Is it possible for a company to thrive in today’s brutally competitive business climate without making significant long-term investments in artificial intelligence (AI) and automation? Personally, I find it hard to believe, given that AI has become core to many processes, especially during the pandemic, when businesses switched to online services for everything from banking and retail to healthcare and live industry events. I see AI evolving from a niche technology to eventually permeating all aspects of business.
One example of AI’s growing impact is reflected in conversational AI, which is making our regular encounters with brands more meaningful. Complex computer interactions, such as those with multi-turn conversational chatbots and more sophisticated Alexa or Google Home devices, are becoming increasingly common. More and more, companies such as Microsoft, Salesforce, Apple, Google, and Amazon are integrating AI into the apps we use at work, in our cars, and on our personal devices, and this trend is going to accelerate.
Despite AI’s continuing advances and the availability of a plethora of AI, machine learning (ML), and automation solutions, they are not being prioritized because organizations are so overburdened with operational work that they don’t allocate enough time to building out advanced technological capabilities. To reduce operational stress, businesses must implement and use these new tools as soon as is feasible; otherwise, they risk falling behind the competition and losing customers.
Three measures you can take will keep enterprise-wide AI and automation adoption moving forward.
Encourage Continuing Education and Training
Technology is only as useful as the people who use it. In the banking industry, AI can monitor each transaction in the background and alert humans to anything out of the ordinary. The AI model is then continuously taught by humans to improve its accuracy (through the feedback it receives about financial transactions). Surprisingly, many times the teams hired to provide the data inputs and the feedback are completely unaware that they are training an AI model for use in future financial transactions. This is one example of why continuing education and training are essential for the people on AI teams. They should know how this technology works and the critical role they play to make it more accurate. AI tools and technologies are best optimized when operated in tandem with humans, but those humans need to understand how the AI operates and how to best work with it to deliver the best outcomes that are ultimately more useful to the business.
Emphasize AI’s Contribution to Cybersecurity
Many chief information officers (CIOs) now view cybersecurity not just as a topic of interest but as a serious cause for concern, given the explosive growth of digital interactions for engagement with customers, as well as more nefarious activities. Data breaches, identity theft, and other forms of cyberattack are on the rise, making it imperative for businesses to strengthen their cybersecurity controls. Leaders must ensure robust endpoint security, maintain reliable backup systems, test for vulnerabilities, and deploy appropriate security technologies.
AI can help cybersecurity teams monitor for data theft and other attacks. The task of monitoring the flood of threats is too big for humans to handle — sometimes IT or security teams are unable to manage the sheer amount of manual processing involved. AI, particularly predictive AI, can help. For example, predictive AI can automatically recognize unidentified computers, servers, and code repositories on a network, which I explored in a previous analysis.
Recognize AI Requires Investment
Rather than focusing on the upfront cost associated with AI, CFOs should shift their focus to the value that the technology offers. Companies frequently make the error of not adopting new technologies because they view investments as costs rather than opportunities. But this just creates more laborious manual work that requires more people, which is also an expense that some companies aren’t willing to commit to, resulting in higher error rates (not enough people for quality control) and/or lower output rates. The end result is that the organization’s goods and services fall short of their true potential. While a company that doesn’t invest in technology and doesn’t adapt quickly to change can achieve greater profits in the short term by simply hiring more people each quarter, it will have a tough time being competitive in the market over the long term.
Also, companies that wait too long to upgrade risk falling behind the competition and losing market share and customers. The history of business is littered with once-household-names synonymous with game-changing technology — Kodak, Polaroid, Nokia, to name just a few — that are either shadows of their former selves or are considerably different and smaller businesses today. These are companies that did not see how and where the technology was headed and failed to upgrade and innovate. As a result, they lost market share or were replaced by rivals in industries that underwent massive digital transformations.
Final Thoughts
AI has contributed to many industries and shown early wins; the technology is now on its way to broad-based implementation. Investment into (and support for) digital transformation and a “human plus AI” approach, as well as training to ensure that employees can leverage AI tools in strategic applications, including cybersecurity, will enable companies to get the most from AI in the near term.