The Cold Start Problem with AI Agents and How to Push Past It

For many enterprises, adopting generative AI feels like standing at the base of a mountain—daunting, uncertain, and with no clear path forward. At first, the world of AI looks quite promising but soon the reality of getting started hits them. 

They want to start but are unsure how to begin their artificial intelligence (AI) journey, often struggling with questions like:

• How do I source or prepare that data?
• What use cases should I prioritize first?
• What kind of data is needed to train an LLM?

Additionally, businesses want to remain hands-off during the process, preferring automated or managed solutions that don’t need an internal AI expertise. At the same time, they want to see results—fast. Since AI investment requires heavy cash outflow, executives are under immense pressure to show a faster ROI. 

This is the typical cold start problem in generative AI. Enterprises don’t have the required knowledge at the start of their journey, making it hard to gain momentum or confidently pursue meaningful AI projects.

At the core, the issue is: LLM training data. Not the data itself, but the understanding how to source, prepare, and optimize this data. High-quality datasets are the foundation for building and fine-tuning Large Language Models (LLM). Without a clear strategy, enterprises risk wasting time, resources, and effort on poorly executed AI projects.

So, in this article let’s try and help you push past the cold start problem by: 

• Clarifying LLM training data, including how to source and prepare it.
• Looking at actionable steps to get started with LLM, even if you’re starting from scratch.
• Knowing the tools, datasets, and best practices you’ll need for a headstart with your AI adoption journey with confidence.

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What is LLM training data?

First things first, let’s understand what an LLM is. A large language model is an advancement in generative AI that not only excels at understanding human language but recognizing patterns in it as well. LLMs are these large deep learning models that are pre-trained on huge data from a wide variety of resources. Hence, they are powerful tools with capabilities like - 

• Answering questions
• Searching, translating, and summarizing text
• Generating new content including text, images, music, and software code.

LLMs like GPT-3 have shown their strength in generating human-like text with high coherence and relevance. So, this gives a basic understanding of why LLMs are supremely important in generative AI. 

Now, the vast datasets—that you gave to the model —is the training data. LLM training data helps the model or algorithm find patterns and provide contextually relevant information. In simpler terms, this training data is the solid foundation on which the success of any large model depends.

If there’s any kind of data challenge, then even the most advanced algorithms will struggle to deliver meaningful results. Questionable data gives questionable results. And, this is exactly where the enterprises hit their first roadblock—they don’t know where to begin when it comes to LLM training data.

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Steps to Getting Started with LLM Training data

Beginning your enterprise AI journey shouldn’t be overwhelming. Yet, thanks to the ‘cold start problem’, the idea of implementing AI is a nightmare in itself. So, we’re here to demystify the whole thing to help you understand how you can push past the problem.

Here’s a step-by-step guide to start your AI journey:

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Objectives- Always tie back your business goals to the AI use cases.

Always zoom out and start with the big picture, before diving into data. Ask questions about major business challenges you are facing, what role will AI play in streamlining processes, metrics and KPIs that you’ll track, etc. 

Let’s take Nestlé as an example. It’s an enterprise but non-tech native company. It’s probably taking its first step towards AI. For a company that size, with a huge global footprint, diverse products, and millions of customers, the potential for AI-driven transformation is huge—but where do you even begin?

Nestlé could think about: 

• Automating responses for customer queries across multiple languages using an AI-powered chatbot to increase customer engagement. 
• Optimizing supply chain logistics by predicting demand fluctuations and reducing waste.
• Using AI to suggest recipes or nutritional plans based on consumer preferences.

Example Use Case: Nestlé could use LLMs to automate customer service across different regions and languages. By training an AI assistant with real customer interactions, FAQs, and multilingual support, Nestlé can ensure seamless engagement at scale.

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Audit Available Data- Don’t underestimate your data 

Most enterprises already have a goldmine of valuable data—they just don’t know how to utilize it. They assume they need new data to train an LLM. In reality, they don’t understand the power of their own data!  

Coming back to Nestlé, the following could be the training data that exists. 

✅ Customer support transcripts (for training AI chatbots)

✅ Product descriptions & marketing copy (for content generation)

✅ Internal reports & documents (for knowledge management automation)

✅ Social media & consumer feedback (for sentiment analysis)

By doing a data audit, Nestlé can see which of these sources are structured, unstructured, or need processing before being used for training. 

Example Use Case: Nestlé might realize that it has decades of R&D reports stored in PDFs. Feeding this data into an LLM could enable internal teams to instantly access historical insights without sifting through endless documents.

