Top 6 Considerations for
Adopting AI

AI adoption presents a paradox—while technical implementation has become more accessible, the strategic landscape has grown increasingly complex. The explosion of AI models, hosting options, tools, and frameworks has raised market expectations, making it more challenging for new entrants to compete. Companies must navigate this evolving terrain carefully to avoid costly missteps.

In Top 6 Considerations for Adopting AI: A First-Hand Perspective, Darko Vukovic, CEO of PolyAPI, shares key insights from his extensive experience in API and integration technologies. He provides a pragmatic guide to help businesses successfully integrate AI into their operations, focusing on strategies that balance cost, performance, and control.

Key Challenges in AI Adoption

Many companies struggle with where to begin. The market is saturated with pre-built AI models and competing technologies, creating analysis paralysis that slows down decision-making. At the same time, rising customer expectations mean that AI must deliver immediate, high-impact results—there’s no room for experimentation that “sort of works.” Businesses attempting to skip foundational learning often face costly inefficiencies and misaligned strategies.

Furthermore, AI isn’t as easy to reverse-engineer as traditional software. Early adopters hold a first-mover advantage, refining their capabilities while new entrants struggle to catch up. Unlike APIs or UIs that can be copied, AI models operate as black boxes, making it difficult to replicate another company’s progress.

A Pragmatic Approach to AI Adoption

To avoid missteps, companies should start with well-defined, achievable use cases. The white paper outlines a step-by-step approach that begins with leveraging general-purpose AI models before progressing to fine-tuned or custom solutions. The key to success lies in contextual reasoning—rather than expecting AI to “know” everything, businesses should provide structured context for each task, enabling more effective and scalable AI solutions.

The paper further explores how to select the right AI model, weighing the benefits and limitations of pre-built versus custom-trained models. It also delves into AI provider selection, hosting considerations, and privacy concerns, offering actionable insights to help organizations maintain flexibility while ensuring compliance.

5 Takeaways from the White Paper:

1️⃣ Start Small, Iterate Quickly – Focus on achievable AI use cases before scaling up.
2️⃣ AI Must Work Within Context – Success depends on structured, just-in-time context, not expecting AI to “know” everything.
3️⃣ Pre-built vs. Custom Models – General-purpose models offer quick wins, while custom models require significant investment.
4️⃣ Market Expectations Keep Rising – AI solutions must deliver value immediately—”almost good enough” is no longer acceptable.
5️⃣ Strategic Flexibility is Key – Choosing the right AI provider, hosting model, and data privacy approach ensures long-term sustainability.