about intellectual-property ownership. You might end up dependent on proprietary code. Without airtight contracts, algorithms or insights may not belong to you. Sharing sensitive data overseas can also risk IP leakage. High-profile multi-vendor projects, such as Healthcare.gov, suffered from “shifting scope, unclear ownership, and last-minute changes”, ending in public failure. Without one accountable owner, projects descend into finger-pointing.
• Lack of Long-Term Support: Many vendors consider a project “done” at launch and move on, just when bugs appear. Without a maintenance plan, “vendors exit when their sprint ends, not when the product is stable”. Your staff must then learn and fix the orphaned system, negating outsourcing benefits. Budgeting for post-launch support is critical to avoid a short-lived solution.

Outsourcing AI development – especially on cost alone – is no silver bullet. Poor communication, quality issues, hidden costs, IP concerns, and support gaps derail outsourced projects just as easily as in-house ones. Nearly 70 % of companies report at least one failed software-outsourcing attempt. Without strong oversight and alignment, outsourced AI projects face delays, overspending, and unmet objectives.

Hard Lessons: When Internal AI Projects Stumble

• MD Anderson’s $60 Million AI Setback: MD Anderson Cancer Centre spent $60 million over three years on an AI “Oncology Expert Adviser” using IBM Watson, yet never deployed a usable system. Overspending, delays, and mismanagement led auditors to write off the spend – a huge negative ROI.
• Pilot Projects That Never Scale: IDC research shows 88 % of AI pilots never reach production. Out of 33 AI proof-of-concepts, only 4 reach real usage due to unclear ROI, data issues, or lack of expertise.
• Amazon’s Abandoned Recruiting AI: Amazon scrapped its internal AI CV-screening tool after discovering gender bias in recommendations – demonstrating the risk of ethical, legal, and brand issues even in successful tech companies.

These cases warn us: AI projects can overrun and under-deliver due to data hurdles, skills gaps, or poor planning. A different approach can dramatically reduce these risks.

A Smarter Foundation: Pre-Built AI Solutions to the Rescue

• Faster Time to Value: Pre-built engines deploy in weeks, skipping lengthy development cycles. One vendor completes tailored AI projects in ~2 months, versus 12+ months in-house – delivering ROI sooner.
• Lower Talent Barrier: Platforms such as MyFalcon “bridge the skills gap with UIs that don’t require specialist knowledge”. Your current staff can leverage AI without hiring a full data-science team, saving on recruitment.
• Built-In Best Practices: Enterprise-grade products ship with GDPR compliance, SOC 2 and ISO 27001 certifications, centralised connectors, and cloud scalability – saving months of security and integration work.
• Predictable Costs and Support: Subscription fees cover maintenance, updates, and vendor support, reducing IT overhead and budget surprises.
• Proven Results and Faster ROI: A retailer adopting MyFalcon cut excess stock by 30 % and lifted campaign response by 15 %. With a proven track record, the platform de-risks the investment.

Using a pre-built AI knowledge engine means working smarter, not harder. Stand on the shoulders of experts and reach AI-driven outcomes faster – without compromising security or scalability.

Conclusion

Building transformative AI solutions in-house is fraught with hiring headaches, slow development, overspending, integration roadblocks, and maintenance demands. Outsourcing to cheap vendors introduces new challenges in communication, quality control, and support. Unsurprisingly, many AI initiatives end up delayed, over budget, or under-performing.

Leveraging pre-built AI platforms like MyFalcon’s Knowledge Management Engine circumvents those risks, providing speed, stability, and support that are hard to replicate internally. CIOs, CTOs, and COOs should be realistic about DIY challenges and evaluate smarter paths to the same (or better) outcomes.

If these issues resonate, you’re not alone. Explore more AI adoption insights on our blog or book a discussion. Whether you build, buy, or partner, the goal is real business value with minimal risk and maximum ROI – the smart play for any enterprise leader.

Sources

1. Antasha Durbin, “Should We Really Build Our Own Knowledge Management Platform In-House?” Ask-AI Blog.
2. Jonathan D. Gough, “Top 5 AI Adoption Challenges for 2025,” Converge TP Blogs (2025).
3. Evan Schuman, “88 % of AI pilots fail to reach production,” CIO.com (Mar 2025).
4. Twendee, “Why 70 % of Companies Fail in Software Outsourcing” (Apr 2025).
5. Earteza Auvee, “15 Most Common Challenges of Offshore Outsourcing,” Impala InTech Blog (May 2024).
6. Casey Ross, “IBM pitched Watson as a revolution in cancer care. It’s nowhere close,” STAT News (Sept 2017).
7. Robert G. Cooper, “Why AI Projects Fail: Lessons from New Product Development” (July 2024).
8. MyFalcon Case Study – “Why Retailers Can’t Ignore MyFalcon’s Knowledge Management Engine” (July 2025).
9. Multimodal Dev, “In-House AI Development vs. Outsourcing: Which Is Better?”
10. LinkedIn Pulse, “No Post-Launch Plan or Maintenance Structure” (Twendee blog).