In an era where artificial intelligence (AI) is redefining the boundaries of technology, the integration of AI-powered solutions has become a pivotal force in transforming tech development. Understanding the effects of recent developments means delving into AI’s profound impact across various sectors, including customer experience (CX), product development, and the broader AI market. AI tools are not just enhancing efficiency; they are revolutionizing the approach to problem-solving and innovation. For product managers, leveraging AI means harnessing the power of advanced algorithms, machine learning (ML), and data analytics to create smarter, more responsive products. This transformation is driving unprecedented changes in how products are designed, developed, and brought to market.
The development process
For product managers, two of AI’s biggest strengths are its ability to identify patterns and derive insights from the vast amounts of data available to organizations today and the potential for increased efficiency and automation. Repetitive or tedious tasks are particularly obvious targets for AI-driven automation, as are various monitoring and reporting tasks. Project management tools like Asana and Monday already use AI to improve workflows and predict project timelines. Research from McKinsey suggests that of the time employees spent on work activities in 2023, up to 50 percent could be automated with generative AI as early as 2030. These automation and efficiency improvements will speed up various processes, increase efficiency, and enable humans to work more effectively.
Advanced AI and ML algorithms are critical tools for sorting and utilizing large data sets. These algorithms can quickly synthesize more data than humans, revealing new marketing opportunities. This is particularly useful for unstructured data, which includes audio files, emails, social media data, and images, and often contains significant insight into customers’ needs and experiences.
AI can also be a powerful creative companion during the product development process. A recent McKinsey article outlined how AI-powered tools can boost creativity and productivity at all stages of the product design process, from market and user research through concept development, refinement, and testing. Once a team has identified a problem or challenge to address, it can use AI tools to help spark and then build on ideas, develop concept images and prototypes, and refine final products.
Customer-focused innovation
A focus on customers’ needs and a desire to deliver a first-class customer experience drive successful development processes. AI helps organizations provide improved customer service in a variety of ways, from delivering a more personalized experience to supporting human customer service professionals. In a survey conducted by Deloitte, CX professionals identified ways AI could improve efficiency and customer experience with employees. For customer service professionals, AI saves time by providing data or answers to customer questions. When interacting with customers directly as a chatbot, for example, AI can provide enhanced service by considering various factors, such as the customer’s language, preferences, ethnicity, or socioeconomic background, delivering a more humanized customer experience.
Today, personalization drives much of the customer experience. Organizations have access to an increasing amount of data on customers’ preferences and needs, which allows for improved user segmentation and profiling. Even when not interacting with customers directly, AI offers an opportunity to create a personalized customer profile more efficiently and effectively, which enables companies to better address their customers’ needs. Many large organizations already use AI to deliver personalized recommendations and products. For example, ML powers Netflix’s well-known customized recommendations, and early last year, Spotify unveiled an AI DJ that chooses what to play based on a user’s music taste and provides commentary in a “stunningly realistic voice.” Even the mental health and wellness company Headspace uses AI to match coaches and users. Product managers can leverage AI tools to directly provide tailored service to individual customers or identify needs and desires within the wider customer base.
Human oversight
While the potential benefits of AI are immense, especially with improved efficiency and data analytics, the need for human oversight will likely never disappear; AI tools are not foolproof. For example, large language models (LLMs) essentially function as predictive machines that generate answers based on patterns in their training data, which means they can reinforce biases and stereotypes. LLMs can even “hallucinate” by returning absurd or false answers. Google made news earlier this year when various hallucinations produced by its AI Overview went viral. To reduce risks like these, it’s vital for humans to check the information received from AI tools to ensure quality and accuracy.
Ultimately, AI is still a tool. It can generate responses, predictions, or suggestions from data accumulated from a variety of sources, but humans make the final decisions about which ideas or projects best serve an organization’s needs. Human oversight is also critical for ethical and regulatory considerations. An article from the Harvard Business Review identified several instances of AI tools used without sufficient oversight, resulting in inaccurate, biased, and prejudiced outputs. These included a Microsoft chatbot that made derogatory and incorrect remarks and an Amazon recruiting tool that was eventually shut down when the company was unable to remove its gender bias. To avoid issues like this, it’s imperative to test AI solutions or suggestions to verify their accuracy, benefit, and effectiveness before projects or products are launched.
AI and other emerging technologies are already reshaping the future of nearly every industry, not only tech product development. While agility and a willingness to embrace change are musts for organizations looking to leverage these technologies, sustainable long-term success is rooted in innovation and customer experience. The best way to measure the success of any AI-driven tool is through customer satisfaction. By focusing on delivering first-class customer service, businesses naturally will discover the best use of technologies like generative AI and ML.
About the Author:
Archana Ravi is a product manager at a Fortune 500 software company with more than 10 years of experience in technical program management, customer experience, product development, and operations. Her current focus is on improving the customer experience, leveraging her expertise in product management, customer experience optimization, innovation, and strategic planning. Archana Ravi is dedicated to driving cross-functional collaboration and implementing artificial intelligence and machine learning solutions. Connect with Archana Ravi on LinkedIn.
