The Future of Business Transformation: AI-Driven Customization
This is the second in a series exploring how the world of business is changing, particularly how running a business is undergoing transformational shifts. In this opening part, I aim to set the stage for what’s to come, sharing insights and lessons learned along the way.
User Case Study Building
I decided to try something different. Instead of using our current work as a case study, I asked Notion AI to generate one for me. It analyzed all the content we had created so far, along with new material for our upcoming website and Whitepaper stored in Notion. It then produced a case study for an industry we hadn’t even considered. The result was impressive—it covered about 90% of what we’d been thinking and even added features we hadn’t thought of yet.
Next, I took that content and ran it through ChatGPT-4o, asking it to refine the case study while incorporating a few extra features we’d discussed but hadn’t been fully developed in Notion yet. The AI quickly grasped the additional prompts and seamlessly integrated them into the case study, enhancing it with ideas we had only briefly mentioned.
We then started experimenting further, asking ChatGPT-4 to create case studies for two other industries that companies had asked us to consult on. We did it this way so it didn’t pull from our existing Notion content. The results were fascinating—our ideas blended with external information to produce solutions that pushed the boundaries of what we had originally envisioned.
Finally, we brought this back to Notion AI, asking it to repeat the process. It provided a different perspective, as it was now referencing the content we had already written. We then merged the insights from both tools to create a much more refined and complete result which we will now use to help us work with these clients to create a compleing case for their $Billion industries.
This process not only validated our work but also expanded our thinking, showing us new ways to evolve and refine our solutions.
The User Case Study Created by AI
To illustrate how a business would implement this AI-driven customization built on DLTs, let's consider a hypothetical scenario. Imagine a medium-sized logistics company, Global Express, deciding to revolutionize its operations. They begin by engaging with an AI platform that specializes in creating customized business solutions.
The AI system starts by analyzing Global Express's current processes, pain points, and future goals through API access to current systems and prompts for information. It then generates a tailored platform that integrates their existing systems while introducing new functionalities. This platform includes a smart contract-based payment system for drivers, an AI-powered route optimization tool, and a blockchain-based tracking system for parcels.
As the company uses this new system, the AI continuously learns and adapts. It not only optimizes operations but also enhances security measures. For instance, it might notice inefficiencies in certain delivery routes and suggest improvements, while simultaneously detecting unusual patterns that could indicate fraudulent activities. The AI could identify suspicious behaviours in customer orders or driver activities, flagging them for further investigation.
Moreover, the system could analyze transaction patterns to detect potential money laundering attempts or other financial irregularities. The DLT aspect ensures that all transactions - from customer payments to driver compensation - are transparent, secure, and automated, making it extremely difficult for bad actors to manipulate the system. This transparency also allows for real-time auditing, further deterring deceptive practices.
By continuously learning from these patterns, AI can propose new service offerings and security measures, staying one step ahead of potential threats while improving overall business efficiency.
Over time, this AI-driven system evolves into a comprehensive, customized solution that addresses Global Express's unique challenges, significantly improving their operational efficiency and competitive edge in the market. This transformation showcases how businesses can leverage AI and DLT to create bespoke solutions that go far beyond off-the-shelf products.
What does this mean
For us, this experience highlights the need to focus intensively on our transfer engine, which is the foundational component of our system. Ensuring that it operates flawlessly, regardless of the demands placed on it, is critical. We’re also prioritizing the creation of clear API contracts to enable seamless AI integration. The next step is to explore how we can empower AI to dynamically generate business rules and convert them to run on Smart Hubs, moving beyond traditional if-then-else workflow models.
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