If so, you’re not alone. Many organizations ventured down this ambitious path in 2024, driven by the promise of AI’s transformative potential. However, building an AI solution internally often proves more challenging, time-consuming, and expensive than anticipated. Before you commit to the next phase of your AI journey, it’s worth examining the common pitfalls and risks that can derail even the most well-resourced teams.
The Hidden Challenges of Building AI Internally
- Normalization of Data: Raw data from different sources rarely comes in a usable format. Normalizing this data so that it can be processed effectively by AI models is no small feat. You’ll need to address inconsistencies, handle missing information, and ensure all data conforms to a standardized structure – a process that requires significant time and technical expertise.
- Enrichment of Data: AI models are only as good as the data they’re trained on. To make your AI solution effective, you’ll likely need to enrich your data with additional context or integrate external data sources. This requires expertise in data acquisition and the tools necessary to enhance data quality.
- Vectorization of Data: Converting data into numerical formats, or vectorization, is essential for machine learning models and Large Language Models (LLMs). This step involves mathematical transformations that demand deep knowledge of algorithms and a clear understanding of your data.
- Just Getting Data into a Data Warehouse Isn’t Enough: Many organizations believe that once their data is centralized in a data warehouse, they’re ready for AI. However, simply storing data doesn’t prepare it for analysis or AI applications. You still need to transform, normalize, and enrich that data to make it usable—steps that often require specialized tools and expertise.
- Permission Control for Data Access: One overlooked area is ensuring strict permission control for data access. Without proper controls, your AI solution risks exposing sensitive information to unauthorized users, potentially leading to costly security breaches and compliance violations.
- Predictable Costs per Inference: AI systems often use tokens or compute resources during operation. Without proper oversight, costs can quickly spiral out of control. Predictable pricing for each inference—each time your system generates a result—requires meticulous cost management and architectural decisions.
- Model Training with Your Data: To achieve meaningful results, AI models need to be trained on your specific data. This involves tailoring the model to understand your unique business processes, language, and context. But training models is resource-intensive and requires ongoing refinement as your data evolves.
- Choosing Between Machine Learning and LLM Approaches: One of the biggest strategic decisions is whether to rely on traditional machine learning models or adopt LLMs. Each approach has trade-offs in terms of flexibility, scalability, and cost, and making the wrong choice can delay your project significantly.
- LLM Flexibility as New Capabilities Emerge: AI is a rapidly evolving field. LLMs are constantly gaining new capabilities and ensuring that your solution remains flexible enough to adapt can be an ongoing challenge. Staying ahead requires constant monitoring and updates to your system.
- Data Security and Governance: Data security isn’t optional. Organizations that fail to meet stringent security and governance requirements risk compliance violations, reputational damage, and financial penalties. Building a secure AI solution requires robust encryption, access controls, and audit trails.
- Ongoing System Support: Building an AI solution is only the beginning. Maintaining and evolving the system over time requires a dedicated team to manage updates, fix bugs, and optimize performance. Without this commitment, your AI solution may quickly become obsolete.
The Alternative Path: CrushBank
Instead of taking on all these challenges yourself, there’s a smarter way forward. CrushBank offers an AI solution that is purpose-built to handle the complexities of modern business needs.
- Data Normalization and Enrichment: CrushBank automates the process of preparing and enriching your data, ensuring you get accurate and actionable insights.
- Seamless Integration: With CrushBank, you don’t have to worry about vectorization or managing LLM flexibility – it’s all built into the platform.
- Secure by Design: Permission control, data governance, and security are at the core of our solution.
- Predictable Costs: With transparent pricing, CrushBank delivers predictable costs per inference, so there are no surprises.
- Focus on Results: CrushBank lets you skip the lengthy development cycle and start achieving measurable ROI right away.
In 2024, the race to implement AI internally might have seemed like the logical choice, but as we enter 2025, it’s clear that the smarter path lies in leveraging a proven, turnkey solution. Let CrushBank handle the complexities of AI so you can focus on driving your business forward.