AI isn't a goal (or a strategy): When AI Fails (or Fails to Live Up to Lofty Expectations)

Let’s face it: AI is here to stay, and despite the hype, it’s really good at a lot of things. However, amidst the fervor for adoption, it’s crucial to remember that AI itself is not the ultimate objective of any marketing strategy. Instead, it serves as a powerful means to achieve broader business goals. So, in this newsletter, we’re going to look at leveraging AI as an augmentative tool rather than an end goal in itself.

?? Want to take this further? This is what I do: I work with orgs on AI adoption and marketing ops strategies that incorporate AI. =>Let's talk.

Let's get real. About Artificial Intelligence.

Reality check: Navigating AI in marketing isn't just about leveraging its power—it's about wrestling with a whole host of ethical issues. From ensuring data privacy and pushing for algorithmic transparency to tackling bias and maintaining security, the ethical use of AI is complex but non-negotiable. Keeping AI ethical means keeping it transparent, accountable, and secure. Stay vigilant about regulatory compliance and third-party risks, and always remember: with great power comes great responsibility.

Let's face it: AI isn't just a tactic or a tech problem; it can be a rather large ethical puzzle. As marketers, we’re not just tossing ads into the ether anymore. We're wielding sophisticated AI tools that can slice through data like a hot knife through butter. But with great power comes great responsibility, and the ethical landscape needs to be navigated with care.

Data Privacy and Protection

Many of you are likely already familiar with GDPR, CCPA, and an alphabet soup of data protection regulations, but usage of AI makes these even more relevant in some cases:        

• Protecting Personal Data: Your AI loves data like a moth loves a flame. But remember, how you collect, store, and handle this data can make or break your company's reputation and compliance status. Ensuring that this data is gathered, stored, and processed securely is paramount.
• Transparency in Data Processing: Ever tried explaining to your customers what AI does with their data without their eyes glazing over? Good luck. But it’s essential. People deserve to know how their information is being used, and let’s be honest, they should. It's crucial to be clear about how AI uses personal information. Customers should understand what data is being collected and how it's being used.
• Consent for Data Use: It’s not just polite to ask; it's legally required. Get explicit consent before using data, or you might find yourself in hot water. Proper consent must be obtained for data collection and use, respecting user preferences and legal standards.
• Data Minimization and Purpose Limitation: Collect only what you need and use it only for the intended purpose. Think of it as the Marie Kondo method: if it doesn’t spark joy (or utility), get rid of it. Collect only the data needed for a specific purpose and ensure it's used solely for that purpose to avoid potential misuse.

Algorithmic Transparency and Explainability

The age of "trust me, it works" is over. People—and regulators—want to know how the AI sausage is made.        

• Document AI Decisions: You need to show your work, just like in math class. If your AI decides to upsell a snowblower to someone in the Bahamas, you’d better understand why. It should be possible to trace how AI algorithms make decisions, especially when these decisions affect customer interactions and marketing outcomes.
• Explain AI Outcomes: If your AI can decide on a promotion, you need to explain how it got there in plain language. No techno-babble. Provide clear, understandable explanations for decisions driven by AI, particularly in scenarios that significantly impact individual customers.
• Avoid Black Box Systems: Make sure someone other than your AI team can understand what the heck your AI is doing. Transparency isn’t just nice; it’s necessary. Strive for systems where the inner workings are accessible and auditable, not just by data scientists but also by regulators and potentially customers.

?? Listen to the podcast for more

Until then, get more insights and updates by listening to The Agile Brand with Greg Kihlstr?m podcast.

Bias and Fairness

Here’s a not-so-fun fact: AI can be as biased as the data it’s fed. Left unchecked, these biases can turn into—best case, an embarrassment, and worst case, a PR nightmare.        

• Assess and Address Biases: Keep an eye on your AI like a hawk. Regular checks for bias can save you from embarrassing and harmful mistakes. Regularly review AI systems for bias and take corrective actions to mitigate any unfair outcomes.
• Ensure Fair AI Processes: Just because it’s automated doesn’t mean it’s impartial. Work actively to ensure your AI treats everyone fairly. Implement measures to ensure that AI-driven decisions are fair and non-discriminatory.
• Governance Frameworks: Set up a rulebook for your AI’s behavior. Think of it as the ethical boundary lines it shouldn’t cross. Develop and enforce governance frameworks that continually monitor AI applications for fairness and equity.

Accountability and Liability

When AI screws up, who's to blame? Your algorithm isn’t going to go on trial.        

• Liability for AI Errors: Decide who’s on the hook when things go south. Spoiler: it’ll probably be you. Clearly define who is responsible for damages or errors caused by AI decisions.
• Human Oversight: Always have a human in the loop, ready to pull the plug or steer the ship when needed. Ensure there are mechanisms for human intervention in AI processes, maintaining human control over critical decisions.
• Responsibility Chains: Know who’s responsible for what. AI doesn’t operate in a vacuum, and accountability shouldn’t either. Establish clear responsibilities for AI actions, from development to deployment and beyond.

Security and Robustness

AI security isn’t just about keeping the data safe; it’s about making sure the AI itself can’t be tampered with.        

• Protect Against Unauthorized Access: Lock down your AI systems like Fort Knox. Safeguard AI models and data against external breaches or insider threats.
• Resilience to Attacks: Harden your defenses against those looking to poke holes in your AI’s logic. Enhance the robustness of AI systems against various forms of cyber attacks, including adversarial attacks designed to deceive or mislead AI.
• Fallback Measures: Have a plan B. Always have a plan B. Develop contingency plans for AI system failures to ensure business continuity.

Compliance with Emerging Regulations

Keeping up with AI laws is like hitting a moving target. Stay sharp.        

• Stay Updated: Keep one eye on the regulatory landscape; it changes more often than fashion trends. Make sure you’re aware of new and evolving AI-specific regulations to ensure compliance.
• Ongoing Compliance Processes: Make compliance a routine, not an afterthought. Implement and maintain processes that ensure continuous compliance with applicable laws.
• Preparation for Audits: Keep your documentation neat and ready for inspection at any time. Set up systems and documentation that facilitate audits or certifications.

Third-Party and Vendor Risk

Outsourcing AI? Reliance on external AI solutions introduces additional complexities:        

• Vendor Compliance: Make sure your third-party pals play by the rules. Ensure that these providers comply with relevant legal and ethical standards.
• Data Sharing Management: Watch who gets access to what. Loose lips sink ships, and in this case, leak data. Control data flows to and from third parties to protect sensitive information.
• Due Diligence: Vet your vendors like you’d vet a babysitter. Thoroughly. Conduct assessments of third-party AI solutions to verify their ethical and legal adherence.

Navigating the ethical dimensions of AI isn't just about avoiding pitfalls; it’s about building a framework that fosters trust and ensures your AI tools are as responsible as they are powerful. Roll up your sleeves—it’s going to be a bumpy ride, but get it right, and your AI won’t just be powerful—it’ll be ethical too.

?? Want to take this further? This is what I do: I work with organizations on AI adoption and marketing ops strategies that incorporate AI. Contact me for more info and to talk about it.

In the next edition, we'll talk about the ethical dimension and implications of AI in marketing.

Stay tuned as we explore more about how to meaningfully incorporate AI into your marketing work and go past the hype. Sign up for this newsletter and you can see more on my website at https://www.gregkihlstrom.com