How AI’s Ethics Protect Your Brand’s Future

Any significant technological innovation is a double-edged sword eliciting excitement over future possibilities and fear over the disruption it will trigger. Generative AI is that current innovation — for as many people who are excited by it, there are just as many who are anxious and fearful.

Will this new technology create a utopian era of autonomy that enables human workers and technology to achieve incredible outcomes together? Or will it snowball into a malicious entity that reduces human relevance or even worse — supersedes human control?

In other words: Will this sci-fi movie have a happy ending — or a dystopian one? Let’s take a look at ethical AI principles in business.

Ethical AI Principles: Concerns Abound

As with any transformative technology, concerns about job security and trustworthiness bubble to the surface quickly, as they should. Generative AI offers the promise to help organizations and the people who work for them become more efficient, engaged and innovative. But if left unchecked and unregulated, it has the potential to upset the delicate balance of business risk and reward.

AI Ethics & Data

It’s not a matter of if businesses will incorporate generative AI, but rather when. And market-leading organizations already have. AI offers unprecedented levels of scale as well as the capability to vastly improve customer experiences. To do so, business leaders will need to create the right implementation strategies and ensure their technology is fueled by fresh, complete, bias-free data or they will expose their business to high levels of risk.

Delivering the AI Promise

For generative AI to deliver on its promise, people in every functional area need to play a critical role in its oversight and development. Even the most trustworthy and robust AI cannot replace human workers. During this AI journey, businesses need to prioritize three crucial areas to achieve AI harmony: ethical practices, robust data and customer satisfaction.

Adopt ‘Do No Harm’ Policies

The concept of “do no harm” typically applies to community helpers, and it absolutely should extend to the ethical AI principles concerning practices and personal data. Considering the level of sensitive customer information many companies handle, it’s critical that organizations implementing AI are transparent in their practices and treat data management, cleanliness and activation in an ethical way.

Avoid Biases, Stale Data

Simply put, biased or stale data will always lead to biased or inaccurate AI decision-making. When the outputs of these flawed programs are customer-facing, companies risk alienating consumers with irrelevant or poorly timed messaging as well as damaging their brand’s equity with messaging or engagement that doesn’t match their brand values in the marketplace at large.

AI Transparency Is Key

Transparency is critical piece of this — having black box AI systems that don’t expose how decisions are made, or how data is used may be legal, but it’s certainly not something that creates trust between brands and consumers. For example, being able to explain why a consumer might receive a different offer in their mobile app versus in the call center — there might be a legitimate reason, or it might be caused by data fragmentation.

For marketing teams in particular, ethical AI principles mean strategies are critical to creating long-term brand loyalty and in turn sustainable business results. Pushing a service to a customer that isn’t in their best interest might result in a short-term sale, but it won’t result in a long-term healthy relationship that yields results over and over.

Related Article: Ethical AI Is Easier Said Than Done, But We’ve Got to Start Somewhere

Ensure Your Scale Is Calibrated

All data feeding into your AI systems should be fresh — not just from last week, or last night, but up to the minute. When it comes to bolstering trust, privacy, and transparency, your data strategy is a key component. First-party data is a brand’s most valuable resource and will become even more valuable in the face of evolving regulation and data deprecation. Ensuring the data you collect is fresh and provides the most complete view of your customers possible is critical.

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First-Party Data

Artificial intelligence more effectively activates first-party data across diverse functional areas and channels. For example, marketing a product to your customer who already purchased it is often a result of disconnected information that creates a fragmented view of the customer. And, it annoys the customer and wastes marketing dollars.

When you’re feeding the right information into your AI, it can help break down the siloes that exist within organizations to make sure customer journeys are comprehensive and contextual, with all necessary data centrally accessible. Remember: AI recommendations and outputs are only going to be as good as the data you’re feeding into AI systems.

Related Article: Using First-Party Data to Build Trust With Your Customers

AI & Targeted Customer Outreach

Consumers are becoming increasingly numb to constant outreach from brands, both because of the sheer volume of messages they receive each day and because they lack relevance, context and timing. Because customers face so much brand noise already, personalization is no longer about what to say and how to say it, but also when and where to deliver a message.

Combining ethical AI practices with existing customer data means brands can accurately analyze rich data sources to reach out to a customer at their exact moment of need — and sometimes, even anticipate a potential issue before the customer even realizes. This not only makes customer-facing teams more efficient, but more importantly, provides an optimal experience for customers that can help enhance their lives versus make them more chaotic.

Final Thoughts on Ethical AI Principles

The current hype around AI may tempt businesses to rush into adopting technology too quickly without proper considerations, strategies and stewards and without consideration of the ethical AI principles. But that path leads organizations to a place where outdated, biased data, unreliable results and dissatisfied customers mix — a place where no brand wants to be.

Instead, creating a thoughtful and ethical strategy will mean gaining the ability to maximize customer data to provide the best experiences possible at any given time for customers and employees alike.

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