Artificial Intelligence (AI) is all around us. Its application to our everyday lives is becoming universal—we have self-driving cars, voice-powered personal assistants like Alexa and Siri, and, thanks to deep learning algorithms, curated one-to-one online shopping experiences.
AI has even plunged into the fintech space. It automates business processes, optimizes bookkeeping efforts, enhances fraud detection and compliance. AI is used to fight money laundering. More recently, investors are starting to rely on AI for advanced algorithmic trading.
But while AI is ubiquitous, this technology is still in its nascent stages—and what many companies are calling artificially intelligent systems are not necessarily always so.
Machine-learning technology that purports to be true AI should not solely depend on predetermined algorithms or knowledge of user behavior. Rather, it should learn autonomously, improving upon iterations and continuously enhancing. And, when it does, AI can significantly benefit businesses.
Still, CIOs should understand how AI strategically works—its processes and potential pitfalls—before signing off on it. Here are seven top questions every CIO should ask about adopting AI today.
1. What is the business case for implementing AI?
Do other strategic moves or capital expenditures necessitate business cases? If so, should AI be part of that standard? If you do require a business case for funding, consider your business’ approval process, how long formal approvals like this typically take and whether you need to make any sacrifices if you forgo the business case.
2. What problems will AI solve?
It’s no secret that AI can solve a number of problems. But determining the current and future organizational priorities are key to identifying the best use cases for AI. In other words, CIOs need to understand just how critical artificial intelligence is to the problem(s) that their companies are solving.
3. How will AI improve client/customer experiences and engagement?
AI can drastically improve client and customer experiences and engagement. But how do you intend to leverage AI to do just that? What about the client and customer experience could be enhanced, and in what specific areas could you benefit from more engagement?
4. What are the potential pitfalls and consequences of getting it wrong?
At the end of the day, AI is still an emerging technology. As it learns, it strengthens—and it can only do so with quality data. What do you risk in the process? Are you willing to take those risks? If there is human intervention involved to ensure accuracy, is AI even worth implementing?
5. What will the process to implement AI look like, including the reporting structure?
Of course, implementing AI takes time. It may automate tasks and take over certain professionals’ jobs. How do you plan to make this transition? Will you have specific team members dedicated to AI and, if so, what skills will they require? Or will you allow AI to run on its own? If so, how will you handle the transition so that all employees are up to speed?
6. How do you address the cold-start problem with AI?
The cold-start problem refers to the issue of an AI system requiring historic data to draw inferences that do not yet exist. So, how will you, as a company, get off the ground running without waiting for the AI system to collect data to start using it?
7. How will you address ethics and the social impact of AI?
Some people argue that AI can feel invasive. After all, it tracks consumer behavior and, therefore, has the power to manipulate. Others argue that it takes jobs away from people. Either way, the potential for misuse is great. Understanding how the business plans to mitigate misuse and ease the impact is critical.