The Rise of Generative AI and the Risks of AI Washing

Since the introduction of ChatGPT by OpenAI last year, the explosion of interest in generative AI has been immense. There are a lot of opportunities but also a lot of risky behavior, including from tech companies. In February, the FTC warned about marketing false or unsubstantiated claims. And more recently, President Biden issued an executive order to set AI safety and security standards. This is why two buzzphrases are increasingly being heard throughout Silicon Valley and the global tech and investor community. The first is AI washing, which refers to the practice of a vendor exaggerating or misleading in marketing products and services that claim to have AI when it is non-existent or plays a very minor role. The second is explainable AI, which we will jump into later.

The Importance of Ethical AI Practices

Today, enterprises have endless possibilities to use more developed AI capabilities, with the ultimate goal being to improve productivity, reduce errors and risk, and become more operationally efficient. There’s also the opportunity to improve customer experiences, enhance personalization, and unlock new revenue opportunities in today’s hyper-competitive business environment. These are often tied to maturing digital transformation strategies. However, AI washing damages trust with customers, prospects, and potential investors. Technology startups may do this to get venture capital funding, buy more time to develop AI capabilities and acquire talents such as data scientists, engineers, and mathematicians. However, this can lead to broader mistrust around AI capabilities and ruin any brand equity you’ve built, even as AI products, services, and features become more sophisticated.

While it’s true even some data scientists who built the algorithms behind AI products, services, and features can’t explain how it came to a specific result, commonly referred to as a black box, what we can agree on is that there is more need for transparency.

This is where there’s a push for explainable AI. With more transparency, it will increase the ability to detect errors, eliminate bias and trace results to better understand outputs created by AI. This is especially important for industries such as financial services and healthcare that are highly regulated, as well as businesses with international footprints that must adhere to region-specific rules and regulations. Without helping customers and end users understand how and why a particular decision was made, a product or service may be assumed to have deceptive marketing tactics or fail to meet compliance requirements.

Best Practices for Implementing AI in Your Business

Here at Treble, we work with lots of interesting AI companies as well as companies that are broadening AI capabilities in their products and services for edge computing, robotics, cloud computing, DevOps, and much more. If you’re building an AI company, expanding or pivoting your existing product or service with AI for enterprises, here are some best practices:

Focus on Purpose-Built AI Solutions

Focus on talking about AI in the context of purpose-built products and services that apply to specific use cases or industries. 

Transparency in AI Processes

Explain not only what results AI will discover but also offer a step-by-step explanation of what is being done, what data is being used, how much data is being used and how the results will get refined over time.  

Demonstrating Real-World Value  

Offer real-world examples that demonstrate the value your product or service offers with quantifiable technical benefits such as time savings and business benefits such as cost savings and ROI. 

Differentiating Your AI Capabilities

Focus on unique differentiators and industry-first accomplishments with AI.

Building a Strong AI Team

Highlight the strength of your team, including data scientists, mathematicians and engineers solely focused on AI.

Educating Stakeholders about AI    

Prepare your prospect or customer contact with a short primer document to use internally to build buy-in from different parts of the C-suite from the CFO, CIO, IT engineers, DevOps and lines of business.

Navigating the AI Landscape with Confidence 

Relatively speaking, AI is still in a nascent stage, and there is a lot of mistrust regarding how it is used. Whether it’s investors or customers you want to attract, how you talk about AI can help open new doors and opportunities or jeopardize your reputation and brand equity.