At Elgentos, we regularly organize webinars where we discuss current topics together with experts. This time, we spoke with Jantine Doornbos from AIBlueprint. The topic of this session: AI readiness.
Many organizations feel they need to do something with AI. Teams are experimenting with tools like ChatGPT, internal pilots are being launched, and ideas around automation are emerging. However, the strategic foundation is often missing. What does it really mean to be AI-ready? And what steps should you take to implement AI successfully and responsibly?
Why AI-readiness Matters
AI readiness refers to the extent to which your organization is prepared to implement AI. This doesn’t just mean having access to the right tools; it mainly requires having your processes, data, and vision in order. In practice, we see that many companies have the ambition to use AI but don’t always know where to start. AI is much more than a smart chatbot — it affects your data foundation, your core processes, and even your governance.
During the webinar, AIBlueprint shared five steps that help organizations achieve AI readiness.
- Everything begins with the problem. AI is not a goal in itself, but a means to an end. The key question is: what concrete problem are you trying to solve? This can range from small process improvements to larger strategic challenges. Without a clearly defined problem, AI remains an experiment without a clear direction.
- Data forms the foundation of every AI application. However, in many organizations data is fragmented, poorly structured, or insufficiently secured. For a successful implementation, data must be transparent, normalized, and securely accessible. Only then can AI truly add value.
- An important step is taking ethical responsibility. What data are you using? Are you allowed to use this data for AI? Is the dataset clean and free of bias? Organizations need to make conscious choices about this. Being AI-ready also means thinking about privacy, bias, and transparency.
- The choice of technology plays a crucial role. Many companies immediately turn to well-known AI platforms without considering the strategic and legal implications. For European organizations, it is important to deliberately choose tools that comply with regulations and remain sustainable in the long term. Technology should support your strategy — not the other way around.
- A clear vision for AI policy is essential. What do you want to achieve with AI in the next six to twelve months? Where can AI make decisions autonomously, and where should it not? By defining these boundaries in advance, you can prevent fragmented initiatives and create alignment across teams.
What Often Goes Wrong?
A common pitfall is that organizations start with the tool instead of the strategy. Companies begin experimenting in the hope that it will automatically lead to gains. In reality, AI requires a strong data foundation and a clear connection to the core business process.
Another pitfall is using AI for isolated applications with a high gimmick factor but little structural impact. For example, generating an image for a blog automatically, while the real optimization potential lies in areas such as product data, pricing, or process automation. When AI is not connected to core processes, adoption remains limited and the overall impact stays small.
What AI Does — and What It Doesn’t
AI is particularly powerful when it comes to optimizing existing processes. It accelerates repetitive cognitive tasks, supports analysis, and automates recurring activities. What AI is less effective at is creating entirely new business models without human guidance. When you use AI for completely new processes, you are essentially expanding your product offering rather than optimizing it.
The greatest gains are often found in making what you already do smarter and more efficient.
The Elgentos Approach to E-commerce AI Readiness
At Elgentos, we focus specifically on e-commerce organizations. We also follow five key steps to successfully implement AI.
We start with strategic positioning. Where within your organization can AI support or make decisions? For business-critical or legal processes, human oversight remains essential. AI should always work alongside humans, not replace them.
Next, we focus on data quality and accessibility. Without specific company data, AI is essentially a generic model that guesses based on probability. By feeding AI with your own structured data, the output becomes more relevant and consistent.
We then look at process selection. Where in the organization does repetitive cognitive work occur within clearly defined frameworks? If reviewing the output takes less time than performing the task itself, AI is often a suitable solution.
A human in the loop is indispensable. Especially for important business processes, verification remains necessary. AI can provide support, but ultimate responsibility always remains with people.
Finally, continuous evaluation is crucial. AI models must be monitored and adjusted over time. Small deviations in output can have significant consequences, for example in product information or pricing logic. That is why structural oversight is an essential part of a mature AI strategy.
Is Your Organization AI-Ready?
AI readiness requires conscious choices. About data. About processes. About technology. And about governance. It starts with understanding your current situation and ends with a strategic, sustainable implementation where people and technology work together.
Curious about where your organization stands when it comes to AI readiness? We’d be happy to start the conversation and explore how we can help make your e-commerce organization AI-ready.