Is Your Data Ready for an AI Product or Feature?

AI has gone from a tech buzzword to a dinner-table debate. I'm trying to convince my dad to download ChatGPT and move him from sceptical and slightly scared to actually understanding (and maybe even believing) in its power. Earlier this year, I attended an AWS event in London, and it was impossible to miss the recurring theme: AI was everywhere. Every other talk, panel, and fireside chat focused on artificial intelligence and its impact across industries. From ambitious startups experimenting with generative AI to FTSE 100 giants exploring large-scale automation, one theme consistently emerged at the conference: how do we effectively leverage AI to make our products more innovative, more useful, and genuinely better for customers?

When introducing AI into your product or building an AI-driven solution, success doesn't begin with algorithms — it starts with clarity. Define the problem first, then ensure your data is prepared to address it. Let's examine what this means in practice, the realities of AI and data readiness, and the strategic priorities your organisation should focus on.

Why Data Readiness Matters

AI systems are built on patterns extracted from large datasets. Poor-quality, incomplete, or biased data doesn't just slow development, it produces inaccurate or even harmful outcomes.

1. 80% of an AI project's time is often spent on data preparation and cleaning (Forbes).
2. Gartner predicts that through 2025, 85% of AI projects will deliver erroneous outcomes due to bias in data or algorithms.
3. According to IBM, data-driven companies are 23 times more likely to acquire customers and 19 times more likely to be profitable.

Simply put: without high-quality, well-structured data, your AI product risks becoming expensive, inaccurate, or even untrustworthy.

"Success doesn't begin with algorithms, it starts with clarity. Define the problem first, then ensure your data is prepared to address it."

James Murray, Senior Product Owner @ Toyota Connected Europe

Signs Your Data Is (or Isn't) Ready for AI

Not all data is created equal. Even the most complex algorithms can't compensate for poor-quality data.

Here are five key signals to assess your readiness:

1. Volume: Do you have enough data?

Machine learning works best when it’s fed with plenty of complete, representative data. If the records are patchy or limited, the predictions become shaky and the results can’t be trusted.

2. Variety: Is your data diverse enough?

AI works best when it can draw from a mix of data, including both structured and unstructured data, such as text, images, or sensor readings. Relying on a narrow dataset limits the model's understanding of the world and its effectiveness in performing tasks.

3. Velocity: Is your data updated regularly?

Outdated data means outdated models. To stay sharp and relevant, your systems need a steady stream of fresh, real-world information.

4. Veracity: Can you trust your data's accuracy?

Inconsistent, duplicate, or biased data can sink even the best AI project. You’ve got to trust your data just as much as you trust your model.

5. Value: Does your data align with the problem you're solving?

At Toyota Connected, we follow a privacy-by-design framework. Collecting unnecessary data not only wastes time and resources but also goes against this principle. The most valuable data is the kind that directly supports your business goals and drives the outcomes you want your AI to achieve.

Three Strategic Steps to Ensure Long-Term Data Readiness

Once the basics are in place, shift focus to creating a strong, scalable foundation for sustainable AI success.

1. Establish a Robust Data Quality Framework

Number one and the most important, in my view:

Get your data right. Implement robust processes to clean, validate, and enrich it. Remove duplicates, fix errors, standardise formats, and fill the gaps. Reliable data equals reliable models. It takes more time and effort than you think, and trust me, I’ve learned that the hard way.

2. Build Scalable Data Infrastructure

AI moves quickly, and so must your systems keep pace. At Toyota Connected, we've built a scalable, high-performance lakehouse that delivers the speed and elasticity AI innovation demands.

3. Strengthen Data Governance, Security & Privacy

Remember, the law binds you — make sure you're having those in-depth conversations with your privacy team or directly with your DPO. Follow UK data protection laws, especially the UK GDPR and the Data Protection Act 2018. Implement robust governance, access controls, and audit processes to safeguard sensitive data and maintain public trust.

The Bottom Line

If your data is clean, varied, well-managed and secure, you’re setting yourself up for success. But if it’s messy, stuck in silos or full of gaps, even the most innovative AI model won’t fix it. Every solid AI or analytics strategy begins with data, and that starts by understanding the difference between first-party and third-party data.

First-party data is the information your organisation collects directly from customers, systems, or internal operations and is typically the most relevant. This means it can be governed, cleaned, and enriched to align with your specific business goals. Enriching data is a discipline in its own right, and I have learned this firsthand while working on enriching connected car data with metadata and customer information at Toyota Connected.

However, organisations also rely on third-party data to fill in gaps or expand their understanding of markets, audiences, or behaviours. This all depends on your use case. While external data can be highly valuable, it also introduces new complexities, risks, and potentially significant costs—depending on the datasets you use. For instance, weather data can be expensive to purchase from commercial providers such as The Weather Company or AccuWeather, especially when dealing with large time-series datasets or detailed local forecasts. However, more cost-effective or free alternatives are available through open government sources, such as the UK Met Office DataPoint or the Office for National Statistics (ONS), which provide publicly available weather data.

Third-party data often varies in quality, structure, and freshness. There may be inconsistencies in how it's sourced, as well as legal and ethical concerns about consent and usage rights, and challenges in integrating it with your own systems. Without proper governance, you risk creating bias, duplication, or compliance issues that can undermine both trust and performance.

Your data ecosystem is only ever as strong as its weakest link. Real differentiation comes from having high-quality, well-connected, and transparently governed data — especially when you’re blending first- and third-party sources. Wishing you all the best on your AI journey — there’ll be highs and lows, just like any product lifecycle. Hopefully, we can catch up at a meetup soon to hear about your progress and share what we’ve both learned along the way.