Haven't we come a long way in the last two to three years? From viewing AI as a threat to embracing the technology as an assistant that helps increase convenience, comfort, and productivity. Most of us are using AI in more ways than we can count. Asking Siri for directions to a nearby restaurant. Commanding Amazon Alexa to play your favorite track. Using Gen AI models like ChatGPT to generate art, articles, and code. The dependencies on AI models are continuously evolving. It's a core part of how we work, think, and create. Explainable AI is now becoming a crucial part of this evolution, helping users and businesses understand the rationale behind AI-driven outputs.

And it's not just the general workforce or students who're leveraging AI. Even executives aren't behind. Because AI offers foresight, clarity, and speed—qualities that can be difficult for an individual human secretary to match. They're using AI tools to identify market trends, produce projections, reports, and more. But all is still not hunky-dory. Amidst these significant developments, questions loom- how does the AI model reach that particular conclusion? Let’s say, based on past historical trends, AI suggests a CMO cut marketing spend by 20 percent. But marketing is an ongoing activity. What might have worked in the past may not yield the desired results in the coming weeks. Secondly, what if AI's conclusion stems from biased, inconsistent, or incomplete data? This is referred to as a "black box." A modern-day conundrum that is almost impossible to interpret.

About 95% of data leaders admit that they can't fully trace AI decisions, as per the latest Dataiku "Global AI Confessions Report". Furthermore, 80% of the 800+ data leaders who participated in this survey say that an accurate but unexplainable AI decision is riskier than a wrong but explainable one.

Because without a clear rationale, even the most intelligent recommendation becomes difficult to trust. Moreover, for businesses that deal with a complex set of offerings, such as finance, insurance, or healthcare. This is why embedding explainability in AI models is crucial, as it can increase trust and supercharge decision-making. In this article, we dive into the rise of executive AI, why explainable AI matters, its use cases, and more.

The Rise of Executive Intelligence

We have seen technology evolving at breakneck speed in the last decade or two. From cuboidal-shaped TVs to 4K UHD televisions, from GPRS/2 G internet to 5G, from Maruti, powered by mature ICE, to Tesla's self-driving cars, the examples are plentiful. However, as observed, no technology has been as revolutionary as artificial intelligence. And today, it's no longer limited to automating tasks. It's increasingly being designed to augment human judgment at the highest level of business. In executive roles. Recently, a China-based NetDragon Websoft appointed an AI program named Tang Yu as its CEO.

Only time will tell how successful such endeavors will be. To stay on course, we will discuss how AI can serve as a strategic advisor. And to what extent? Sure, AI can process data rapidly. Streamline answers based on accurate input or prompts. But can it be trusted entirely to lead a business? Also, there is an ethical dilemma.

Why Explainable AI Matters

AI relies on historical data. That data can be inconsistent, inaccurate, and may represent the world's imperfections. Bias, often based on race, gender, age, or location, has been a long-standing risk in ML models.

Executives need insight into why a particular decision was made. In regular operations, a small error may be overlooked as inefficient, but in executive decision-making, it can result in a loss of credibility. Let's understand this through an example. Suppose an AI-driven pricing tool recommends increasing the yearly subscription for the brand's services from the existing $119 per quarter to $139 per quarter for specific geographies, based on factors such as inflation and increased viewership. However, the model's historical data overlooked recent consumer sentiment trends.

What could be the customer's first reaction? Those who have the subscription as default may be tempted to cancel it. There may be other forms of backlash, especially if the leadership team struggles to defend or justify the decision. Because without transparency, they wouldn't know. Not only do executives need to explain every move to boards and regulators, but also to the end users. This is why explainable AI matters. It enables accountability. Helps identify potential biases. Characterize model accuracy and transparency.

What Explainable AI Looks Like in Practice

1) Finance:

Imagine an AI model suggesting that the CFO phase out/ discontinue a long-running mutual fund. With explainability, such as declining investor interest and increased withdrawals, the AI model may become a trustworthy executive. Because the leadership team can see the whole reasoning behind this suggestion.

2) Human Resources:

AI models can explain why the employee retention rate has gone down between the last two quarters. Whether it's due to seasonal demand or personal reasons, such as moving to a different city, or factors that companies have greater control over, like promotion delays compared to industry standards and work-life balance.

3) Healthcare:

Consider an AI system that advises primary healthcare centers on optimizing resource allocation during flu season. The system highlights trends over the last few years. Correlates seasonal infection rates with demographics, gender, and race. Sure, these factors can have some bias, but that's why data fed into the systems needs to be clean, accurate, and up to date. However, with updated insights, healthcare executives can be better prepared and make decisions that improve patient care and safety.

These are just some of the possibilities opened by explainable AI. You too can add the missing link to your intelligent systems with responsible AI implementation and get a trustworthy AI assistant.

Conclusion

As we have seen, AI has immense potential. It offers businesses a strategic advantage. But we must note, “intelligence without explainability = brilliance without trust.” That's why embracing explainable AI matters. It can help:

• Operationalize AI with trust and confidence
  
  
• Speed time to AI results
  
  
• Reduce the risk and cost of model governance

That said, AI requires deliberate implementation and oversight. It can't inspire and motivate, exercise emotional intelligence, and make creative strategic decisions. With AI in play, roles within the C-suite could become more fluid, collaborative, and strategic.