We are getting into the busy season for corporate leadership when managers from all functions are meeting to evaluate performances and plan for what is next. After a year of rising costs, persistent supply chain issues, and ongoing efforts to meet sustainability targets, there are plenty of challenges. But one topic still seems to be front and center on everyone’s mind—artificial intelligence (AI)/generative AI (GenAI).
It is the age of innovation FOMO, and leaders are overwhelmingly being asked to incorporate some AI/GenAI functionality into their operations so their companies are not left behind. But amid all the excitement, it is important to remember that innovation is a process, not a solution. To create lasting impact, organizations must ensure any new capabilities are matched to specific needs, evaluated for risk, and tied to measurable business outcomes.
Here are three common questions/challenges from corporate leadership teams and how AI/Gen AI can help, along with examples from several industries where this innovation is already making a difference:
It feels like there is new technology being introduced every day, and our budget is already stretched thin. How can we determine where our investment in AI/GenAI innovation will yield the most ROI?
Paradoxically, when everyone starts to speed up, it is time for your leadership team to slow down and focus on the fundamentals. First, make sure everyone is aligned with how you are thinking about AI/GenAI. AI has been around for a while now, and at a high level, it is best to think about it as a tool to analyze data, gather insights, and work smarter. GenAI is more nascent and involves how to use all those insights to autonomously generate actual content and recommendations. Every company can benefit from incorporating AI/GenAI capabilities, but it helps to democratize the transition so workers feel valued.
Companies looking to build an enterprise-wide AI ecosystem can take inspiration from the “Kaizen” method pioneered by Toyota. This approach involves continuous improvement, where teams across all levels of an organization are encouraged to make small, incremental changes to eliminate waste and optimize processes. Not only does this help identify where AI/GenAI might have the most impact, it starts to foster a “test-and-learn” mindset that will permeate through the culture of an organization and result in happier, more productive employees.
Focus On: Transportation Industry
In transportation, AI/GenAI is helping companies improve everything from demand forecasting and inventory management to predictive maintenance and route optimization. Delta Air Lines uses GenAI to analyze customer data and provide personalized travel experiences, UPS uses its AI-powered ORION system to adjust delivery routes as traffic conditions change, and the New York City MTA deploys AI to cut down on fare evasion.
As we scale, we’re finding that communication gaps are developing between the C-Suite and functional leadership, especially IT. How can we use AI/GenAI to create more effective internal and external messages without losing our authenticity?
While GenAI can produce remarkably realistic messages, it is important to maintain certain standards to safeguard brand reputation. In other words, style counts, and people want to communicate in a way that feels genuine. According to a recent survey from PwC, establishing that trust is increasingly critical among the C-Suite, consumers, and employees, and 93% of business executives agree that building and maintaining trust improves the bottom line. The same is true within an organization, and it is common for workers to be cautious about new management directives that ring false, or distrustful of new technology that is not put in the proper context.
Miscommunication wastes time and money, slowing down innovation and operational efficiency. GenAI can proactively address this by analyzing huge datasets of previous interactions (with customers and employees) to model potential reactions, offer real-time insights, and serve as a bridge between two “languages” (i.e. what the business wants to say, and how it is received by customers/employees). When executives have timely, AI-driven insights into performance, they can better align operational decisions with strategic goals. And when workers are made a part of the process through continuing education and upskilling initiatives, AI/GenAI can be viewed as an asset instead of a threat.
Focus On: Retail Industry
Post-pandemic consumer behavior has shifted dramatically, so it is critical that retail companies use AI to analyze customer data and deliver highly personalized service, product recommendations, and marketing campaigns. At scale, AI can also be used to help predict future behavior, enabling targeted sales efforts and improved customer acquisition. The future in this space is exciting, and poised to totally revolutionize how we shop. For example, Amazon continues to refine its AI-empowered “Just Walk Out” technology that analyzes data from cameras and in-store sensors to power checkout-free stores worldwide.
In our industry, we deal with large amounts of sensitive customer information and we are concerned about how introducing new technology might expose our data to increased vulnerabilities. What are some benefits to using AI/GenAI in these industries, and how can we mitigate risk?
Like medicine, the golden rule in AI/GenAI transformation is, “First, do no harm.” Certain industries like healthcare and financial services have had slower widespread AI adoption due to their complex, highly-regulated environments, but there have been huge strides made in specific functions. The most visible evidence is in customer service, where AI-powered chatbots and virtual assistants can provide 24/7 support and help answer common logistical questions. For example, since its launch in 2018, Bank of America’s AI-powered chatbot “Erica” has responded to 800 million inquiries from over 42 million clients and provided personalized insights/guidance over 1.2 billion times.
Ironically, despite lingering concerns over security in sensitive industries, AI/GenAI has enjoyed a net positive impact in the field of fraud detection. Fraud is an endemic problem in finance that is only getting worse, and experts predict fraudulent banking will cost the industry $48 billion by 2029. AI algorithms can scour huge datasets to identify anomalies that may indicate fraudulent activity and security teams can establish thresholds for suspicious activity, triggering interventions only when these thresholds are exceeded. GenAI can also help automate certain routine tasks (data entry, reconciliation, etc.) and free up time for teams to make more nuanced decisions (loan approvals, defaults, etc.) that benefit from deeper human analysis.
Focus On: Banking Industry
In 2021, PNC launched PINACLE, a cash-management application that uses AI and machine learning (ML) to train from a company’s historical data. Once the module is trained, it can be updated daily and produce a rolling forecast to help predict future cash flow, reduce version control issues, and gain better insight into current and future cash positions for various scenarios. AI is also helping to empower investors, especially those focused on sustainability. Morgan Stanley advises that AI’s analytical capabilities can help “identify companies with strong ESG performance, mitigate risks, and shape portfolios that better align with sustainability objectives.”
Setting the Tone for 2025
Companies have a once-in-a-lifetime opportunity to optimize their operations with AI/GenAI, but that sort of transformation requires discipline. Headed into next year, leadership needs to make clear that: (1) change is a team sport; (2) the ROI of any new technology must be tied to specific business outcomes; and (3) speed without direction creates chaos. By tuning out the hype and staying focused on meaningful impact, organizations will be set up for lasting success in this exciting new era of innovation.
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