Editorial
Every day seems to bring a new outrageous headline with promises of artificial intelligence's superpowers. The hype is very high right now, with headlines conveying the sense that disruption is imminent and everything will soon be overtaken by these technologies.
The reality, however, is very different. Yes, artificial intelligence (AI) does have a lot of potential and it’s an exciting field. However, not every organization has the right foundation in place to deliver real results. There are critical and fundamental challenges for many organizations to address before they can meaningfully capitalize on AI and machine learning technologies.
Is your company ready? Here’s a look at five signs that you might miss out on the AI revolution.
1. You Don’t Have an Innovation Strategy
Given the hype out there, you need a foundational strategy in place to evaluate new technologies and determine whether they could address critical needs for your company.
You don’t need a crystal ball to predict the technologies or products that will take hold of the market. You do, however, need the ability to systematically review these opportunities and figure out how they will affect your business. You can only achieve your strategic goals with a solid innovation strategy that will enable you to adapt to new changes to your business.
Related Article: 6 Factors for Organizational Innovation
2. You Don’t Have the Right Team
Implementing AI is a cross-disciplinary effort that requires substantial business and engineering expertise, as well as full buy-in from people at various levels of your organization. Trying to deploy AI without the right team practically guarantees failure.
If you are planning to develop your algorithms and AI practice in-house, you need a team of stakeholders from business, engineering and leadership positions, and a cross-functional team of data scientists, business strategists, AI engineers and user researchers.
Does that sound daunting? A better route for your team may be to use an off-the-shelf AI system or machine-learning-enabled product. With that option, you gain the best of both worlds: experimentation with AI minus the hefty cost of managing it in-house.
Keep in mind your team needs to focus on both employee and customer experiences. Every employee, from the front-line customer service agent to the product marketer, affects the customer experience. AI is having a major impact on the digital workplace already. Among other things, it offers the ability to solve one of the most pressing issues facing organizations today: knowledge management. With the average tenure of employees getting shorter, companies are using AI to create workplace experiences that automatically offer new (and existing) employees task-relevant insights as they work. This speeds up innovation cycles and time-to-proficiency by infusing every interaction with relevant information; it also relieves employees of the need to re-create knowledge that already exists in your organization.
Related Article: Why the Benefits of Artificial Intelligence Outweigh the Risks
3. You Have a Data Problem
Your artificial intelligence and machine learning technologies are only as good as the data you feed into them. If you are limiting yourself by keeping customer interaction data in separate siloes, not embracing the collective volume of data your organization stores and building a unified view of those interactions by customer, you are going to run into problems.
It’s common for companies to struggle with synthesizing all of the data they need for machine learning and artificial intelligence to work. Whether difficulties arise from patching together multiple legacy systems or as a result of mergers and acquisitions, you need to understand the root causes of data problems before you implement AI and machine learning. This will take some time, and you need to fix this now before it’s too late.
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4. Your Culture Doesn’t Reward Failure
Have you ever heard of zShops or Amazon Auctions? Both were auction-style sites that Amazon.com attempted to launch. Both failed. However, the story doesn’t end there: Amazon used the lessons learned to launch Amazon Marketplace, the wildly successful and popular platform for third-party sellers.
The point is this: Don’t avoid failure; avoid drawn-out failures. A quick failure is not a failure at all — it’s a learning opportunity, and you can use what you learned to further develop your strategy. I’m not saying you should reward every bad idea and encourage failure. I’m saying you should create a culture of educated risk-taking that fosters creativity and innovation.
With any emerging technology, you need to expect some stumbles and pitfalls along the way. It’s inevitable. Companies that succeed in the AI revolution will set themselves apart because they will recover from failures more quickly (and because they allowed themselves to have failures in the first place).
5. You Aren’t Listening
How do you expect to meet employees’ and customers’ needs with AI if you don’t listen to them? Take every opportunity to study and learn from your users. They will surprise with you in many ways.
Case in point: the milkshake study from innovation expert Clayton Christensen. A fast-food chain wanted to improve sales of milkshakes and immediately launched into “what changes do we need to make to the product?” mode. But after studying sales data, company officials realized that the majority of milkshake purchases were made by customers who ordered shakes for breakfast to satiate their appetites as they embarked on long commutes. Once they understood this, they were able to adjust their offering in a way that appealed to those customers (creating a thicker, chunkier milkshake and offering the option of adding chunks of fruit that could last an entire commute).
When you’re looking for ways to use a new technology like AI, it is crucial that you ground your business case in the real needs of your users and make an active effort to constantly listen to them and understand their needs.
Related Article: 8 Examples of AI in the Workplace
Focus on a Problem You Need to Solve
Not every use case for AI will be worth pursuing. There’s a lot out there. And as is the case with many emerging technologies, hype has overtaken the conversation. I’m still waiting for the day when drones take over for the post office and I can 3D-print my next house or ride to work in a self-driving car. Companies that merely chase the AI breakthroughs promised in the headlines won’t be able to deliver the real results that will help them lead the market. Focus first on what problem you need to solve, and then find technology that helps.
Now that I’ve covered the signs that your company could miss out on the AI revolution, I have a question for you: What are the signs that your company is going to lead in the AI revolution? Post your answers in the comments here, or tweet at me (@techpoobah).
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