Think big, start small with AI

Jan 23, 2024
Nick White

In 2023 decoding and understanding how to leverage Artificial Intelligence (AI) was the top priority for most corporations around the world. But AI is not a 2023 invention and has already changed the world in many ways over the past decade. How else could Amazon, Google and Meta have been ready to respond so quickly to OpenAI and Microsoft making their large language, visual, and audio models available to the public? Our shopping, searching, navigating and socializing were already being fueled (and monetized) with AI.

With the release of ChatGPT, AI is now mainstream and the interest and investments from every services and product company has the potential to revolutionize the way we live and work. One of the key challenges facing organizations today is how to effectively leverage AI to drive business value - Think big but start small.

Think big

So, what does it mean to think big about AI?

It means breaking down silos and bringing together teams from across the organization to collaborate on AI initiatives. It means investing in the right technology and infrastructure to support AI development and deployment. And it means embracing a culture of innovation and experimentation, where failure is seen as an opportunity to learn and grow.

In short, thinking big about AI means embracing the possibilities of this transformative technology and working together to create a better future for all.

Start small

But you also want me to start small?

Yes, because today most AI projects fail!

  • According to a report by Dimensional Research, 8 out of 10 AI projects fail, while 96% of them run into problems with data quality, data labeling, and building model confidence.
  • Another study shows that nearly 78% of AI or ML projects stall at some stage before deployment, and 81% of the process of training AI with data is more difficult than expected.
  • In addition, a Forbes article states that back in 2018, Gartner made a widely shared prediction that 85% of AI projects would eventually fail.  

Ok, so what does it mean to start small with AI?

  • Don’t boil the ocean and try to fix all of the data at once (data foundations & infrastructure).  
  • Don’t try to build an AI application before you have self-service analytics in place.  
  • Build your data foundations & infrastructure while you deliver iterative value along the Data & AI Maturity Scale.  

In summary, treat data as a product.


For your organization to use AI to shape the future and realize value, you must be open to innovation and failure. However, if you take a crawl, walk run approach you can maximize the value you derive from the failures. For help thinking big and starting small with AI, contact us at

Origin Digital™