Adoption and change in e-commerce and retail
Enterprise AI adoption in e-commerce and retail improves decision-making by analysing sales data, customer feedback, and inventory management, enabling category managers to optimise product assortment and shift supervisors to identify potential issues, then act while the deviation is still small.
This week, a category manager at a large retailer must decide which products to feature in a promotional campaign, and the decision will be based on a combination of sales data, customer feedback, and intuition. However, this decision-making process can be improved by leveraging enterprise AI adoption in e-commerce and retail, which enables organisations to query their own data in natural language and get answers in real time.
Complexity in e-commerce and retail
E-commerce and retail present unique challenges that make it harder to make informed decisions, such as assortment decisions, returns and their root causes, and promotion effectiveness. For instance, a shift supervisor checks the maintenance log to identify potential issues that could impact inventory management, while a category manager compares two suppliers to determine which one can provide the best products at the most competitive prices. Most people get wrong the idea that these decisions can be made solely based on historical data, but they are wrong because they fail to account for the dynamic nature of customer preferences and market trends.
A capable system like AsscherAi can help organisations to better understand their data and make more informed decisions, but it requires a deep understanding of the underlying complexities of e-commerce and retail. By analysing data from various sources, such as customer feedback, sales data, and inventory management, organisations can identify patterns and trends that can inform their decisions.
Attaching new capability to existing routines
One of the key factors that determines the success of enterprise AI adoption in e-commerce and retail is the ability to attach new capability to a routine that already exists. This means that organisations should identify existing processes and workflows that can be improved or automated using AI, rather than trying to create new ones from scratch. For example, a category manager can use AsscherAi to analyse sales data and customer feedback to identify opportunities to optimise product assortment, and then use this information to inform their decisions.
By building on existing routines, organisations can reduce the risk of disruption and increase the likelihood of successful adoption. This approach also enables organisations to leverage their existing knowledge and expertise, rather than trying to acquire new skills or hire new staff.
Who needs to be able to ask
When it comes to enterprise AI adoption in e-commerce and retail, it is not just about who needs the answer, but also who needs to be able to ask. This means that organisations should consider the needs of various stakeholders, including category managers, shift supervisors, and customer service representatives, and ensure that they have the ability to query the system and get relevant answers. A category manager may need to ask questions about sales trends and customer preferences, while a shift supervisor may need to ask questions about inventory management and logistics.
By enabling multiple stakeholders to ask questions and get answers, organisations can increase the value of their AI investment and improve decision-making across the organisation. This approach also enables organisations to promote a culture of curiosity and inquiry, where employees are encouraged to ask questions and seek answers.
The first wrong answer and rebuilding trust
The first wrong answer can be a major setback for enterprise AI adoption in e-commerce and retail, as it can erode trust and confidence in the system. However, this does not have to be a fatal blow, as trust can be rebuilt by acknowledging the mistake, identifying the root cause, and taking corrective action. For example, if a category manager receives an incorrect answer to a question about sales trends, they can work with the AI team to identify the source of the error and implement changes to prevent it from happening again in the future.
By being transparent and proactive in addressing errors, organisations can rebuild trust and demonstrate their commitment to getting it right. This approach also enables organisations to learn from their mistakes and improve the accuracy and reliability of their AI system over time. You can learn more about how AsscherAi can help organisations to rebuild trust and improve decision-making.
When enterprise AI is not the answer
While enterprise AI adoption in e-commerce and retail can be a powerful tool for improving decision-making, it is not always the right choice. There are certain situations where the complexity of the problem, the availability of data, or the need for human judgement and intuition may make AI a less suitable solution. In these cases, organisations may need to rely on other approaches, such as manual analysis or expert judgement, to make informed decisions. If you are unsure about whether enterprise AI is right for your organisation, you can contact us to discuss your options and determine the best approach for your specific needs.
Frequently asked questions
How can enterprise AI improve decision-making in e-commerce and retail?
Enterprise AI analyses sales data and customer feedback to inform product assortment and inventory management decisions.
What are the challenges of adopting enterprise AI in e-commerce and retail?
E-commerce and retail present unique challenges, such as assortment decisions and returns, requiring a deep understanding of underlying complexities.
How can organisations rebuild trust after receiving incorrect answers from an AI system?
Organisations can rebuild trust by acknowledging the mistake, identifying the root cause, and taking corrective action to prevent future errors.
When is enterprise AI not the right choice for e-commerce and retail decision-making?
Enterprise AI may not be suitable when the complexity of the problem or the need for human judgement and intuition outweighs the benefits of AI-driven decision-making.