Understanding the difference between assisted intelligence and artificial intelligence

Since the term AI was first introduced in 1956, our reliance on artificial intelligence has grown immensely—much more than we ever anticipated. It’s not just a concept from futuristic sci-fi films anymore; today, nearly every sector, including highly regulated fields like finance, has embraced AI to enhance its competitive edge and boost profitability.

Companies around the globe are now integrating AI to streamline their operations, stay ahead of their competition, and solidify their positions in the market. Honestly, it’s hard to imagine any CEO ignoring the potential AI has, when used properly, to make our work lives easier, more efficient, and cost-effective. I’ll say it again: when used properly.

While AI is certainly driving growth for many businesses, it's important for us as leaders to take a moment to reflect: Are we truly leveraging what we refer to as "artificial intelligence," or are we actually dealing with "assisted intelligence"? Have we fully tapped into the capabilities of genuine "artificial intelligence"?

From my perspective, we still have a long way to go to reach that goal. Although I am not yet a believer in the full potential of AI, I am a strong advocate for assisted intelligence. For example, using machines to analyze thousands of messages in search of potential non-compliant language and behavior is a practical application of this technology. But it all starts by understanding the difference between what is assisted intelligence and what is artificial intelligence.

The Role of Assisted Intelligence in Modern Workplaces

If we really think about how “AI” has changed our daily lives - is it really doing what it says on the tin? While many of us call it "Artificial Intelligence," a more accurate term might be "Assisted Intelligence." Thanks to innovation in machine learning and natural language processing, many workplaces are changing for the better—about 75% of knowledge workers are already using AI tools to their advantage. However, the idea of AI being fully independent and capable of independent thought is still a long way off.

And that’s not necessarily a bad thing!

Many companies are not yet ready for these types of - advanced - AI solutions. When we take the time to properly examine their primary challenges, it becomes very clear that the biggest obstacle is managing the vast amounts of data generated each day. In fact, over half of employees—around 53%—report feeling overwhelmed by this amount of data, which hinders their ability to engage in strategic thinking, or what I refer to as "meaningful work."

Here is an example: a company with 100 employees that sends out 100 messages each day generates approximately 200,000 messages each month. Even if the company implements policies to monitor just 5% of these messages, it would require a large compliance department to read through them all. Instead of relying on random sampling, companies should allow technology to analyze all messages in real-time and identify the ones that are significant. This way, compliance teams can focus on essential tasks and ensure that the company remains safe from any fines.

So, what can organizations do? They need solutions that can cut through the data clutter and offer clear, actionable insights, allowing teams to concentrate on what truly matters.

Navigating Uncertainty in AI

It’s important to recognize that AI has the potential to boost global corporate profits by up to $4.4 trillion a year. This possibility has business leaders everywhere eager and excited to incorporate AI into their operations for better efficiency and smarter decision-making.

But there’s also some hesitancy from cautious CFOs and compliance officers who want to make sure their organizations really understand the needs, benefits and risks of new technologies - especially something as unregulated as AI. Even though AI tools, especially large language models, can analyze massive datasets, their decision-making processes often aren’t transparent.

This lack of clarity can be a real challenge for compliance teams. In recent years, the amount of data they handle has increased by 10 to 15 times. While the idea of AI spotting noncompliant behavior is appealing, the industry is still figuring out how to best use AI and machine learning within compliance frameworks.

To make matters worse, the rules and regulations are constantly changing. Compliance professionals need to have accurate data to make necessary adjustments while navigating the complexities of compliance amidst ongoing changes.

Another big challenge is understanding how AI makes its decisions. Organizations frequently face questions about where their data comes from, its accuracy, and whether it’s ethically sourced, especially in tightly regulated sectors like finance, where compliance is critical.

For example, FINRA has recently shared updates regarding how regulatory standards apply to AI-generated content, reminding us that businesses are still accountable for their outputs, whether created by humans or AI.

So, what’s the takeaway? While adopting AI can be complicated, businesses should start with a clear vision. This vision needs to outline how AI can help tackle operational challenges while also highlighting growth opportunities—even as they weigh the risks of non-compliance against the potential benefits of AI.

New Year, New Possibilities

Despite the challenges that come with AI, there are plenty of opportunities for organizations ready to face these issues head-on. The good news is that as companies become more aware of their specific needs, they can use AI to pull valuable insights from their data.

In the coming year, we should expect even more exciting advancements in AI technology that will enhance its capabilities and improve user experience further. This progress will help organizations integrate these tools into their everyday workflows more effectively, giving them a competitive edge.

The real competitive edge however will be seen in those organizations that prioritize transparency and accountability in their AI strategies and will earn trust from clients, stakeholders, and regulators. It’s not just about compliance; it’s about fostering fairness and equity in how technology is used.

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