AI Adoption in e-commerce has several compelling benefits that align with the industry’s dynamic, customer-centric nature.  Businesses that understand their customers best can provide a unique shopping experience focused on giving them exactly what they need, which is key to thriving in the competitive online arena.

While AI in e-commerce opens the business up to new opportunities to understand and serve customers, it also comes with underlying responsibilities. Given that the foundation of any AI is data, proper data controls are essential.  Without them, your AI could be unintentionally biased, provide misleading insights and erode the trust you’re trying to build with your customers.

Goldman Sachs predicts that AI has the potential to raise global GDP by 7% making it an unavoidable consideration for businesses looking to grow and gain a competitive advantage.  

Integrating AI into your e-commerce business involves strategic planning and a focus on risk awareness and cybersecurity. It’s essential to develop a risk mitigation plan and align AI adoption with your broader IT strategy and security policies.

So, what are the steps you should take and the risks you will face?

5 Steps to Adopt AI in Your E-commerce Business

Adopting AI isn’t just about processing more data, it’s about translating that data into customer insights you can leverage to drive growth.

If you decide to adopt AI in your e-commerce business, there are several steps you should take:

Step 1: Define Your AI Goals and Map Them To Business Objectives

Evidence from the field of machine learning has shown that setting targeted goals, like improving customer segmentation, could lead to breakthroughs that directly impact the growth and revenue of your business.  For example, if a key business objective is to enhance customer engagement, focus your AI efforts on CRM tools. Alternatively, if optimising inventory management is a higher priority, you could use AI to forecast demand, manage stock levels and reduce holding costs.

A recent MIT Sloan study that found a targeted approach to AI applications (like segmentation) saw an increase in Customer Satisfaction scores by up to 10%, leading directly to revenue growth.

McKinsey’s State of AI Report 2023 indicated that clear objectives for AI implementation resulted in a 1.7x greater chance of a positive impact on revenue growth. 

Defining clear goals establishes a foundation for using AI to drive business growth and customer satisfaction. By mapping AI initiatives to your strategic vision for the business, you’ll also establish a way to measure the impact on revenue and engagement.

Step 2: Assess Your Data Readiness and Quality

In this Forbes article, Ajith Sankaran, the vice president of Course5 Intelligence, states that in his opinion, “the single biggest reason for the failure of AI and analytics projects involves data.”

One of the most overlooked aspects of AI adoption is the potential for biased outcomes due to inadequate data controls. Unchecked datasets can breed biases and turn AI from a tool to a liability. At Zendata, we firmly believe that if the data is incorrect, incomplete, or unrepresentative, the AI’s decision-making will inherently reflect those biases.

This causes a number of problems such as reinforcing existing stereotypes or providing inaccurate customer insights.  It’s not just a question of data quality - but data ethics.  Your data must be accurate but it must also be diverse and inclusive; this is key to developing a fair and unbiased model. 

Assessing data readiness and quality is a critical step in understanding if your data is suitable for the application it’s intended for, like AI implementation.

For your initiative to be successful, your data needs to be clean, consistent and complete; your data sources need to be connected and the data needs to be readily available. It also needs to be accurately labelled and ready for the AI model to ingest and interpret. 

Ensuring data integrity and addressing data silos can accelerate AI-driven insights - but poor data quality will lead to inaccurate analytics and decisions - which creates unnecessary risks.

Well-organised, diverse and accurate data will improve the quality of all decision-making - not just AI-driven insights -  allowing you to drive business growth and reduce potential risks. The success of AI projects is intrinsically linked to the quality of the underlying data and the controls that govern it. 

Step 3: Choose AI Tools Aligned with Security and Operational Goals

The surge in AI popularity has dramatically increased the number of tools to choose from, with a recent Deloitte survey finding that 47% of businesses think choosing the right tool is a significant challenge.  

Gucci, a leading fashion brand, use Salesforce’s Einstein AI in their client service centre to create conversational replies that advisors can use to provide a unified brand experience, resulting in a 30% increase in conversions. This proves that taking the time to find the right tool will allow you to reap the rewards.

The choice of AI tool has to match the business’s goals and be suitable for the type of data available. When the tool is chosen with a clear understanding of the data it will work with, it’s easier to identify potential biases because the tool can be configured to handle the nuances of the datasets it will process and reduce the risk of perpetuating any existing biases.

