TL;DR;

All it takes to build AI-foundations is a bit of planning, some cultural shift and some broad upskilling.
Implementing AI at scale in an organisation can yield a wealth of benefits, from improving profit margins to saving workers' valuable time. But getting value out of AI projects requires long-term planning, culture shifts and organisation-wide training. However effective your AI product is at producing insights, it will not return value unless those insights are trusted by the end-users, and acted upon effectively. So how do you lay a strong foundation on which to build momentum and enthusiasm for taking AI to the next level?

Implementing AI at scale in an organisation can yield a wealth of benefits, from improving profit margins to saving workers' valuable time. But getting value out of AI projects requires long-term planning, culture shifts and organisation-wide training.

However effective your AI product is at producing insights, it will not return value unless those insights are trusted by the end-users, and acted upon effectively. So how do you lay a strong foundation on which to build momentum and enthusiasm for taking AI to the next level? We can take some tips from the books of companies who have successfully scaled AI.

The Harvard Business Review identified three important shifts that enable organisations to deploy AI at scale. Those are:

  • Building diverse, multidisciplinary development teams
  • Putting trust in data-driven decisions and AI outputs
  • Adopting an agile, experimental and adaptable approach

Let's take a closer look. To ensure that the AI projects in development have the biggest impact, it's important to have a diverse and multidisciplinary team working on them. A mix of skills and perspectives can help make sure everything is covered and the output meets all the needs of the end-user. Involving operational and management staff in the development process will also ensure that AI products address broad priorities, and that potential barriers to adoption are identified early.

To enable actionable outputs from your AI, there needs to be a shift from top-down decision making to data-driven decision making. The end-user of the product should feel empowered to act upon the insights and recommendations of the product in order to properly employ it.

Finally, organisations need to adopt a 'test-and-learn', agile mentality for AI development and deployment. In this model, all end-users become accustomed to trialling AI during its development, and come to see 'problems' merely as areas to be improved in the next version.

Putting it into practice

These three simple tips, in practice, can be much harder to implement. Successful AI deployment requires strong leadership. Thankfully you can get AI leadership training to help you support and reassure your team while paving the way to success. Remember, successful AI projects will enhance, not diminish roles.

To ensure that the end-users of AI products are fully equipped to extract value from them, the authors suggest that 50% of investment in analytics should be spent on activities that drive adoption, which includes training, both courses and on-the-job practices.

Implementing change in an organisation is a difficult task, which is why Nightingale HQ offer tailored training and support specific to companies attempting to adopt AI. It's essential to identify the unique needs of your own organisation when considering barriers to change, resources and an organisational structure in which to deploy analytic capabilities, rather than going on what seems to have worked for others.

By focusing on building a bridge between developers of AI, management and end-users, you can prepare your organisation to scale up AI and reap maximal benefit.