Project Overview and personal contribution
Throughout the Information Strategy course, our team developed Reddit Early Signals, an AI-enabled solution designed to help businesses proactively identify emerging consumer trends through Reddit conversations. Instead of relying on predefined keywords, our solution uses AI to identify relevant signals before companies even know what to search for. Interestingly, Reddit was not originally my idea. During our initial brainstorming, each team member pitched their own concept. After discussing the different proposals, we collectively agreed that the Reddit concept offered the strongest opportunity. Although it was not my initial preference, I became increasingly enthusiastic as we developed the idea together.My main contribution during the AI Strategy Lab project focused on the strategic and commercial aspects of the project, particularly the business model and pricing strategy. I also contributed to strategic decisions and helped coordinate our progress through regular online and in-person meetings.Looking back, I learned how challenging it can be to transform a promising technological idea into a realistic business strategy.
Strategic Decision
One of our most important decisions was pivoting from Track B, developing an independent venture, to Track A, transforming an existing organization.
Initially, we approached Reddit Early Signals as a standalone business opportunity. However, during our strategic discussions, we questioned whether an independent company could realistically access the necessary Reddit data. More importantly, we questioned the venture’s long-term sustainability. If Reddit controlled the underlying data and could develop a similar solution itself, how defensible would our business be?
Since our concept depended heavily on Reddit’s historical conversations and community context, developing the solution within Reddit became the more logical choice.
Interestingly, we did not fundamentally change our product idea, but rather the organization through which we intended to create value. This decision helped me understand the concept of AI as an amplifier of complementary assets. While AI models are increasingly accessible, proprietary data and established infrastructure remain difficult to replicate.
Before this project, I primarily focused on whether a business idea could create value. I now realise that understanding who can sustainably capture that value is equally important. In future projects, I would evaluate competitive threats, complementary assets, and organizational positioning much earlier.
Business Model Development
Developing the business model was one of the most educational aspects of my contribution. Using the course framework of value creation, value delivery, and value capture, we explored how Reddit could transform its existing intelligence capabilities into a commercially attractive service.
We developed a freemium model with four subscription tiers: Free, Pro, Business, and Enterprise. This connected directly to the lectures on information goods, particularly versioning and second-degree price discrimination. Different functionality levels allowed customers to select subscriptions corresponding to their willingness to pay, while the free tier aimed to lower adoption barriers. However, I also realized that it introduced the risk of customers remaining on the free tier rather than upgrading.
Our financial analysis estimated operating costs, contribution margins, and an illustrative break-even point of approximately 82 paying customers. Although promising, these calculations depended heavily on assumptions about customer adoption and willingness to pay.
I realized that creating a financially attractive business model is fundamentally different from proving commercial feasibility. Another surprising insight concerned AI economics. Our calculations suggested that human validation could be considerably more expensive than the AI processing itself. This connected to the course discussions on verification and trust. It was enlightening to see what we’d learnt during the course. Customers would not simply pay for AI-generated information, but for reliable insights supporting their decisions.
I learned that AI does not necessarily eliminate operation costs; it can shift human involvement towards verification and judgment. In future projects I would therefore validate customer demand and pricing assumptions earlier.
Developing the prototype
Developing the prototype was particularly challenging for me, as my strengths lie more in business strategy rather than programming. Nevertheless, I genuinely enjoyed the process and learned a great deal, particularly thanks to the support of my more technically experienced teammates. This experience was valuable for my role in developing the business model and assessing financial feasibility. It helped me better understand the technical requirements, limitations, and operational costs behind our solution.
I realized that business strategy cannot be developed independently of technical feasibility. Although I do not need to become an expert programmer, improving my technical understanding will help me make more realistic strategic decisions and collaborate more effectively with technical specialists.
Collaboration, Feedback and iteration
Collaboration was another important learning experience. Throughout the project, our regular online and offline meetings allowed us to brainstorm, challenge assumptions and align our individual contributions.
However, working with different personalities and perspective was not always straightforward. We occasionally disagreed about strategic priorities, particularly when reconsidering our positioning from track B to track A.
Looking back, I recognize that I sometimes became too focused on defending my own reasoning instead of immediately considering alternative perspectives. Nevertheless, our continued discussions helped us reached agreements and improve our strategy. I gradually realized that different opinions could strengthen decision-making by revealing assumptions that might otherwise remain unquestioned.
During the developing of the prototype, it became clear how important it is to evaluate the results rather than just the technological performance. Personally, I found myself focusing too much on performance alone. This tied in with the lectures on AI governance and experimentation. The AI Canvas further highlighted why human judgment remained essential for validating signals before customers received them.
I learned that effective collaboration requires more than dividing tasks. It involves integrating different expertise, remaining open to feedback and continuously questioning assumptions.
How the project changed my understanding of information strategy
Before this project, I primarily associated AI with automation, efficiency improvements, and chatbots. However, the project significantly broadened my perspective of its strategic applications.
The pivot demonstrated the importance of complementary assets and competitive positioning, while developing the business model revealed the challenges of capturing value under uncertainty.
Building the prototype taught me to appreciate technical complexity and the importance of learning from teammates with different expertise. Our collaboration also showed me that constructive disagreement can improve strategic decisions.
Most importantly, I learned that Information Strategy is not simply about implementing innovative technologies, but about aligning technological capabilities, organizational resources, and economic incentives to create sustainable value.
In future projects, I want to challenge assumptions earlier, validate commercial opportunities more systematically, strengthen my technical skills, and approach collaboration with greater openness.
Ultimately, the AI Strategy Lab project taught me that successful AI strategy requires not only technological innovation, but also critical thinking, adaptability, and effective teamwork.
