4 Ground Truths All Aspiring AI Companies Need To Know
In a world where acting first on emerging trends is a mission critical objective, AI-driven insights are the best weapon against the competition.
Most companies understand the weight and importance of AI as a foundational competency. Unfortunately, they may also have misguided expectations about AI, including the idea that “AI” is just another name for an all-knowing, all-seeing supercomputer.
Approaching an AI initiative without a clear understanding of the opportunities and risks can result in wasted time and money, so before you kick off that project, here are some ground truths that can get you started on the right foot.
Truth #1: AI is just the next step of your big data strategy
For executives that spent the last four or five years racing to become a big data company, the idea of an AI initiative may inspire deja vu and heartburn. But in reality, those attempts to become masters of big data were not made in vain.
Even if your organization never truly capitalized on the promise of big data, the good news is, you’ve probably taken steps in the right direction by adopting systems -- like customer relationship management (CRM) solutions, mobile services, online communities, and loyalty programs -- that have increased the value and volume of your data repository.
In the age of AI, as long as you are collecting the data, you already have access to insights -- they’re just waiting to be discovered.
Truth #2: The true goal of AI is outcome driven solutions
Most enterprise reporting is about KPIs. The problems with this approach is the fundamental assumption that the KPIs you are monitoring are the ones that support your larger business goals.
Approaching business through the lense of KPIs requires humans to think through massively complex problems to determine which metrics matter. This is a waste of good AI, which is designed to help you focus on outcomes -- not metrics. Rather than looking at a set of KPIs that you believe will increase your topline margins, you can use AI to say, “I need to increase my top-line margin by three basis points. How do I do it?”
For example, if you are a high-end retailer, you might be striving for deeper customer relationships. Without AI, you may measure the success of this goal through a set of KPIs like NPS scores, repeat visits, and spend per visit. But which of these metrics is most important and why? With AI, you can actually uncover the business actions that move the needle -- whether it’s more precise style recommendations, higher quality social interactions, or better fit data on the website.
On the other hand, if you are a large grocer, you may be looking to reduce waste on perishables. AI can help you see the best path forward -- whether it’s smarter demand planning, more tailored promotions, or a combination of both.
To move from simple pattern matching to planning-focused cognitive technologies, humans must bring intuition and common sense to the equation. This means truly understanding the desired business outcomes rather than focusing on KPIs. While this may be a shift in how most companies -- and systems -- have been managed historically, making this leap will propel your business to new levels of success.
Truth #3: Accept That The Biggest Challenges You Face Won’t Be Technical -- They’ll Be Cultural
Surprisingly, the most complicated aspect of most AI projects isn’t the technology -- it’s the people. Want to know why 85 percent of big data projects really fail? Because the right team didn’t come together with the right authority and buy-in to make the changes happen.
At its core, AI isn’t just a technology play; it signifies a massive cultural change. In order to ensure successful rollout and adoption, you must have a strategy for managing the organizational dynamics. If key team members approach business from a, “but this is how we’ve always done things” mindset, then your AI initiative will fail.
When done right, AI can result in striking changes. But the leaders of the business must start by presenting the possibilities, and shifting the culture to embrace the changes. This is the only way data-driven insights can actually disrupt the business (in a good way.) To assess the readiness of the teams around you, try posing the question, “If the system makes a recommendation, will we make the leap?”
Truth #4: It’s all worth it.
According to Deloitte, 82 percent of early adopters have seen a positive financial return on their AI investments. That’s because AI gives companies the opportunity to finally leverage the vast amounts of data generated by products, customers, competitors, and the outside world, to expose new business opportunities as they arise.
For example, SoftBank Robotics recently demonstrated how AI could be used alongside robots to intelligently automate the fulfilment of click-and-collect orders. In this example, Pepper -- the customer service robot from SoftBank Robotics -- was able to intelligently make upsell suggestions to customers that had placed an online order, prioritizing overstocked items based on real-time inventory information coming from Tally, an inventory tracking robot from Simbe Robotics. Customers coming to the store to pick up an order benefitted from smart recommendations that improved the buying experience, while the business benefited from larger cart sizes and faster sell-through rates of overstocked items.
This is just one example of how businesses can use data from devices -- like robots, sensors, and cameras -- to execute more sophisticated personalization and fulfilment strategies. If you want to learn more about how AI can empower your business, we’d love to share how you can leverage intelligent automation to streamline operations, improve customer experience and employee satisfaction, and drive revenue. Visit softbankrobotics.com/us to learn more.