This is very promising. We are now in the age of AI. What can AI do for my business today. What might AI do to my business tomorrow. We need to act. We need to act first. We need to act fast. We need to adopt and adapt. We need to ASAP identify vendors who can help us through this change.
Stop, breathe.
The opportunities that AI presents today are indeed very promising. However, it is necessary to embrace a prudent and well-planned strategy guiding through AI adoption. The complexities of integrating AI technologies into business operations are well understood and extensively documented. Yet, each organization present its unique set of challenges. Hundreds of AI labeled products and services are propping up every day pumping ones urge to act, and it is thus easy to fall prey to AI washing.
What is AI washing you might ask?
AI washing refers to the practice of exaggerating or misrepresenting the capabilities of artificial intelligence (AI) systems in order to make products or services appear more advanced or sophisticated than they really are. A borderline deceptive practice where companies overstate their capabilities by using the term “artificial intelligence” (AI) in a misleading or superficial way.
AI washing could manifest in several ways:
- Exaggerated Claims: Companies may claim that their products or services are powered by AI, when in reality, the AI component is minimal, insignificant or even nonexistent. This could involve using simple algorithms or basic automation and labeling it as AI. This can also involve exaggerating the extent to which AI is integrated into a product, misrepresenting the sophistication of algorithms used, or even using AI jargon without any substantial machine learning functionality.
- Misleading Marketing: Businesses may use ambiguous terms like “smart,” “intelligent,” or “AI-driven” without providing concrete details about how AI is actually used in their products. This ambiguity can mislead consumers into thinking the product is more sophisticated than it truly is.
- Hyping Minor Features: Sometimes, AI washing involves emphasizing small AI features within a product while downplaying or ignoring its overall limitations or lack of significant AI integration.
- Unsubstantiated Performance Claims: Companies might make bold claims about the performance or capabilities of their AI systems without providing evidence or independent verification to support these claims.
Undermining of transparency makes it difficult for consumers to discern between genuine AI-driven products from those that merely use the term for marketing purposes. AI washing thus jeopardies the trust in the broader data ecosystem. It is hence important for consumers to be critical and seek evidence when evaluating products or services that claim to incorporate AI. The SEC as well has warned against such malpractices [1]. Even though civil penalties were issued to a few, AI washing remains prevalent and seems to be on the rise [2].
To avoid falling victim to AI washing, consumers should educate themselves about AI terminologies and capabilities, seek to ask specific questions about how AI is implemented in a product, and look for evidence, independent reviews or expert opinions that validate the claims made by companies regarding their AI technology.
Some aspects one could inculcate in the decision-making process include:
Apriori identification of the problem: It is not a good practice to fit a problem to a solution. Buying into a solution before nailing down on the problem may not serve in your best interest. Rather seek to identify what you need to solve for and that could help in navigating towards an optimal solution.
Understanding the expenses attached with the AI tag: Investments in data-driven technologies come with a long-term commitment and at a significant cost to an organization in terms of time and money. Having a clear picture about the return on investment and the time required for deliverables could help in setting the right expectations.
Focusing on resources at hand: Identifying the intellectual property in your data, present-state infrastructure, available talent for the project, investment leeway and timelines ground us while making a successful transition or adoption.
Research and outreach: Obtaining multiple opinions on the product, conducting process impact analysis, running hypothesis tests and furthermore, working with an expert always helps to provide additional clarity into the unseen while finalizing on the any business process changes.
With this proactive strategy, you are being much more careful with how your data is handled and models are built and deployed. Sometimes slow and steady wins the race—and sometimes smaller can be smarter.
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