The state of AI in the consumer industry
The Consumer industry, as we view it, encompasses a wide range of businesses including Consumer Products, Retail, Automotive, Lodging, Restaurants, Travel, and Transportation. What these seemingly disparate businesses have in common is a strong and defining focus on serving customers – and a common set of current and future business issues they are trying to solve.
- What are the key business issues and opportunities?
- How can AI help to achieve a competitive advantage?
- How will AI impact the consumer industry's future?
Many consumer-related businesses are actively exploring ways to harness the power of AI, and many valuable use cases are emerging. However, AI adoption and maturity levels vary widely for a variety of reasons, including: scalability due to data quality and complexity; organizational constructs and talent scarcity; and lack of trust.
of consumer c-suite executives agree that the deployment of AI can help improve customer care.
of consumer c-suite executives say that AI can greatly enhance inventory management by helping to effectively manage costs and buyers’ needs.
Facing the top obstacles
Most consumer-related companies are increasingly deploying artificial intelligence, yet many are still facing challenges in their AI initiatives:
- Moving from concept to scale: This is particularly difficult since many have large legacy data and analytics platforms, decentralized data and analytics operations, and (in many cases) decentralized authority and responsibility – whether across business units, or even more so, across independently operated franchises. This often leads to data being inconsistent, poor quality, and limited in usability, which is a big problem for AI systems, which tend to be extremely data-intensive (with the quality of the input having a direct impact on the quality of the output).
- Aligning and integrating AI across the business: Often, AI is used in isolated pockets of the organization – sometimes working with IT, sometimes not. However, in order to achieve the full benefits of AI at scale, an integrated business and technology plan (and case for change) is required.
- Continued lack of trust: There continues to be a lack of trust in AI and what it can and should be allowed to do. Tackling this issue requires a coordinated change management approach for communicating with leaders and teams and hearing/addressing their concerns. For businesses without direct control over this critical element, deploying AI at scale can be difficult to achieve.
Where are the opportunities for consumer companies?
Over time, the task of building trust in AI will likely get easier as AI technologies become more widely accessible – and accepted – for businesses and consumers alike.
Every successful AI deployment fuels a virtuous cycle that improves people’s understanding of what AI can do and helps expand the scale and scope of future AI use cases. Also, because these learning algorithms and solutions reduce the effort it takes to deliver insights and decisive action, the resulting operational improvements increase confidence and drive increased return on investment.
Looking ahead, AI systems for consumer-related businesses are expected to become increasingly autonomous – changing the way companies move goods, enabling increased mobility, and transforming how they manage their workforces – while at the same time becoming increasingly interconnected across entire ecosystems, enabling AI to add value to business processes from end to end.
of consumer-related business executives said they are likely to invest in AI and automation in customer interactions over the next two years.
Understanding what can be achieved by AI today
From increased efficiency and profits to improved decision-making, explore the way that consumer-facing businesses are harnessing the power of AI in our five use cases:
More than fleeting improvements
Fleet Network Optimization
Use AI and machine learning to create optimized network plans for ground and air fleets – maximizing efficiencies within and across business lines.
Next level personalization
Personalize and improve the customer experience through consolidated platforms that harness the power of AI, machine learning, and natural language processing.
Mix and match
Items Assortment Planning Optimization
Use AI to determine which items should be stocked or substituted to optimize sales, margins, inventory, and customer satisfaction.
Closing the loop on supply and demand
Consumer Demand Planning, Forecasting, and Marketing
Use AI to augment marketing and improve demand planning and forecasting.
Customer contact in the AI era
Digital Contact Center
Use AI technologies such as natural language processing and machine learning to improve the contact center experience and overall customer satisfaction.
Navigating the future of AI in the consumer industry
Intelligent automation is transforming the consumer industry in unexpected ways. As AI capabilities mature, they open up entirely new ways of doing business that can increase operational agility, improve the quality and speed of decision making, and enhance the customer experience.
Explore our five emerging AI use cases in the consumer industry to uncover future-driven opportunities