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Use Open-Source Datasets 

Build on what already exists. Not all training data has to come from scratch. Many high-quality open-source datasets exist to help enterprises get started. These datasets can either supplement existing data or be used to fine-tune models.

Some open-source datasets relevant to Nestlé’s AI journey- 

• Common Crawl – Massive dataset of web pages (useful for training AI assistants).
• OpenFoodFacts – Dataset with nutritional information and product details (useful for personalizing recommendations). This can be useful for market analysis, consumer behavior studies, and nutritional research.
• Customer Review Datasets – Public datasets containing e-commerce and product reviews (useful for sentiment analysis).

By combining these datasets with existing data, Nestlé can build AI models customized to its unique needs.

Example Use Case: If Nestlé wants to train a chatbot for nutritional advice, it could blend OpenFoodFacts data with its own product ingredient lists to ensure accurate, AI-generated recommendations.

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Partner with Experts 

One of the biggest problems in adopting AI in the enterprise is the lack of in-house expertise. Enterprises like Nestlé, don't want to deal with the technical complexity of training an LLM from scratch. 

Because implementing AI from scratch is not simple. The long drawn process involves identifying use-cases, integrating the tools into existing systems, and training the employees. And, without the right guidance, it can lead to delays, rising costs, and even the failure of the project. 

This is where teaming up with trusted AI providers can make all the difference. By leveraging no-code or low code AI platforms, Nestlé can focus on its business goals while leaving the technical heavy lifting to experts.

Companies like Zams specializes in helping enterprises through the process of AI 

adoption, offering a complete end-to-end assistance. 

• Identifying the key opportunities for AI initiatives
• Building, and deploying predictive and agentic models
• Securing outcomes 

Example Use Case: Instead of hiring an in-house data science team, Nestlé could use Zams’s ‘Data Scientist as a Service’ to fine-tune an LLM for its customer support automation.

Related resource: No-code and low-code: What’s the difference?

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Iterate and Measure

ROI—how can you possibly explain the result of a project you’re taking up for the first time, to your colleagues? This is one of the biggest challenges enterprises face. That’s why tracking outcomes from day one becomes non-negotiable.

Some KPIs and success metrics could look like -  

• Reduced customer response time (if implementing an AI chatbot).
• Decrease in supply chain inefficiencies (if optimizing logistics with AI).
• Increase in personalized product recommendations (if deploying AI-driven customer engagement tools).

By setting clarity in success metrics and continuously iterating based on feedback, enterprises can ensure their LLM initiatives are aligned with business goals.

Example Use Case: If Nestlé launches an AI chatbot for customer service, it could track:

• The number of queries handled autonomously vs. those needing human intervention.
• The customer satisfaction score (CSAT) after interacting with the AI.

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Challenges in Preparing and Managing LLM Training Data

Unlike AI-native companies, enterprises usually don’t have the in-house expertise to handle data complexities. Instead, they want a hands-off, outcome-driven approach that takes these challenges away. But without careful preparation, poor-quality data can lead to biased, inaccurate, or non-scalable AI models.

Here are the key challenges enterprises face when managing LLM training data:

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Garbage In, Garbage Out

LLMs are only as good as the data they’re trained on. If the training dataset isn’t clean— is biased, incomplete, or low-quality—then the model will have those exact flaws. These will lead to incorrect predictions, unfair decision-making, or even reputational risks. That will be too bad. 

How come my own data isn’t clean- you might ask. Following could be some of the reasons -

• Historical biases – Many enterprises rely on past customer data, which may reflect outdated or skewed patterns.
• Data silos – Different departments collect and store data in disconnected systems, leading to incomplete training datasets.
• No diversity – Training an LLM only on internal data may limit its understanding of broader market contexts.

Let’s look at the challenge from Nestlé’s angle. If it trains an AI-powered recipe generator on historical consumer data, but that data primarily comes from Western markets, it might struggle to generate recommendations that align with Asian or Middle Eastern cuisines.

So, what’s the solution? 

Enterprises need curated and diverse datasets that represent their entire customer base. Techniques like data augmentation, bias correction, and active learning can improve LLM training data quality.

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Scalability- Handling Growth Without Breaking the System

As enterprises go beyond their initial AI projects, data demands grow exponentially. What worked for a pilot project may not scale when the company moves to full deployment.

Here are some of the common scalability problems - 

• Higher data volume – As more interactions are fed into AI systems, data processing speeds and storage become bottlenecks.
• Massive computational power – Training larger models requires huge processing capabilities (which not all enterprises are equipped for).
• Maintaining data relevance – AI models trained on static datasets can become obsolete. Continuous learning is essential.

