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What Skills Make Implementing Company-Specific AI More Difficult Than Procurement?
For finance professionals, understanding AI implementation challenges is crucial. This expert analysis explains why building company-specific AI solutions is significantly more demanding than buying standardized tools, focusing on the specialized skills required for use case identification, design, and effective implementation.
Question
What is more challenging to implement in a company environment?
A. Buying standardized AI solutions, because of the effort to identify and evaluate vendors, select the right vendor, and complete all the necessary contractual and commercial procurement work.
B. Building company-specific AI solutions because of the additional skills required to identify the right use case and to design and effectively implement the solution.
Answer
B. Building company-specific AI solutions because of the additional skills required to identify the right use case and to design and effectively implement the solution.
Explanation
The correct answer is B. Building company-specific AI solutions is more challenging because it involves a fundamentally higher level of strategic, technical, and operational complexity compared to procurement.
While buying standardized AI solutions presents procedural hurdles, these are manageable within existing business functions. Building custom AI, however, requires creating entirely new capabilities from the ground up.
Why Building is More Challenging Than Buying
- Strategic and Use Case Identification: The first and most critical step in building a custom solution is identifying a business problem that is both valuable to solve and technically feasible for AI. This requires a rare combination of deep domain expertise (e.g., in financial services), business acumen, and a sophisticated understanding of AI capabilities and limitations. A mistake at this stage can lead to a costly failure. In contrast, when buying a tool like Microsoft Copilot, the use cases are broad and pre-defined (e.g., improving productivity in writing emails).
- Specialized Talent and Skills: Building custom AI demands a dedicated team of highly skilled and expensive specialists who are in short supply. This includes data scientists, machine learning (ML) engineers, AI architects, and MLOps professionals. A company must be able to attract, retain, and manage this talent. Buying a solution relies on the vendor’s talent, not your own.
- Data Requirements: Custom AI solutions require vast amounts of high-quality, relevant, and often proprietary data for training and fine-tuning. The process of collecting, cleaning, labeling, and governing this data is a massive undertaking in itself. Standard solutions are pre-trained and ready to use, bypassing this significant hurdle.
- Development and Integration: The technical process of designing the AI model architecture, training it, and then integrating it seamlessly with a company’s existing legacy systems is complex and resource-intensive. This involves navigating technical debt and ensuring the solution is scalable and secure.
- Ongoing Maintenance and Optimization: A custom AI model is not a one-time project. It requires continuous monitoring for performance degradation (model drift), periodic retraining with new data, and ongoing optimization. This creates a long-term operational burden. When buying a solution, the vendor is responsible for all maintenance and updates.
In summary, buying an AI solution is primarily a procurement and configuration challenge, handled by existing business departments. Building a custom AI solution is a strategic, technical, and operational challenge that requires creating new, specialized capabilities across the entire organization.
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