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The CIO’s Guide to Avoid AI-Washing: 9 Tips for Vetting AI Vendors and Solutions

Written by

ODG Team Contributor

Published on

18 Jun 2024

As more businesses turn to AI to gain a competitive advantage, CIOs play a pivotal role in evaluating AI vendors and solutions. However, with the proliferation of AI hype and exaggerated claims, CIOs must be vigilant to avoid falling victim to “AI-washing.”¹

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How Soon Is Too Soon With AI?

So far, AI has proven to be useful for many users around the world. However, it’s not yet a hundred percent reliable. CIOs must actively navigate the complex landscape of AI, explore new solutions, and avoid overhyped claims or AI-washing by evaluating their vendors and solutions effectively.

AI-washing is the practice of embellishing AI products or services without substantiating their capabilities. In a world flooded with buzzwords and inflated promises, CIOs must discern between genuine AI solutions and mere marketing gimmicks.

B2B AI products are full of promises but filled with challenges and risks of early adoption. So here are insights from industry experts and thought leaders to help you make informed decisions and drive business value with technology.

Practical Tips for Vetting AI Vendors and Solutions

To navigate this complex landscape effectively, here are nine practical tips for vetting AI vendors and solutions:

1. Count on Humans for the Human Touch

AI solutions often apply to every aspect of the business. From marketing automation to sifting through job applicants, lead scoring, cross-selling, and every administrative task you can think of.

However, human input is still required to maintain an empathetic approach. AI is still young and still relies on external input. It still needs to learn from more data before it becomes fully reliable. Although technology can automate many aspects of your company’s daily operations, it can never replace the level of insight that a human professional can bring to the table.

So, the best way to implement AI tools is to add a human touch to the process. Even with all its capabilities and capacity, AI still needs to be run by humans. This ensures that the data used is verified and biases are addressed to avoid misinformation and unwanted prejudice.

2. Define Your Business Needs and Limitations

Before embarking on the vendor selection process, clearly define your organization’s specific business needs and objectives. Understanding your requirements will enable you to assess whether an AI solution aligns with your strategic goals. Furthermore, it will help you identify which specific AI solution you need.

Remember that AI is best for administrative tasks and repetitive processes. This will help improve your capacity, reducing time and resource allocation on small tasks. However, leaving the decision-making process and critical responsibilities to your workforce is best to ensure accurate and wise choices.

3. Scrutinize Vendor Claims

Don’t take vendor claims at face value. Instead, dive deep into the technical details and capabilities of the AI solution. Look for evidence-backed results, case studies, and real-world implementations that demonstrate the effectiveness of the technology in solving relevant business challenges.

If you want to take it further, you may contact other businesses that have implemented the software and gather information directly from them. By recognizing their capabilities, you can determine their reliability, ensure compliance with laws and regulations, and maintain an excellent public image.

Online reviews can also help you understand whether the selected tool will fit your company well. Check if they’re associated with any issues in the past. This ensures you’re choosing the right product that is free from any allegations and controversy. Ultimately, it will give you confidence in implementing their technology within your organization.

4. Evaluate Data Quality and Accessibility

AI is only as good as the data it operates on. So, don’t forget to assess the quality, completeness, and data accessibility required for the AI solution.

Ensure the vendor has robust data governance practices to maintain data integrity and compliance with regulatory requirements. Again, compliance is essential. If the AI passes the necessary compliance requirements, it will likely be a good fit.

5. Assess Model Transparency and Explainability

Transparency and explainability are crucial factors in AI adoption, particularly in regulated industries. Seek vendors that provide transparent insights into their AI models, algorithms, and decision-making processes.

Understand how the AI solution arrives at its conclusions and whether stakeholders can interpret and trust it. This includes judging the logic behind the process. So, don’t forget to ask how and why their software gives the results it does. To ensure success in implementing AI, you need to be on top of the process and learn how it operates. You need to ensure that human biases are not perpetuated within the AI tool.

6. Consider Scalability and Integration

As your business evolves, scalability and seamless integration become paramount. Evaluate the scalability of the AI solution to accommodate the growing data volumes and user demands. Additionally, assess its compatibility and ease of integration with your existing IT infrastructure and applications. Think about how you can use the tool for different parts of your operations.

Can you integrate the different processes and tools you already have?
Can you use it for multiple departments and various tasks?
Is it easy to integrate into your existing workflow, or will it disrupt productivity?
Will you be able to get your return on this investment?

An effective tool will help you lighten the administrative load. If this AI implementation significantly improves your processes, it’s worth having.

7. Prioritize Security and Privacy

With the increasing prevalence of data breaches and privacy concerns, security should be a top priority when vetting AI vendors. Ensure your vendor employs robust security measures to protect sensitive data and comply with privacy regulations such as GDPR and CCPA.

Your AI tools must be safe for business use. If you adopt too early, there may be risks associated with new programs. Adopting new technologies is generally a competitive advantage, but be cautious of the challenges—you might end up becoming a guinea pig for software testing.

Instead, get a pre-tested tool. Free trials are great only if the product is efficient. This way, you’ll be safe from underdeveloped tools and maintain data security and privacy for your data, clients, and customers.

8. Seek Proof of ROI and Business Impact

The success of an AI investment hinges on its ability to deliver measurable results and tangible business impact.

Request concrete evidence of ROI from the vendor, including metrics such as cost savings, revenue growth, productivity gains, or improved customer satisfaction. This will give you the insight regarding ROI and business impact you need for an AI overhaul and stakeholder buy-in.

9. Engage in Proof of Concept (POC) or Pilot Projects

Since AI software is rather new, there may be little evidence. So, if you believe in the tool’s capabilities but cannot find evidence, consider a pilot test. Implement it for a few roles or a single department, then go from there.

Mitigate risks by conducting proof of concept or pilot projects to evaluate the AI solution in a real-world environment. This hands-on approach allows you to assess its performance, usability, and fit for your organization before making a full-scale commitment.

AI IS A SUPPORTING TOOL, NOT A REPLACEMENT

Competition might push you to demand fast results. You may not want to simply sit around and watch the AI wave pass as it benefits countless other organizations. However, AI should aid your business, speed things up, and refine your processes, not replace your human efforts, creativity, intuition, and decision-making capabilities.

AI is a steppingstone to continuous digital transformation. But don’t forget to track your progress, question discrepancies, dig into the claims made by AI vendors, verify customer satisfaction, and assess their reputation.

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