Across the enterprise, AI promises transformational power \u2013 however, it\u2019s not as simple as just plugging it into the organisation and instantly reaping the benefits. What are some of the top things CIOs need to focus on to avoid any pitfalls, unlock its value, and best position themselves for success with AI? <\/p>\n\n\n\n
Here\u2019s what hype looks like: using AI to \u201cradically transform the way you do business\u201d or to \u201caccelerate comprehensive digital transformation\u201d or \u2013 heaven forbid \u2013 to \u201ccompletely change our industry.\u201d These are big statements \u2013 and absolutely dripping with hype.<\/p>\n\n\n\n
Getting real with AI requires identifying specific use cases within the organisation where a particular type of AI can be deployed to achieve a specific goal. For example, maybe you want to reduce customer churn by 20% and have identified an opportunity to use chatbots powered by large language models to provide more effective customer service. That\u2019s what reality<\/em> looks like.<\/p>\n\n\n\n In separating the hype from reality, organisations gain the added benefit of clearing up any misconceptions \u2013 at any level of the organisation \u2013 about what AI can and can\u2019t do, thus performing an important \u201clevel set\u201d around expectations.<\/p>\n\n\n\n On one side, any AI tool you\u2019re using has access to data, and that means that access needs to be controlled like any other system within your tech stack. The data needs to be secured and governed, and issues around privacy, sovereignty, and any other regulatory requirements need to be thoroughly addressed.<\/p>\n\n\n\n As part of this effort, organisations also need to be aware of the security measures required to protect the AI model itself from bad actors trying to manipulate that model. For example: prompt injection \u2013 inputs that prompt the model to perform unintended actions \u2013 can affect the model and its responses if not carefully guarded against.<\/p>\n\n\n\n Securing your AI system is one side of the coin; the other side is understanding how to apply<\/em> AI to cybersecurity. There are a growing number of use cases here where AI can help identify risks or vulnerabilities by analysing large amounts of data, helping organisations to prioritise the areas they need to focus on for risk mitigation. <\/p>\n\n\n\n In summary? While any usage of AI will require you to \u201cplay defence\u201d on the security front, it will also enable you to \u201cplay offence\u201d more effectively. In that sense, AI has multiple implications for cybersecurity.<\/p>\n\n\n\n When it comes to ROI for any AI investments, don\u2019t narrowly focus on absolute numbers when it comes to metrics like time savings or cost savings. While well-suited to industrial workplaces that are churning out widgets every day, absolute numbers can be an awkward fit when applied to a knowledge work setting.<\/p>\n\n\n\n The advice here for any knowledge-centric enterprise is: Don’t get hung up on the idea of actual dollars and cents or a specific number \u2013 instead, look for a relative improvement from a baseline. So, rather than saying \u201cWe\u2019re going to reduce our customer acquisition costs by $100,000 this year\u201d, it\u2019d be more appropriate to focus on reducing existing customer acquisition costs by 10%. Likewise, don\u2019t focus on each junior associate in the organisation completing five more due diligence projects per calendar year; look to complete due diligence projects in 30% less time.<\/p>\n\n\n\n Change management has always mattered when it comes to introducing new technology into the enterprise. AI is no different: Successful adoption requires a focus on people, process, and technology \u2013 with a particular emphasis on those first two items.<\/p>\n\n\n\n A major challenge is educating the workforce with an eye towards improving their AI literacy \u2013 essentially, enabling them to understand what’s possible and how they can apply AI to their daily workflows. <\/p>\n\n\n\n Know that a centralised model of control that dictates \u201cthis is how you can experiment with AI\u201d is probably going to be ineffective. It will be too stifling for innovative individuals in the organisation. Far better to provide centres of excellence or educational resources to those people who are most inclined to take the initiative and move forward with AI experiments in their team or department.\u00a0<\/p>\n\n\n\n One caveat here: It\u2019s essential to have guardrails in place as teams and individuals experiment with AI, to prevent misuse of the technology. That\u2019s the tightrope that CIOs need to walk when introducing AI into the organisation. Striking the right balance between \u201ctotal control\u201d and \u201cfreedom to explore, but with appropriate oversight and guardrails\u201d.\u00a0<\/p>\n\n\n\n The promise of AI is massive, but only if CIOs adopting the technology focus on the right areas. And that means filtering out the hype, keeping security implications top of mind, redefining ROI, and guiding change with a steady hand. By paying attention to these areas, CIOs can safely navigate a path forward with AI. And ensure that it isn\u2019t just a technology with promise and potential, but one that delivers actual enterprise-wide impact.<\/p>\n\n\n\n Learn more at iManage<\/a><\/p>\n\n\n\n2) Understand the Implications for Cybersecurity<\/strong><\/h3>\n\n\n\n
3) Focus on the Right Kind of ROI<\/strong><\/h3>\n\n\n\n
4) Give Change Management its due<\/strong><\/h3>\n\n\n\n
The Future of AI Depends on what CIOs do next<\/strong><\/h3>\n\n\n\n