Today, most business professionals are beginning to trust artificial intelligence with a range of tactical tasks, from content production to customer service operations to inventory management. For good reason, too. Over the last two years, the technology has advanced by leaps and bounds, overcoming AI challenges that previously impeded adoption by the general population \u2013 including scale, accessibility and ease of use.<\/p>\n
Despite the meteoric rise in AI adoption across corporate America, many senior business leaders still hesitate to lean on this technology for strategic, high-level decision-making. According to the World Economic Forum, 44 percent of CEOs<\/a> do not feel ready to deploy AI yet. Fear, distrust, a lack of knowledge surrounding AI, and other factors influence this sentiment. AI evangelists face a tall order to sway executives in favor of AI-assisted strategic visions.<\/strong><\/p>\n The mountain of AI challenges may be high but scaling it can be well worth the effort. As productivity booms, Goldman Sachs predicts AI will boost corporate profits by 30 percent or more over the next decade<\/a>. To maximize the value of AI adoption<\/a>, buy-in at the highest level is paramount. Leadership plays an integral role in successfully implementing AI within an organization, so getting them on board is crucial.<\/p>\n Are you working to establish buy-in with leadership at your organization? Or are you a senior leader considering AI investments in the near future? Read on for an in-depth exploration of AI challenges and opportunities for leadership across industries. We’ll also discuss how you can better prepare your organization for widespread adoption.<\/p>\n Have you ever heard of AI being called a “black box”? It’s a common metaphor used for technologies with internal workings that aren’t clear to common end users.<\/p>\n In business, AI’s “black box problem” refers to a leader’s inability to understand how AI lands at a particular output. Without this insight, they’re essentially left in the dark about how the applied logic arrived at a particular business decision. This lack of insight can be daunting and doesn’t provide the legs to effectively make the case for a business decision.<\/p>\n Mastering complex mathematical models and data sets is no easy task. So, your organization’s leaders will likely need more transparency around how AI makes assessments and provides recommendations before they trust the technology to guide high-stakes strategies.<\/strong><\/p>\n AI developers are testing out new solutions, but these are still very much in their infancy. Explainable AI<\/a> (XAI), for example, is “AI that’s programmed to describe its purpose, rationale, and decision-making process in a way that the average person can understand.” In short, it’s a framework to overcome the black-box nature of AI so that people can trust the right data and information supports the given insights. However, explainable AI still has a long way to go before it is trusted as a strong framework.<\/p>\n One way you or a fellow business leader can more easily trust in the process is by choosing technology that adheres to ethical principles and guidelines for AI development. After all, AI solutions are only as good as the algorithms they were trained on. For example, it’s important to know that the developers of a particular AI product have cleaned the data<\/a> of any biases.<\/p>\n Learning about data sources and the guardrails in place to correct bad data<\/a> \u2014 which can occur due to training discrepancies or outdated information, for example \u2014 will instill more confidence in business leaders, building trust in AI. Consider establishing an AI governance board that includes experts across the organizations to help ensure proper human oversight is in place. This governance board will foster a culture of accountability that places compliance front and center in all your AI-assisted operations.<\/strong><\/p>\n AI developers and businesses implementing the technology are also thinking about industry standards and regulations, though this comes with its own set of challenges. We dig deeper into the nuances of AI regulation in the next section.<\/p>\n You’ve likely noticed the very public conversations about the risks of data privacy, security, and accountability related to AI. Media headlines also prominently discuss the need for government regulation to combat these risks. Misinformation, plagiarism, deep fakes, and other negative impacts have culminated in an overarching narrative that AI is potentially more trouble than it’s worth.<\/p>\n This can understandably be off-putting. You may already be concerned about implementing technology that could put your business at risk, and then you must also consider the work involved in staying on top of regulations and ensuring compliance as they evolve. AI has a range of implications for business applications, especially where customer data collection is involved.<\/p>\n To clear this barrier, robust governance frameworks for AI’s ethical and responsible use<\/a> will be needed before widespread implementation.<\/strong><\/p>\n Last year, U.S. President Joe Biden instituted an executive order to support AI development while also providing guidelines for federal agencies to consider when applying AI. This is a first step that may comfort holdouts, but there is still a long journey ahead before the proper regulation is in place.<\/p>\n Government regulations and ethical transparency are macro AI challenges for business leaders. Next, we dig into some of the more day-to-day implications.<\/p>\n One of the most common narratives around AI deployment is its impact on the workforce, specifically job displacement. The SAG-AFTRA strike and resulting regulations<\/a> are good examples of how this fear can come to life. Already, AI has changed the way that people work. In some cases, it has been used to take over tasks that humans traditionally handled.<\/strong><\/p>\n While this can be scary, there are opportunities for you to amplify employee work experiences rather than eliminate them. Offering upskilling opportunities and rolling out processes to gradually educate employees will result in better outcomes across the board. We’re only just now seeing the initial impact of AI on employees, so it’s important to implement a culture of continuous learning and iteration. Those who succeed will actively partner with their employees to understand how AI can enhance creativity and efficiency. Check out our recent article for more in-depth strategies for building AI trust<\/a> with employees.<\/p>\n Legacy system integrations also present AI challenges and risks. According to new insights from Chief, 30 percent of business leaders<\/a> view keeping up with technological advancements as a top organizational challenge. AI’s novelty presents interoperability challenges. They don’t want to change existing systems, so they seek technology that can easily integrate.<\/p>\n Addressing these AI challenges starts with a business mindset. You must understand what your primary goals<\/a> are and look at how AI can help you get there.<\/strong> When you can align your objectives with the key benefits of AI, you can make the case that the trade-off of change for the sake of innovation will ultimately get you to a more efficient and profitable place in the long term. On average, businesses can expect a six percent to 10 percent<\/a> revenue increase from adopting AI.<\/p>\nBuilding Confidence in AI with Transparency and Ethical Compliance<\/h2>\n
Navigating AI Governance and Compliance<\/h2>\n
Addressing Talent Impacts and Operational Barriers<\/h2>\n