Step 4: Integrate AI into Your Tech Stack

A recent report from Accenture revealed that companies that prioritised the integration of AI with their existing tech stack saw a 35% improvement in operational efficiency.  Not only does it add a new dimension to your capabilities, but AI has the power to enhance your use of your existing systems too.

However, this integration must be managed effectively to maintain robust security policies and avoid creating new vulnerabilities in your organisation’s infrastructure.  Integrating new technology increases your attack surface and AI isn’t exempt from this. 

The UK’s National Cyber Security Centre (NCSC) recently warned businesses against the growing danger of “Prompt Injection” attacks. These sophisticated cyber attacks can manipulate AI algorithms, leading to data breaches or compromised decision-making.

Deploying AI should add a new dimension to your capabilities but requires continuous monitoring to ensure security and prevent vulnerabilities from occurring within your tech infrastructure. 

Step 5: Monitor and Refine AI Performance

Business strategies evolve and your AI tools need to evolve with them to remain effective.  Continuous monitoring of the performance of your AI systems helps you to stay ahead of vulnerabilities as the technologies evolve and to maintain the optimum performance of the model.

A TechCrunch article, written by the founders of Fiddler AI, states that “Through real-time monitoring, companies will be given visibility into the “black box” to see how their AI and ML models operate… explainability will enable engineers to know what to look for (transparency), so they can make the right decisions (insights).”

Beyond performance metrics, monitoring your AI tools is a critical step in data protection. At this stage, implementing a tool like Zendata’s Privacy Mapper can help you identify whether or not any Personally Identifiable Information has made its way into your AI model which could result in a loss of data integrity, impartiality and compliance. Effective monitoring is vital to ensuring data protection, compliance with privacy regulations and preventing biases.

Leveraging AI should drive business growth and enhance operational efficiency while keeping your business secure and protected from threats. By taking a holistic approach and following these five steps, you put your organisation in a strong position to take advantage of the benefits AI can bring to the table.

However, the most critical step of all is establishing proper data controls.  Without the proper processes to clean, validate and standardise your data, along with policies for ethical data sourcing, you risk building a flawed AI model. 

What are the Risks Associated with AI Adoption

While AI offers significant benefits, it also presents certain risks that need to be considered and mitigated.  According to a Fishbowl survey, 43% of professionals have used ChatGPT and nearly 70% did so without their boss' knowledge - meaning businesses are exposed to additional risks and compliance violations unknowingly.  

According to a KPMG survey, only 6% of organisations have a dedicated team in place to evaluate the risk of AI and consider how to mitigate it.  However, the risks associated with AI adoption extend beyond operational challenges - they also incorporate ethical challenges related to bias in the data used to train them.

So, what are the risks associated with AI adoption in E-commerce?

Risks in Data-Driven Decision-Making

Organisations rely on data to make decisions and, when leveraging AI in the process, the decisions are directly influenced by the quality and integrity of the data the model is trained on. 

If the data is inaccurate, biased or has been manipulated at any stage, it could result in flawed decision-making, like recommending low-quality products. This could lead to serious consequences for the business - like loss of revenue, a data breach or fines for non-compliance with data protection regulations. 

Inaccurate data can enter systems in a number of ways, either through data drift or data poisoning. Data drift is a change in model input data and can lead to the performance of the model deteriorating over time; data poisoning is typically an adversarial attack where threat actors intentionally introduce modifications or inject misleading data into the training dataset. 

Both are risks to an organisation but for different reasons. Data drift leads to inaccurate and unreliable outputs from your AI models. Data poisoning is a direct security risk and suggests a vulnerability in your infrastructure that needs to be investigated as a priority.  The outcome of both is the same: an increased risk of bias in your AI model which could result in poor decision-making, financial loss, or loss of compliance.

At the recent Gartner Security and Risk Management Summit in London, Mark Horvath, VP Analyst at Gartner stated that “AI requires new forms of trust, risk and security management (TRiSM) that conventional controls don’t provide… enabling better governance or rationalising AI model portfolio, which can eliminate up to 80% of faulty and illegitimate information."

You must acknowledge the risks in data-driven decision-making and ensure your data is accurate to prevent your AI model from making flawed decisions.  By focusing on building strong data controls, establishing data integrity and implementing data validation protocols, you can protect the business from revenue loss and compliance fines.

