AI copilots – such as chatbots and virtual assistants – are rapidly evolving from optional tools to essential assets, helping businesses unlock significant value across customer engagement and internal operations. For instance, websites using chatbots see a conversion rate jump from 10% to 33%, according to a study by Glassix. This indicates that AI copilots aren’t just about automating tasks; they’re about driving tangible results.
Consider the impact on revenue alone: online stores report a 7-25% increase in sales directly attributed to chatbot integration, as noted in Chatbots Magazine. And it’s not just about boosting the bottom line – AI copilots allow companies to enhance customer service capabilities without the need to grow support teams. In fact, research shows that 69% of users prefer chatbots when seeking quick responses, while 40% of customers don’t mind whether their issue is resolved by a human or an AI assistant, as long as it’s solved (Dashly.io).
These figures underscore a trend we’re seeing firsthand at Intershop, where the focus has shifted toward leveraging AI as a strategic, transformational force in commerce. Recently, I had the privilege of hosting a workshop at THE SESS10N by diconium, an event centered on digital innovation, to explore how businesses can strategically implement AI copilots to drive growth, streamline operations, and create memorable customer experiences. Together, our participants and I examined actionable AI strategies, the importance of prioritizing impactful projects, and the need for scalable AI architecture – all essential elements for businesses aiming to thrive in an AI-powered landscape.
In the sections that follow, I’ll share the core insights from our session, designed to equip businesses with the knowledge they need to implement AI in a way that delivers both immediate and lasting value.
Successfully implementing AI goes far beyond introducing new technology – it requires a strategic approach that aligns directly with business objectives and delivers measurable value. In our workshop, we explored essential steps in shaping an AI strategy, including identifying target customer personas and clarifying business goals. Additionally, we defined key areas where a balanced AI approach can drive substantial impact.
To create an effective AI strategy, organizations must begin by clearly identifying their target customer persona. This foundational step ensures that AI initiatives are tailored to meet the unique needs of different customer types. During our workshop, participants highlighted a range of personas they focus on, with B2B customers being the primary target compared to fewer mentions for resellers, retail stores, and distributors/wholesalers. The diversity in these results underscores the importance of defining customer personas early in the AI planning phase.
Whether an organization serves B2B customers, B2C consumers, or other groups, this definition influences everything from AI project prioritization to the personalization features an AI copilot might offer. For example, a B2B-focused company may prioritize predictive reordering or conversational product discovery tools tailored to complex buying cycles. By understanding their target personas, organizations can better align AI initiatives with customer expectations, resulting in more relevant, impactful solutions.
Defining business goals is another crucial step in crafting a targeted AI strategy. During the workshop, participants shared their primary goals for introducing AI assistants, with “increasing customer satisfaction” and “improving product discovery” emerging as top priorities. Other goals included winning new customers, increasing revenue, enhancing conversion rates, and creating a more efficient buying process.
These goals shape the direction of AI projects by providing a clear understanding of what success looks like. For instance, companies aiming to improve customer satisfaction and product discovery might focus on deploying virtual shopping assistants or natural language search. Conversely, businesses prioritizing revenue growth may benefit from predictive analytics that supports personalized promotions or upsell opportunities. Establishing these goals helps organizations measure the impact of AI and align their efforts with broader business objectives.
To achieve the full potential of AI, organizations should focus on areas where AI can drive significant value across both internal operations and customer interactions. A balanced approach in these areas not only enhances efficiency and productivity but also fosters deeper customer loyalty and engagement.
This dual approach – addressing both operational needs and customer experience – ensures that AI investments create value across all areas of the business. With a balanced strategy, companies can achieve both immediate gains and sustainable growth, making AI a powerful enabler for long-term success.
One of the biggest challenges businesses face when implementing AI is determining which projects to prioritize. While AI offers a wide array of potential applications, allocating resources to initiatives with the highest potential for return on investment (ROI) is essential. In our workshop, we introduced the RICE framework a structured approach that helps organizations prioritize projects by weighing potential value against feasibility. This framework considers four key factors: Reach, Impact, Confidence, and Effort.
The RICE framework allows businesses to evaluate each AI initiative based on:
By calculating a RICE score for each project, companies can prioritize initiatives with the most significant potential impact while ensuring resources are spent wisely. During the workshop, participants identified several high-impact AI projects that could yield substantial benefits.
