As generative AI continues to revolutionize industries, organizations are eager to integrate this transformative technology into their operations. While the temptation to have in-house IT teams build custom AI solutions is strong—especially when those teams are enthusiastic about "learning on the fly"—this approach can lead to significant pitfalls. Hidden complexities in data management, security, reporting, cost control, governance, and more can turn well-intentioned projects into costly misadventures.
Empowering internal IT teams to develop AI solutions seems advantageous:
However, these perceived benefits often overshadow the risks and challenges inherent in building AI systems without adequate experience.
Volume and Variety: AI systems require vast amounts of data. Managing large datasets, especially unstructured data, demands robust infrastructure and expertise.
Data Quality: Poor-quality data leads to inaccurate models. Gartner estimates that poor data quality costs organizations an average of $12.9 million annually1.
Data Integration: Combining data from disparate sources is complex. Inconsistent formats and siloed databases can hinder AI development.
Data Breaches: Handling sensitive data increases the risk of breaches. According to IBM, the average cost of a data breach in 2023 was $4.45 million2.
Compliance Issues: Regulations like GDPR and CCPA impose strict data handling requirements. Non-compliance can result in hefty fines.
Model Security: AI models themselves can be targets for attacks like adversarial examples or model inversion.
Black Box Models: AI systems, especially deep learning models, can be opaque. This lack of transparency complicates decision-making and accountability.
Regulatory Compliance: Industries like finance and healthcare require explainable AI for compliance. Failing to provide adequate reporting can lead to legal issues.
User Trust: Stakeholders may distrust AI outputs without clear explanations, reducing adoption and effectiveness.
Overruns: Projects often exceed budgets. A study by McKinsey found that 17% of IT projects go so badly that they threaten the existence of the company3.
Hidden Expenses: Costs for cloud computing, data storage, and talent acquisition can escalate quickly.
Maintenance: Ongoing support and updates add to the total cost of ownership, often underestimated in initial planning.
Lack of Policies: Without established AI governance frameworks, projects can drift without clear objectives or oversight.
Ethical Considerations: Issues like bias and fairness require careful governance. A lack of attention here can lead to public relations crises.
Strategic Alignment: Projects may not align with business goals, leading to wasted resources.
Specialized Expertise Needed: AI development requires skills in machine learning, data science, and software engineering.
Learning Curve: Allowing teams to "learn on the fly" delays project timelines. According to a report by O'Reilly, only 15% of companies consider themselves mature in AI practices4.
Turnover Risks: Investing in training can be lost if employees leave, taking their newfound expertise elsewhere.
A mid-sized financial services company decided to develop an AI-powered risk assessment tool in-house. The IT team, eager to build their skills, embarked on the project with minimal prior AI experience.
A survey by Gartner revealed that 85% of AI projects fail to deliver on their objectives due to issues like data quality, lack of expertise, and poorly defined use cases5.
Platforms come with:
If an organization still opts for in-house development, mitigating strategies include:
While the ambition to build custom AI solutions in-house is commendable, the hidden complexities and risks often outweigh the benefits. Data management challenges, security vulnerabilities, reporting inadequacies, cost overruns, governance issues, and talent gaps can derail projects and result in significant financial and reputational damage.
Leveraging established AI platforms mitigates these risks by providing expertise, robust infrastructure, security, compliance, and scalability. This allows organizations to focus on their core competencies and strategic objectives rather than reinventing the wheel.
In the rapidly evolving landscape of AI, time is of the essence. Organizations that recognize the pitfalls of "learning on the fly" and opt for more secure, efficient solutions will be better positioned to succeed and thrive.