The prevailing messaging around Artificial Intelligence (AI) positions it as an essential tool for helping retailers improve their operational efficiency, customer experience, and revenue growth through actionable insights, personalized recommendations, and optimized strategies. However, according to a recent study by Forrester Consulting commissioned by Epicor, many retailers are still in the early stages of their AI journey, facing challenges such as data quality, data management, and education. Retailers need to overcome these barriers before they can even begin their AI journey.
The study surveyed 205 North American retail business and IT decision-makers to determine their current AI use and readiness:
These findings reveal that many retailers lag behind in AI adoption. To move the needle forward on their AI journey, retailers will need to take some action in several key areas:
Improve data quality and management
Data is the fuel for AI—without quality data, AI will not function properly. Retailers need to help ensure that their data is accurate, complete, consistent, and accessible across the organization. They also need to put in place data governance policies and practices to safeguard data security, privacy, and compliance. Implementing AI techniques within an organization helps provide an opportunity to address data management issues. These upgrades may include digitizing paper-based forms, improving authentication standards, and adding data lineage to better monitor data access and control.
Educate leaders and employees on AI
AI is a complex and evolving technology, and retailers need to keep up with the latest developments and best practices. Retail executive teams should educate themselves and their employees on the benefits, challenges, and use cases of AI, as well as the skills and tools needed to implement and manage AI solutions. Management should move quickly to address employee fears or misconceptions about AI’s impact on their roles and responsibilities.
Identify the change and why it's important
To help ensure that a change is effective, it is essential to identify the need, explain why it matters, and establish clear goals. This is especially critical when it comes to implementing AI. It is crucial to articulate the specific task(s) that the AI system will perform in order to manage expectations and define how the technology will enhance effectiveness across the enterprise. For instance, if the desired outcome is to predict a customer's next move, AI will analyze unstructured data to optimize the next best action and drive recommendation engines. This insight can then be used to predict future buying behaviors, shape demand, and maximize margins.
Create a change management team
To manage change effectively when introducing AI, identify enthusiastic team members across various departments and organizational levels. These individuals can serve as advocates throughout the process: before, during, and after transformation. This team should evaluate and delegate tasks, define business and technology direction, set success criteria, and help ensure adoption. Technical and operational leadership should both be represented on this team. When developing a team like this, it’s important to start small, particularly when staff is limited. To help make things run smoothly, consider investing in and collaborating with the right technology partner.
Strategize and plan
To effectively introduce AI into your business, it is important to develop a strong implementation plan. Hold regular team meetings and document the strategy for the change from start to finish, including requirements, budget, timelines, training, and success measurements for a seamless process.
Communication is essential
When introducing AI technology into a workplace, clear and open communication is crucial to the success of the transition. It's important to maintain transparency around key issues and their potential impact on employees. To help ensure the effective integration of AI technology into daily workflows, employees must play an active role and provide genuine feedback on what is beneficial and what is not.
Success criteria
To build success into any retail analytics and AI program, it's vital to set clearly defined criteria. While a pilot program may focus on improving customer experience and operational metrics, it’s equally important to consider the following long-term effects: data management, algorithmic performance, security guidelines, model deployment, transparency requirements, integration with legacy systems, and business adoption.
Choosing the right partner for any technological investment is the key to a smooth implementation. With more than 50 years of experience providing technology solutions in the retail market, Epicor empowers businesses with POS and retail management solutions, including cloud-based platforms. Our retail management tools integrate data from various sources, including POS, CRM, and e-commerce—generating insights and recommendations that can help you drive results:
Retail business insights
Dashboards help provide retailers with a holistic view of their business performance, including sales, inventory, margins, and customer satisfaction.
Retail personalization
Selected features show retailers how to deliver personalized and relevant experiences to their customers, such as product recommendations, offers, and content based on preferences, needs, and context.
Retail business optimization
Tools that enable retailers to optimize their pricing, promotions, and merchandising can also help determine demand and enhance sales performance.
Staffing insights
With the right data, retailers can plan their staffing needs based on peak times, customer traffic, and sales volume.
With Epicor retail management solutions, retailers can transform their business and achieve their top business goals.