AI-Driven Marketing and Compliance Risks

In their article, The 15 Biggest Risks of Artificial Intelligence, Forbes highlights that "Instilling moral and ethical values in AI systems, especially in decision-making contexts with significant consequences, presents a considerable challenge"

AI models can perpetuate biases that are inherent in the datasets used to train them if they aren’t carefully designed.  For example, AI used in targeted online advertising might show ads for certain products or services to specific demographics which could easily lead to discrimination. Align with your legal and compliance teams to build-in checks for bias and fairness to protect the business from reputational harm.

Because AI models process such huge volumes of data there is always the risk of accidental breach of regulations like GDPR or CCPA if the model isn’t carefully monitored. However, implementing compliance tracking measures can help you safeguard against these vulnerabilities.

We recommend ensuring your datasets are representative of diverse populations in order to reduce the risk of bias in your models.  This involves collecting data from a variety of sources and demographics, reducing the risk of inadvertently reinforcing stereotypes and bias.

AI in marketing presents unique challenges for compliance.  Carefully designing systems with rigorous data controls to avoid bias and aligning with legal frameworks is key to avoiding reputational damage and regulatory breaches. 

Data Privacy and Security Risks in Personalisation

Accurate AI personalisation relies on collecting and processing large amounts of customer data - particularly personal data - in order to provide the best possible experience.  This creates a number of data privacy and security risks for organisations as the mishandling or unauthorised use of this data could result in privacy and/or data breaches. 

IBM’s 2023 Cost of a Data Breach Report found that the average cost of a data breach in 2023 was $4.45 million and that’s just the financial cost of remediating the breach itself.  

Couple that cost with the potential fine of up to 4% of global annual turnover (if governed by GDPR) and then factor in the reputational damage that comes with a data breach, the true cost is likely to be much higher. 

Laws like GDPR and CCPA impose strict data handling requirements on data processors and controllers, so it’s critical you build your AI systems to comply with these regulations.

Personalisation will undoubtedly enhance your customer’s experience but it does pose significant risks to your compliance efforts.  You must ensure you have the correct consent to collect and store the necessary data and that it’s done in alignment with data protection regulations.

Fraud Detection and False Positives

Juniper Research found that the total cost of e-commerce fraud to merchants will exceed $48 billion globally in 2023.

Businesses need to monitor the sensitivity of fraud detection systems to minimise false positives while still effectively identifying threats.

While AI systems are effective at detecting fraud, there is always the risk of false positives. If legitimate transactions are flagged as fraud this will have an impact on customer satisfaction and potentially lead to loss of revenue.

Striking the balance is key to maintaining customer satisfaction and highlights the wider issue of vulnerabilities in public-facing automated systems.

Vulnerabilities in Public-Facing and Automated Systems

The introduction of AI systems into businesses increases the attack surface and adds new vulnerabilities. Threat actors could easily exploit weaknesses in algorithms or their underlying infrastructure, leading to a potential security breach.

Because so many businesses are implementing AI as a means to stay competitive, they might not have fully vetted the security implications.  This can lead to vulnerabilities in the AI systems themselves, or in their integration with existing systems.

In Accenture’s recent State of Cybersecurity Resilience 2023 report, Palo Dal Cin, Global Lead of Accenture Security, said: “The accelerated adoption of digital technologies like Generative AI - combined with complex regulations, geopolitical tensions and economic uncertainties - is testing organisations’ approach to managing cyber risk.”

You should conduct regular assessments to test AI systems and your wider infrastructure for potential security weaknesses and implement layered security measures to defend against threats.

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Conclusion

Successful AI integration into e-commerce organisations requires careful balancing of the potential benefits against the risks.

The effective integration of AI hinges on alignment with business goals, strong data controls and a clear data governance policy.  It’s a dynamic process that must continually adapt as market trends change and new data is collected.

As AI becomes increasingly common, its role in improving the business must be balanced with due diligence in privacy and security.  The crux of this is data, ongoing monitoring and an ethical approach.  By maintaining this balance, you can harness the potential of AI while upholding regulatory compliance and customer trust.

Taken together, these steps will help you lay the groundwork for a successful and compliant AI implementation.

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Further Reading:

Article: AI in E-Commerce - Part Two: Mitigating Risks

Article: Why Artificial Intelligence Design Must Prioritise Data Privacy