1. Conversational product discovery
Conversational commerce leverages AI-driven natural language processing (NLP) to allow customers to search for products conversationally. This interface mimics a natural dialog, enabling customers to ask questions and refine their search through follow-up queries. Providing a natural language interface improves personalization and accessibility, allowing customers to quickly find the products they need and deepening engagement with the brand. This initiative scores high in Reach and Impact as it enhances the shopping experience and improves conversion rates, especially in e-commerce settings.
2. Predictive reordering
Predictive reordering uses AI to anticipate when customers are likely to need to reorder certain products, based on past purchase data. For businesses focused on customer retention, this feature provides a seamless way for customers to stay stocked on essentials without additional effort. By facilitating repeat orders, predictive reordering can significantly boost customer loyalty and long-term revenue.
The RICE framework helps companies focus on AI initiatives that align best with business goals, customer needs, and available resources. By selecting high-impact projects like these, businesses can ensure their AI investments offer the clearest path to value, making it easier to realize both immediate benefits and sustainable growth.
Building a strong AI foundation requires more than just implementing individual AI tools; it demands a comprehensive architecture that is scalable, adaptable, and able to support future growth. At the heart of a successful AI copilot system is a layered architecture designed to integrate seamlessly with business operations while ensuring long-term flexibility. In our workshop, we discussed the essential components of this architecture, which can be broken down into three primary layers: the frontend, the AI orchestration & workflow layer, and the data platform/backend. Together, these layers form the backbone of a responsive, scalable AI system.
The frontend serves as the user interface where customers and business users interact with the AI copilot. This layer is essential for creating an intuitive, accessible experience, making it easy for users to communicate with the AI assistant. The frontend typically includes features like chat interfaces, voice recognition, and visual interfaces that support both natural language and image-based interactions.
The AI orchestration & workflow layer acts as the system’s control center, coordinating various AI functionalities and handling complex interactions. This layer is responsible for managing workflows, calling specific AI modules, and orchestrating responses to user queries.
At the core of any scalable AI system is a robust data platform, which serves as the foundation for storing, processing, and analyzing data. This backend platform supplies the copilot with the data it needs to deliver accurate, personalized responses and make data-driven decisions.
Investing in a well-structured, scalable architecture enables companies to evolve their AI capabilities to meet both current and future needs. This approach brings several benefits:
A thoughtfully designed AI copilot architecture is essential for businesses aiming to grow their AI capabilities sustainably. By investing in a layered setup with a robust frontend, a flexible AI orchestration & workflow layer, and a data-rich backend, companies can support continuous innovation and remain responsive to evolving market demands.
One of the most effective ways to stay agile in a fast-paced AI landscape is through partnerships with specialized providers. At Intershop, we collaborate with trusted AI partners like SPARQUE.AI to bring proven, market-ready solutions to our clients, accelerating AI implementation and reducing the time, risk, and resources required to develop new technologies from scratch.
One of our flagship partnership-driven solutions is the Intershop Copilot, a high-impact AI solution tailored for B2B customers. Designed as an AI-powered procurement and service assistant, the Copilot leverages generative AI and Large Language Models (LLMs) to precisely understand and respond to customer inquiries. With a dialog-based interface, the Copilot delivers a personalized shopping experience tailored to the unique needs of B2B users.
Through seamless integration with SPARQUE.AI’s powerful product discovery engine, the Copilot generates targeted product recommendations and customized search results that drive higher average order values and unlock valuable cross-selling opportunities. Key features – such as cart management, image-based product recognition, and voice control – optimize the procurement process and enhance customer satisfaction, making the Intershop Copilot a transformative asset for B2B e-commerce.
By collaborating with specialized providers, businesses gain access to cutting-edge technology and industry expertise that deliver distinct advantages:
With a partner-based approach, companies can confidently access advanced AI capabilities and mitigate risks, enabling them to innovate at a faster pace and deliver enhanced value to their customers.
For companies ready to harness the power of AI, a clear, strategic approach is essential. At Intershop, we’re dedicated to providing the insights and tools needed to make AI investments that pay off, helping our clients drive efficiency, improve customer experience, and stay ahead in a rapidly evolving digital landscape.
By building a thoughtful AI strategy, prioritizing high-impact initiatives, and establishing a scalable architecture, we can all leverage the power of AI to create meaningful change in e-commerce. Our workshop insights are just the beginning, and we look forward to continuing this journey with businesses as they navigate the exciting possibilities of AI.