Example: As we discussed earlier, if Nestlé launches an AI-powered customer service chatbot, it will need to process millions of customer interactions daily. Without the right infrastructure, response times could slow down, frustrating users. 

So, it’s safe to use cloud-based AI platforms. They scale with enterprise needs, prevent data infrastructure bottlenecks and keep AI models up to date.

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Compliance with Security, Governance and Data Privacy Regulations

Enterprise AI adoption comes with a heavy regulatory burden, no doubt. Especially for enterprises handling customer data. If there’s mismanage of that data, it can lead to compliance violations, lawsuits, and more importantly, loss of customer trust.

Key regulatory challenges:

• GDPR (Europe) – Requires transparency in AI decision-making and user consent for data use.
• CCPA (California) – Gives consumers the right to request deletion of their data from AI training sets.
• Industry-specific laws – In sectors like healthcare or finance, data handling regulations are even stricter.

Example: If Nestlé uses customer data to train an AI-powered nutrition advisor, it must ensure that sensitive health data isn’t misused, or it could face penalties.

How to solve this challenge? For this, enterprises should work with trusted AI providers that offer built-in compliance tools, such as audit logs, automatic data anonymization, etc.

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10 Open-Source Datasets for Getting started with LLM

If you have reached this section, it means only one thing—you want to clean your data and get started with LLM. Using open source datasets is one option. These can help overcome the cold start problem by providing a plethora of existing data, eliminating the need for extensive custom data collection.

This table shows the 10 open-source datasets for training LLMs.  

You can drastically reduce the time and resources required to develop effective LLMs. These LLM datasets provide a solid foundation that helps you overcome the cold start problem. 

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Overcoming the Adoption of AI in the Enterprise: Real Success Stories 

Many enterprises now realize AI’s potential in successfully transforming their business operations. Here are some examples of such enterprises. 

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Amazon 

Overview: As the largest online retailer, Amazon has harnessed AI to enhance logistics and supply chain operations. 

Challenges: Amazon has faced various problems in handling the increasing order volumes, efficient warehousing of those orders, and reducing human errors.

AI Solutions: 

• Launched more than 200,000 robotic units in fulfillment centers to assist in the movement and sorting of packages. 
• Utilized machine learning algorithms to optimize warehouse layouts and robot paths. 
• Implemented demand forecasting through computer vision to reduce order picking errors. 

Results: The initiatives accelerated the order processing time and enhanced customer satisfaction through quicker delivery and substantial cost savings in operational efficiency. 

Walmart 

Overview: Walmart has engaged AI to optimize supply chain operations at a very large scale and to make the consumer service impeccable. 

Challenges: The necessity to optimize supply chain efficiency, introduce better inventory management, and make the logistics responsive to user activities. 

AI Solutions: 

• Utilization of an advanced AI platform for accurate and extensive demand forecasting. 
• Machine learning deployment for the optimization of delivery schedule and routing. 
• Use of autonomous vehicles and drones for inventory management. 

Results: Walmart improved supply chain efficiency, reduced waste through better management of inventory, and enhanced customer satisfaction through improved availability of products.

JPMorgan Chase 

Overview: This financial giant has deployed an artificial intelligence-powered virtual assistant named COiN to tailor back office operations. 

Challenges: The bank needed the ability to cope with seemingly endless volumes of financial documents in an efficient process without such many errors. 

AI Solutions: 

• COiN automates tasks including data input and compliance checks by analyzing invoices and receipts from machine-learning algorithms. 

Results: The introduction of COiN has hugely expanded the speed and accuracy with which financial documents have been processed, freeing employees for more complex work.

Clearly enterprises have adopted AI in their journey successfully. So, what’s stopping your company from getting started with LLM? It’s always the first step that’s tough. But once you’re able to push past the cold start, you’d create amazing results for your enterprise as well. 

Final Thoughts: Taking the First Step Past the Cold Start

The biggest barrier to adopting generative AI isn’t technology—it’s knowing where to begin. By identifying clear objectives, leveraging existing data, using open-source resources, partnering with experts, and measuring success, even a non-tech-native enterprise like Nestlé can move past the cold start problem and make AI an integral part of its business.

Ready to take the first step? Start by auditing your LLM training data today. The future of AI in your enterprise begins with the right data—and the right strategy.

If you feel stuck, reach out to us at Zams. Let us be your partner on your journey into AI, book a demo today!