Artificial intelligence (AI) is no longer a buzzword but a strategic imperative for businesses across all industries. It’s no longer a question of if businesses should adopt AI but rather how they can do so effectively. AI innovation shapes new paradigms in business operations, allowing companies to solve complex problems, make informed decisions, and deliver personalized customer experiences.<\/p>\n
There are AI use case opportunities in multiple industries (including retail, healthcare, transportation and logistics, manufacturing, financial services, and marketing and advertising) that can create high-value results by boosting efficiency, cutting costs, and increasing business growth and competitiveness within and across the enterprise. But how can you determine if the ROI from these AI innovation use cases justifies pursuing them?<\/strong><\/p>\n Luckily, there are some standard ROI evaluation techniques for AI use cases<\/a> that can help. Here are several:<\/p>\n Specific ROI metrics can also determine how much value derives from AI use cases for particular industries. For example, in retail, these would include increases in average order value or customer lifetime value because of higher conversion rates from personalized recommendations.<\/p>\n In healthcare, reductions in misdiagnosis rates save money and improve patient outcomes. AI can also lead to lower hospital readmission rates or emergency room visits, which also produce cost savings and better patient outcomes.<\/p>\n For manufacturing, decreased maintenance costs and unscheduled downtime are shown as a percentage of the total maintenance budget or lost production revenue, and fewer maintenance costs and unplanned downtime are shown as a percentage of total production cost or revenue.<\/p>\n Up until recently, the impact AI has had on how business gets done, as indicated in the above types of use cases, has involved predictive analytics and its usage of machine learning algorithms.<\/strong> But now, there is a higher-order AI incarnation coming into play with heretofore-unknown enterprise-wide content-generation capabilities.<\/p>\n We have already crossed this (potentially) highest frontier in AI innovation \u2013 generative AI<\/a> \u2013 to deploy AI technologies for enterprise growth. There are abundant examples of use cases<\/a> where innovative AI is transforming business operations in big ways.<\/p>\n There are even more use cases for AI innovation<\/a> in areas such as call centers, customer support and service, and fraud detection.<\/p>\n The extent to which AI can automate business processes to drastically reduce or even eliminate labor-intensive human task work can yield significant benefits in terms of improved productivity and even higher employee morale and motivation. Nevertheless, a caveat about overreliance on AI is appropriate here.<\/p>\n There are risks involved in integrating artificial intelligence unless human beings supervise it.<\/p>\n One of the significant risks that generative AI<\/a> presents is fabricated information. Training data that leads to biased or faulty responses may be hard to detect unless a person can monitor and verify what\u2019s happening. Salesforce, for one, is emphasizing this point in determining how it will enhance its Service Cloud platform<\/a> for contact center operations.<\/p>\n Another risk is the huge amount of copyrighted data on the internet that trains large language models. Some of the outputs may flout intellectual property safeguards and copyright laws. Without transparency or source references that reveal how you generated the outputs, you must have somebody scrutinize the results to see that they don\u2019t violate copyright or IP laws.<\/strong><\/p>\n Not only is AI enriching the content that businesses share with their customers, but it\u2019s also making it possible to convey that information more efficiently and empathetically, with better bottom-line results.<\/p>\n Conversational AI supports enterprise growth by breaking down communication barriers between businesses and their audiences. By making custom self-service options easier to use, it creates a more personalized and efficient support experience. Compiling vital customer data during interactions turns prospective customers into actual ones.<\/p>\n By helping customers quickly find and buy items and aiding this process with suggestions that reflect their preferences and past behaviors, conversational AI is creating a better shopping experience, more customer engagement, and higher retention and conversion rates. These are only a few real-world use cases where conversational AI has made its mark.<\/p>\n The two most distinct types of conversational AI are chatbots and virtual assistants<\/a>. While chatbots have their place within generative AI, their rule-based and script-focused nature limits their ability to perform tasks beyond preset parameters. Their dependence on a chat interface and a menu keeps them from giving helpful answers to customer questions and requests. On the other hand, virtual assistants are sophisticated programs that understand natural language voice commands and can perform tasks for the user.<\/p>\n With all the technological possibilities AI presents for businesses to explore, it can seem like a kid-in-a-candy-shop dilemma to decide which ones make sense for you. But AI adoption can\u2019t be an impulse buy or an indulgence if it\u2019s going to work.<\/strong> Instead, it\u2019s going to take a holistic approach that informs an overarching business plan, as well as a risk vs. rewards analysis that looks at every aspect of the enterprise to determine when, whether, and how AI can help you.<\/p>\n Consequently, strategic planning, as well as addressing the challenges involved in data and organizational change management<\/a>, are crucial to successfully adopting AI innovation within your businesses.<\/p>\n Developing a clear long-term strategy<\/a> is essential to effectively deploying AI for business projects. Such a strategy has several components.<\/p>\n For starters, you must understand AI and what it can do so that you can determine which AI applications and use cases will create tangible business value for the enterprise. After that, you\u2019ll need to prioritize the business cases to implement. Whether it\u2019s enhancing customer services, improving productivity, or automating labor-intensive tasks, you must determine how \u2014 and if \u2014 AI can solve a business problem related to that objective.<\/p>\n AI is only as good as the data it uses. Ensure your AI algorithms are based on high-quality data and that data labeling is performed accurately. Solving data management challenges in AI adoption is about establishing absolutely dependable data quality and availability<\/a>. Too many businesses have insufficient or low-quality data to work in an AI environment. High-value and optimized data, in tandem with good data management, is how to build AI models that deliver results. Getting that kind of data requires a coherent strategy<\/a> for gathering, managing and securing data and processes to collect, clean and store data.<\/strong><\/p>\n The people involved in AI adoption are critical to its success, too. There are three parts to this.<\/p>\n The timing and scope of your AI adoption influence<\/a> all other aspects of your strategy. It\u2019s best to have a big-picture, long-term vision. Start small after extensively testing prototypes and with a few pilot projects keyed to specific, modest business objectives. Scale up rapidly once your initial experiences pan out.<\/p>\n Finally, you must plan to be ethical, with an eye toward strict regulatory compliance, data privacy protections, algorithmic transparency to build trust, and human instead of robotic responsibility for critical decision-making. You should also prepare employees with jobs AI innovation imperils to transition into new skills for the brave new world.<\/p>\n To some extent, the organizational change management challenge in AI involves change practitioners’ unfamiliarity with AI. They have limited experience with it, don\u2019t know how to use it, and are afraid of risks they can\u2019t define. An October 2023 Prosci study<\/a> found that 53 percent of respondents cited a dearth of AI use cases in change management as a barrier to adopting AI innovation.<\/p>\n Yet it\u2019s clear that deploying AI as a change management strategy yields definite benefits.<\/strong> Some 30 percent of survey participants cited the increased efficiency AI delivered by automating processes, rapidly analyzing data, shortening response times, and creating draft communications and change management plans.<\/p>\nROI Methods and Metrics: Is AI Innovation Worth it?<\/h2>\n
\n
The Implications of Generative AI Integrations<\/h2>\n
\n
Don\u2019t Abandon Governance for AI Innovation<\/h3>\n
The Conversational AI Contribution<\/h2>\n
Setting the Stage for Successful AI Adoption<\/h2>\n
\n
Embrace Change with AI Innovation for Business Growth<\/h2>\n