The AI Cold Start Problem: Strategies for Business Leaders

Years ago, I was working for a company that launched a marketplace for corporate training courses. Each new customer required a tailored conversation with a company representative who would compile a personalized curriculum based on their needs. They were eager to implement a smart AI recommendation engine to reduce their workload so they could scale more efficiently. However, there was one glaring problem: they didn’t have a lot of customer data. Few records of purchase histories, company profiles, and marketplace browsing behavior. The AI engine was limited in its recommendations because there simply wasn’t enough data.

This experience culminated in one of the most important lessons I’ve learned in AI: the quantity and quality of your data is the ultimate limiting factor. No matter how good your AI models are, without plentiful and good data, they can’t do their job. Data is more than just fuel for AI; it’s the very foundation everything else is built on.   

This, in essence, is the “cold start problem.”

Large Language Models (LLM) like ChatGPT have gained widespread success largely because they were trained on massive amounts of public data (e.g., the internet) and refined through human feedback. While some experts suggest that foundation models now make it possible to launch AI initiatives without any proprietary data—because they can apply broad, general knowledge to new problems—this doesn't eliminate the need for high-quality, context-specific data in most business applications. Whether you're developing recommendation systems, predictive analytics applications, or custom AI solutions, relevant historical data remains essential to deliver meaningful, targeted business value. Understanding this distinction is critical when planning your digital transformation strategy.

In this article, we will see why the cold start problem is a critical hurdle in digital transformation journeys and directly impacts the Return on Investment (ROI) on AI initiatives. We will explore practical, actionable solutions for overcoming this challenge with the right data sourcing, product design, and go-to-market or internal go-live strategies. 

Defining the Cold Start Problem

The cold start problem arises when an organization launches a new product or digital initiative (say, deploying AI or predictive analytics) but lacks the relevant historical data for these systems to perform effectively. This is not simply a trivial matter of technical configuration; it’s a challenge that impacts the pace, effectiveness, and strategic value of major business investments. 

Think of entering a new market with a brand nobody knows. Without customer relationships or sales records, growth is slow and risk is high. The cold start problem is similar: AI models need exposure to real, representative data before they can deliver targeted recommendations, accurate predictions, or meaningful insights. Until that foundation is built, time-to-value drags, adoption lags, and promised returns are delayed.

Let’s revisit the training course marketplace challenge. There are typically two main approaches:

1. Recommending products similar to those a company has previously purchased.
2. Recommending products based on the company’s profile and drawing on purchases made by other companies with similar profiles.

In the first approach, as long as a company has a history of past purchases and there’s a well-defined product catalog, the AI model can provide reasonable recommendations. In the second approach, as long as a company profile exists and there are other companies with similar profiles who have made purchases, the system can generate relevant recommendations.

But what happens if a new company has no purchase history, no profile, or there aren’t similar companies who have made purchases?

This scenario is a classic example of the cold start problem, a challenge that’s widespread across many AI applications, including:

• Healthcare diagnostic tools, which require a wide variety of patient data spanning different conditions.
• Financial fraud detection systems, which depend on both legitimate and fraudulent transaction examples to identify suspicious activity.
• Customer service chatbots, which need access to historical conversation logs to provide relevant and accurate responses.

Why Senior Leaders Should Care

Senior leaders need to pay close attention to the cold start problem because it creates a real slowdown for any AI initiative. When your data is thin, AI projects take longer to launch, and users may not see the promised benefits soon enough. For customer-facing initiatives, this gap allows competitors with better data to offer smarter, faster, or more engaging products. For internal transformation efforts, this friction creates anti-momentum and a “loss of faith” leading to change management and investment challenges. 

Cold start challenges also have a direct impact on critical business risks and outcomes. For example, if your AI system lacks sufficient data, it may be unable to meet regulatory or compliance requirements, potentially leading to costly penalties, audit failures, or reputational harm. Likewise, limited data can increase exposure to operational risks, such as failing to detect fraud or security threats. From a customer perspective, poor personalization can result in lower engagement and lost revenue opportunities. Mitigating the cold start problem early is therefore essential for improving user experience and for effectively combating risks. 

Leaders who recognize and address the cold start problem put their organizations in a much stronger position. Sourcing useful data, building flexible early product or system designs, and starting with carefully chosen groups of users help move AI plans forward and support long-term business goals.

Framework for Overcoming Cold Start

Organizations can overcome the cold start problem by focusing on three key strategies: 

1. Data sourcing: identify and gather the necessary data to enable your AI models, whether through first-party data, partnerships, existing public datasets, generating synthetic data, or transfer learning from related domains (i.e., using AI models that have already been trained on data from similar tasks or industries and adapting them for your specific needs).
2. Product design and user experience: design early product experiences that provide immediate value, even before AI models are fully trained. Set user expectations, incentivize data contribution, and reveal AI-driven features thoughtfully as data accumulates over time.
3. Go-to-market and internal go-live strategies: carefully plan your rollout and pricing, initially targeting select user segments and piloting with limited audiences. Manage expectations around ROI and demonstrate value incrementally as your AI capabilities mature.

