In an era of rapid technological evolution, businesses can use AI to foster growth and maintain competitiveness. We explore how the burgeoning fields of generative AI and conversational AI present opportunities for innovation, operational efficiency, and improved customer experiences, driving enterprise growth.
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.
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?
ROI Methods and Metrics: Is AI Innovation Worth it?
Luckily, there are some standard ROI evaluation techniques for AI use cases that can help. Here are several:
- Cost savings analysis evaluates the direct cost savings of an AI solution (e.g., reduced labor costs) and compares them with the expenses of the solution over time. Hence, you might calculate how many manual labor hours you eliminate and quantify the related cost savings.
- Revenue generation analysis assesses the indirect effect of AI on various ways to build revenue, then estimates how much additional revenue derives from actions such as better product recommendations or pricing strategies. Then, you would measure how much-increased sales revenue happened because of AI-generated recommendations.
- Productivity enhancement analysis measures how much AI-driven automation, optimization, or decision support systems improve productivity by quantifying the time savings, efficiency gains, or throughput improvements employees accomplish. An example would be figuring out how much time AI automation saves vs. manual processes in completing a task.
- Customer satisfaction and loyalty analysis tells how much AI-instigated product or service enhancements improve customer satisfaction metrics such as customer retention rates and quantifies their impact on customer engagement and overall experience. Gauge how much customer satisfaction scores or repeat purchase rates rise following the activation of AI-powered customer service chatbots or recommendation engines.
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.
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.
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.
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. But now, there is a higher-order AI incarnation coming into play with heretofore-unknown enterprise-wide content-generation capabilities.
The Implications of Generative AI Integrations
We have already crossed this (potentially) highest frontier in AI innovation – generative AI – to deploy AI technologies for enterprise growth. There are abundant examples of use cases where innovative AI is transforming business operations in big ways.
- Software developers are writing code, particularly complex code, more efficiently. They’re using generative AI to automatically update and maintain code over different platforms, detect and eliminate bugs in the code, automate code testing, and quickly create technical and other documentation that coders need.
- Generative AI has given product designers the ability to quickly evaluate and automatically adjust product development design concepts. It also facilitates structural optimization, guaranteeing products are strong and durable while saving on materials, resulting in big cost reductions.
- In sales and marketing, generative AI empowers marketers to engage in highly personalized communications with prospective and existing customers across multiple channels, including email and social media, and to assess data to understand consumer behavior patterns and develop content that moves their target audiences. Sales teams benefit from deep analytics, insights into customer buying habits, and the technology’s ability to target and segment audiences and identify high-value leads.
- Project managers can improve project management and operations by delving into and producing instant summaries of vital business documents. This saves time and lets users focus on big-picture strategy instead of everyday business management.
There are even more use cases for AI innovation in areas such as call centers, customer support and service, and fraud detection.
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.
Don’t Abandon Governance for AI Innovation
There are risks involved in integrating artificial intelligence unless human beings supervise it.
One of the significant risks that generative AI 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’s happening. Salesforce, for one, is emphasizing this point in determining how it will enhance its Service Cloud platform for contact center operations.
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’t violate copyright or IP laws.
Not only is AI enriching the content that businesses share with their customers, but it’s also making it possible to convey that information more efficiently and empathetically, with better bottom-line results.
The Conversational AI Contribution
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.
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.
The two most distinct types of conversational AI are chatbots and virtual assistants. 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.
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’t be an impulse buy or an indulgence if it’s going to work. Instead, it’s 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.
Setting the Stage for Successful AI Adoption
Consequently, strategic planning, as well as addressing the challenges involved in data and organizational change management, are crucial to successfully adopting AI innovation within your businesses.
Developing a clear long-term strategy is essential to effectively deploying AI for business projects. Such a strategy has several components.
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’ll need to prioritize the business cases to implement. Whether it’s enhancing customer services, improving productivity, or automating labor-intensive tasks, you must determine how — and if — AI can solve a business problem related to that objective.
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. 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 for gathering, managing and securing data and processes to collect, clean and store data.
The people involved in AI adoption are critical to its success, too. There are three parts to this.
- Ensure leadership buy-in across the enterprise by informing the people in charge about the benefits, risks, anticipated investments, and ROI of adopting AI.
- Find people from within your industry or similar businesses who have effectively developed and deployed similar AI projects and learn what it would take to do the same thing yourself.
- Decide whether to develop in-house human resources or outsource the project to obtain the expertise necessary to create, execute, and manage the appropriate AI technologies.
The timing and scope of your AI adoption influence all other aspects of your strategy. It’s 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.
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.
To some extent, the organizational change management challenge in AI involves change practitioners’ unfamiliarity with AI. They have limited experience with it, don’t know how to use it, and are afraid of risks they can’t define. An October 2023 Prosci study found that 53 percent of respondents cited a dearth of AI use cases in change management as a barrier to adopting AI innovation.
Yet it’s clear that deploying AI as a change management strategy yields definite benefits. 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.
Embrace Change with AI Innovation for Business Growth
The groundbreaking technological advancements in AI have the potential to revolutionize all aspects of business operations, making them more productive, efficient, and profitable. Additionally, AI can help ensure foolproof data security, regulatory compliance, and the establishment of an unwaveringly ethical AI culture.
All of this depends upon enterprise leaders’ willingness to do their due diligence about ROI prospects, the potential risks and rewards of AI adoption, and the strategic planning and function management challenges and objectives that must be addressed to fully realize AI’s business potential.
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