Reality-Checking AI Hype: Two Things You Should Know

There’s a lot of hype about artificial intelligence (AI). Maybe you’ve heard that it can triple, quintuple, centuple your productivity overnight; it can replace every employee in your organization; it can solve your problems with the slightest (automated) flick of its finger. But that’s not reality.

True: AI is already essential to our modern lifestyle.

True: AI has incredible power to transform and improve organizations.

But also:

True: AI is a potential enabler when applied correctly, not a magic bullet.

In 2021, the McKinsey Global Survey polled thousands of business leaders across industries and found that the companies seeing the highest earnings boost from AI are spending more efficiently on AI and are more likely than other organizations to mitigate their AI-related risks. AI adoption is growing: “56 percent of all [McKinsey Global Survey] respondents report AI adoption in at least one function, up from 50 percent in 2020.” The result? Simply “having” AI no longer constitutes a competitive advantage. Companies that not only “have” but also use AI cost-effectively and efficiently are the real market winners.

If your company hasn’t yet adopted AI (or tried and failed in the past), then the question on your mind is probably, What’s the difference between innovation and hype? After all, it’s hard to tell without experience, and mercenary AI companies won’t hesitate to sell you hype with a price tag marked, “innovation.”

That’s why Synaptiq created  AIQ™, "Artificial Intelligence Quotient", a score that determines your organization's readiness for AI and overall data maturity to help companies succeed with their AI initiatives. You can read more about AIQ and the individual 11 key capabilities on our blog, or take our AIQ assessment now to deep-dive into your company’s data maturity and AI-readiness.

BUT! Although AIQ is a valuable metric, raising your company’s AIQ shouldn’t take priority over other business objectives. Like we said before, AI is not a magic bullet. It won’t solve your problems overnight—especially if those problems sound something like, “We’re broke because we over-invested in AI hype!”

Your company’s goal should not be “raising AIQ” but rather “meeting our business objectives and raising AIQ to support those objectives.”

Here are two things to keep in mind:

#1. Low AIQ is not necessarily bad. Sometimes, it means a blank slate: the perfect foundation for building something better. Unlike IQ, AIQ can increase significantly with strategic change. Companies aren’t “born” with genius AIQs!

If your company has a low AIQ, it’s essential to consider whether (and why) you think a future AI endeavor is worth the investment needed to raise AIQ today.

• How will AI help you achieve your near or long-term business objectives?

• What will the cost of raising AIQ be versus the benefit?

Synaptiq’s team of experts has worked with AI and data science in numerous sizes and shapes of organizations and industries for over a decade. We’ve seen everything, so we know that the road from “wanting AI” to “using AI well” is fraught with many obstacles.

Undoubtedly, AI is the future, and the future is approaching—fast. But it won’t arrive tomorrow. If your organization is better prepared to raise its AIQ three months, six months, or even a year or more from today, then wait. Build internal support; fine-tune your business processes. It’s better to commit confidently to achieving AI-readiness later than half-ass the effort today and fail as a result. The latter can result in AI paranoia that may stifle your organization for many years in the future.

#2. High AIQ does not guarantee success. Don’t get cocky!

If your company has a high AIQ, then congratulations! We recognize that it’s hard-won. However,  even a high AIQ doesn’t guarantee anything. To use a track and field metaphor: you can still trip on the last hurdle.

What is the last hurdle for AIQ? Use your AI-readiness to implement AI. Consider the following example: Company XYZ has worked hard to develop its Data Sourcing maturity, its final AIQ capability that it needed to achieve AI-readiness. Its data hunters have sourced accurate, complete, and relevant data—nice!

But at the last minute, as Company XYZ prepares to embark on its first AI endeavor, there’s a hiccup. Company XYZ falls for an egotistical impulse and substitutes in-house data for its great new externally sourced data. Its AI endeavor fails.

What went wrong? Implementation. Company XYZ had a high AIQ, including Data Sourcing maturity. But when it didn’t use that maturity in its AI endeavor. Instead, it regressed back to a low-maturity habit: using messy in-house data rather than more appropriate data it purchased from a third party. The lesson you can learn from this mistake is simple. If your company has a high AIQ, actually use it!

Before we asked the question, What’s the difference between innovation and hype? Now you have the answer. AI hype occurs when companies prioritize having AI over business objectives. That might mean raising AIQ when it’s not worth the investment (see tip #1) or failing to implement the eleven capabilities that constitute AIQ when rushing an AI endeavor (see #2). The key to avoiding AI hype and succeeding at AI innovation is to approach AIQ (and, by extension, AI) as an enabler to further business objectives.

About Synaptiq

With over 60 clients in 20 sectors worldwide, Synaptiq is a full-scale AI consultancy delivering impactful solutions with applied machine learning and vision, natural language processing, and other data-driven techniques. If you are an organization looking to explore digital transformation, we can help you unlock the power of AI and data science. 

For more information about Synaptiq, please visit www.synaptiq.ai

HOW SYNAPTIQ CAN HELP

Synaptiq focuses on the humankind of AI; building a better world as we lean into an age of human and machine interaction.

We believe solving serious challenges, making real impact and saving lives is worth every waking moment. So we collaborate and make thoughtful considerations across disciplines examining past, present and future models of merit. Whether history, science, math, nature, human behavior; they all inform the data and ideas that help us find answers to world-class riddles.

We keep our AI on people because AI is how we do it, humanity is why we do it.