Data Sourcing

Let’s face it: building great AI starts with good data. But what happens when you don’t have enough? Here are some practical ways to get the data you need: 

1. Collect and curate first-party data: For many customer-facing products or internal systems, the most valuable data is the information you collect directly through your systems’ user interfaces, often called “first-party data.” For example, when you automate sales processes, you need to carefully design the fields and workflows in your CRM to ensure your sales reps enter accurate and relevant data. Remember to invest in thoughtful user interface design and data quality initiatives at this stage for better pay off in AI outcomes down the road. You can also hire a team or use LLMs and automation tools for labeling and organizing this initial dataset.
2. Make your own data: don’t overlook synthetic data. Work with experts to generate realistic, representative sample data that mirrors real-world scenarios. This can be a game-changer for sensitive industries like healthcare or finance where privacy is a major concern.
3. Use pre-trained models: ask your data scientists if they can start with pre-trained models from similar fields. This transfer learning approach saves time and helps you get value from your AI much faster, even if you have limited data to start. 
4. Purchase or enter into data partnerships: there’s a wealth of free, public datasets out there, and plenty of vendors are ready to sell you what you need. For free, public datasets, be careful not to assume they are high quality (many aren’t); do your due diligence. Even better, consider teaming up with another organization for a win-win: you get access to their data, and in return, share the insights or new data your project generates.

If you would like to explore this topic further, check out the blog, How Much Data Do We Need by my cofounder at Synaptiq, Dr. Tim Oates. 

Pros, Cons, and Ethical Considerations of Data Sourcing

As you evaluate different data sourcing strategies, it’s essential to understand both the opportunities they unlock and the risks and responsibilities they bring.

Pros:

• Accelerates AI development: leveraging external data, pre-trained models, or synthetic datasets jump-starts AI initiatives and reduces time to market.
• Cost efficiency: data partnerships and use of publicly available resources help avoid the high costs of building proprietary datasets from scratch.
• Improved model performance: combining diverse data sources can increase model robustness and adaptability, which is crucial for business-critical applications.
• Regulatory adaptability: approaches like synthetic data help organizations navigate privacy constraints, especially in regulated industries. 

Cons:

• Data quality concerns: externally sourced or synthetic data may not always fit business needs or accurately represent real-world scenarios, which may lead to biases or poor model performance.
• Integration complexity: merging disparate data sources and formats can introduce technical and organizational challenges.
• Hidden costs: data acquisition, partnership negotiations, and integration efforts can require significant time and resources.
• Reliance on external entities: partnerships or third-party datasets may bring risks around availability and long-term continuity.

Ethical Considerations:

• Privacy and consent: always ensure data is collected, processed, and shared in compliance with relevant copyright and privacy laws such as GDPR, HIPAA, etc. Explicit user consent is a must whenever personal data is used (or shared).
• Bias and fairness: carefully vet data sources to identify and reduce bias, ensuring models do not reinforce discriminatory patterns.
• Transparency and accountability: maintain clear records of data governance and usage, accurately reflecting how data-related decisions impact users and stakeholders.
• Data security: protect sensitive data with robust security measures throughout its lifecycle (acquisition, storage, processing, and deletion).

Product Design & User Experience

When you’re facing a cold start problem, don’t underestimate the impact of smart product design on your users’ experience. How you onboard your earliest users can make all the difference both to gathering the data you need and for building long-term engagement and trust  

Here are a few strategies that work:

• Set realistic expectations: be upfront about what users can expect in the early days while your AI models are still being trained. Transparency helps manage early adopter patience and builds credibility.
• Prioritize immediate value: hold off on showing AI-powered results until you’ve gathered enough quality data for them to be meaningful. In the meantime, provide value through simple, rule-based features or manual workflows (the non-AI ways) to keep users engaged and give your systems time to learn. This ensures immediate utility without relying on data-hungry algorithms. As data accumulates and your AI improves, gradually introduce smarter features like predictive analytics, personalized suggestions, or dynamic automation. 
• Motivate contributions: offer incentives for users who share data or participate in ways that help train your models. Recognition, rewards, or additional features can encourage the data contributions you need.
• Roll out AI features gradually: as your models improve, gradually phase in AI-driven experiences. This approach avoids overwhelming users and ensures each new feature actually delivers additional value. 

The key here is to invest early in thoughtful product design to get past the cold start and iteratively improve the experience, or you may never get out of the “chicken and egg” problem.

Go-To-Market or Internal Go-Live

When bringing your AI solution to market, be intentional about how and when you launch. Think carefully about your rollout strategy, pricing strategy, and business case expectations. 

Start with a pilot: launch first to a small segment of users, so you can gather targeted feedback, surface initial issues, and build credibility before scaling up. Clearly acknowledge to these users where the data powering your AI is being sourced from, and always ensure you have a robust, accessible data privacy policy so users know exactly how their data is (or is not) being used. 

