5 Must-Know Tips For Entrepreneurs To Choose The Right AI Vendor

Forbes

9 hours ago

Dr. Son Nguyen is the cofounder & CEO of Neurond AI, a company providing world-class artificial intelligence and data science services.
Choosing the right AI vendor can make or break your business' digital transformation. Running Neurond for nearly a decade, I've seen countless vendor selection processes, both successful partnerships and costly mistakes. In fact, many entrepreneurs fall into common traps: overpaying for fancy but impractical solutions, underestimating integration challenges or trusting vendors that don't truly understand their needs. Consequently, they may face expensive setbacks, wasted resources and missed opportunities.
So, how do you avoid these mistakes and make a smart choice?
Today, I'd love to share insider secrets that help you select the ideal AI partner for your business without the trial and error.
1. Don't Just Ask About Speed, Ask About Data
One of the most common questions I've received is about how accurate our models are or how fast they can process information. These metrics are certainly important, but they're only part of the story. In reality, the success of any AI implementation relies on its foundation: the data.
Almost no one asks the right questions about data requirements, and that oversight can be a big mistake in the selection process. An AI model is only as good as the data it's trained on. Your vendor might promise 95% accuracy in a demo, but if they require data you can't provide in the right volume, quality or format, then this rate becomes meaningless.
To make everything clear from the beginning, have examples of the amount of data needed, its quantity and the formats. This data audit will clarify whether you can meet their demands with your existing data or if you'll face a costly data collection project. Also, as an AI partner, we always provide an exit strategy, letting clients easily extract their data and any models trained on their data if the partnership ends. If possible, require the full transparency of what happens to your data before it hits their models.
2. Find AI Partners Who Solve With You
When meeting with clients, we don't treat them as customers and try to sell them AI solutions, but as partners for their business. In other words, the perfect AI vendor should become a temporary team member, working alongside you to ensure success. A transactional relationship might get you a product, but a true partnership will get you results. This distinction is crucial because implementing AI is rarely a simple process. It requires collaboration, adaptation and a shared commitment to solving complex problems.
Review detailed case studies that highlight challenging deployments to gain invaluable insight into their problem-solving capabilities and resilience. Moreover, you must meet the engineers working on your project, not just the sales team. With their expertise, communication style and understanding of your specific needs, they will be hands-on with your data and systems. This direct interaction allows you to assess their technical prowess and determine if their approach aligns with your operation.
3. Understand The Full Cost Picture
Many AI solutions appear affordable at first until hidden costs start adding up. Failing to account for these additional costs can turn a promising investment into a financial drain.
One major factor to consider is the cost of running large language models (LLMs). Most LLM providers charge based on 'tokens,' meaning the more input you send and the more extensive the output generated, the higher your bill. Plus, processing non-English languages or specialized characters can further increase token counts. If you plan to fine-tune or customize an LLM with your specific data, be prepared for additional costs related to compute time.
Beyond LLMs, the underlying infrastructure presents another cost component. AI, especially LLMs, demands computational resources. This translates to significant expenses for high-performance GPUs, specialized AI hardware and servers. Whether choosing cloud-based services like AWS, Azure or Google Cloud or building your own on-premise infrastructure, these compute expenses are directly scaled with your usage. Be clear about the associated costs to avoid unexpected budget overruns down the line.
4. Trust AI Vendors With Emerging Tech
In AI, standing still is the fastest way to fall behind. The industry changes monthly, sometimes weekly. Hence, a vendor without strong momentum will hold your business back.
If I were you, I'd look at their research culture, sorting those actively publishing research, presenting at academic conferences and contributing to open-source AI projects. A vendor engaged with the broader AI community is not just a consumer of technology but a creator. This shows they're pushing boundaries rather than relying on outdated tech.
Equally important is their talent retention. High turnover in AI teams often signals deeper issues, including poor leadership, a lack of challenging projects or falling behind competitors. Question about team stability and how they keep top researchers and engineers engaged.
5. Considerable Challenges Of Integration
Remember that an AI product's lifecycle is not a one-time event that ends at deployment; it's a continuous process of refinement, updates and customization. The complexity rarely relies on the model itself; it's found in the surrounding infrastructure, security protocols and crucial change management processes.
In these cases, your business must proactively identify outdated systems that might conflict with AI implementation before deployment begins. A forward-thinking AI vendor should be a partner in this process, helping you spot potential roadblocks and plan for necessary upgrades. Also, AI success depends on CI/CD pipelines that can handle frequent updates and deployments. Ask your vendors how their solution integrates with CI/CD practices. When you fine-tune models or as the vendor releases new versions, you can test and deploy them efficiently and safely without disrupting your operations.
Conclusion
Choosing an AI service provider has never been an easy task. Not just depending on technical or financial situation, you have to find a true partner who aligns with your business goals, understands your data needs and scales with your growth. Focus on data transparency, collaboration and integration challenges. With these hidden tips in mind, you'll be well-equipped to choose an AI service provider that meets your current demands and empowers your business to grow.
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