To further support trust and improve your product, build in an easy-to-use feedback loop so users can share what’s working and what isn’t with your AI solution. Early feedback is crucial for refinement. 

When considering pricing, be strategic: it often makes sense not to charge during a closed beta or pilot phase, allowing your product to collect crucial user feedback and usage data. Once proven, determine if the AI functionality is an add-on to an existing product (and should be priced as such), or if it’s a new category requiring its own pricing structure. Offer a free trial to encourage adoption and remove barriers for users to try before they buy. Set pricing expectations accordingly, keeping prices low at first while your models are still proving their value and you continue learning from real-world data. Avoid making promises about ROI until you have substantiative,  real-world results. Overcommitting too early can undermine trust and cause disappointment down the road.

And if you’re working in regulated or sensitive fields, always test your models internally before rolling them out to customers. This will help catch compliance or privacy issues while giving your teams confidence in the system’s performance. 

Real-life Examples 

 Over the past decade, my company, Synaptiq, has solved many cold start problems for our clients. Two cases stand out for the lessons they offer:

Optimizing Government Cloud Usage with Simulated Data

In the early days of our company, we worked with the federal government to help automate their cost management of cloud resources. At the time, there were no automated tools available to optimize cloud resource usage, and direct access to the necessary consumption data (e.g., system logs) was strictly off-limits due to security protocols.

Facing this cold start, we generated simulated log data in-house and fine-tuned our reinforcement learning model until it met performance standards. We then provided the trained model to the government client for secure deployment, where it recommended when they should turn on or off specific cloud resources to save money while continuing to meet agreed-upon service level agreements.

Read more about it in our coauthored research paper, Automated Cloud Provisioning on AWS using Deep Reinforcement Learning.

Applying First-Party Data to Analyze Client Chat Data 

Earlier this year, we worked with a legal firm that had a treasure trove of historical client chat data. They wanted to ask product management questions of that data using generative AI. Early into the project it became clear that the historical data lacked “product management” semantics so we ran headfirst into the cold start problem. 

In an attempt to solve the problem, the client supplied a product management taxonomy, then proceeded to tag the chat data. However, it wasn’t as easy as they expected. Tagging product management concepts like “bug” or “feature enhancement” just didn’t make sense in many of the historical chat conversations with their clients. In the end, the client tagged a subset of the chats and had to rethink the original use case itself. 

This example also highlights why it’s important to conduct an Exploratory Data Analysis (EDA) of your data to make sure it’s even relevant to the problem you’re trying to solve before jumping headfirst into an AI solution. The data will tell you what is possible, you just have to analyze it. 

Implementation Playbook: Actionable Takeaways

• Diversity data sourcing: don’t rely solely on in-house data. Leverage public datasets, forge data partnerships, and consider purchasing data as needed. When data is inaccessible, generate synthetic samples or use pre-trained models from adjacent domains.
• Strategic product design: Invest early in user experience that provides value from day one, even before AI features go live. Use rule-based or manual workflows to keep users engaged and encourage data sharing until your models are production-ready.
• Prudent GTM and internal go-live planning: start with pilots targeting small user cohorts. Be transparent about expected outcomes and avoid overpromising ROI until models are validated in real-world use. Keep pricing flexible initially to reflect early-stage value.
• Staged feature rollout: launch with non-AI or minimally-AI features to deliver immediate results. Gradually introduce AI-driven enhancements to increasingly larger user segments, using early feedback to improve both UX and model performance.
• Special care in sensitive industries: For regulated domains, always pilot and rigorously test models internally before wide launch. Prioritize data privacy, compliance, and transparency, documenting every step for accountability.
• Emphasize continuous improvement: as the user base grows and more data becomes available, regularly enhance your AI’s capabilities, iterating on product design, user engagement, and technical approaches to maximize long-term value.

Conclusion

The cold start problem is a universal challenge when launching AI initiatives, especially for solutions that depend on large, high-quality datasets to function effectively. When initial data is scarce, performance and accuracy can suffer, putting adoption and ROI at risk.

Fortunately, organizations can overcome this barrier through a thoughtful, multi-pronged strategy. Leveraging diverse data sources — be they public, purchased, collaborative, or synthetic — establishes the groundwork for smarter AI. Equally important is designing products that gather valuable data organically and in a way that respects user privacy, empowering users while fueling continuous learning.

From a strategic perspective, start by focusing on early adopters and applications that add value with limited data. As engagement grows, so does the dataset - allowing for ongoing refinement and improved results.

With creativity and pragmatism, businesses can successfully implement AI solutions that evolve and mature alongside their growing data resources, transforming cold start challenges into opportunities for differentiation, ultimately delivering increasingly powerful results over time.

Have a cold start problem you’d like to talk about? Direct message me, and we will schedule a call to talk through it.