• Written by Azhar Aliyar, Director

I still remember my first meeting with the IT team to discuss the adoption of AsscherAi, our enterprise AI platform. The excitement was palpable, but so were the doubts. How do we get started with something that promises to change the way we work? The answer, I learned, lies in taking small, tangible steps towards a checkable goal.

Getting started with enterprise AI adoption can be daunting, but it's crucial to take that first step. Many organisations struggle to find the right starting point, and that's where the concept of a first thirty days comes in. By focusing on a specific, achievable goal, teams can begin to see the value of AI-powered insights and build momentum for further adoption.

Choosing a first case with a known answer

When selecting a first case for AsscherAi adoption, it's essential to choose one that already has a known answer. This allows teams to compare the AI-powered insights with existing knowledge, providing a baseline for evaluation. For instance, a shift supervisor checks the maintenance log to identify trends in equipment downtime. By using AsscherAi to analyse the same data, the team can verify the accuracy of the AI-powered insights and build trust in the system.

This approach also helps to identify potential biases or errors in the data, ensuring that the AI system is providing reliable information. By starting with a known answer, teams can refine their understanding of what works and what doesn't, setting the stage for more complex and nuanced applications of AI-powered insights.

Attaching new capability to existing routines

Another crucial aspect of getting started with AsscherAi is attaching the new capability to a routine that already exists. This could be a daily report, a weekly meeting, or a monthly review. By integrating AI-powered insights into existing workflows, teams can minimise disruption and make the adoption process more manageable. For example, the category manager compares two suppliers using AsscherAi, which provides real-time data on pricing, quality, and delivery times.

This approach also helps to ensure that the AI system is used consistently and regularly, providing a steady stream of insights that can inform decision-making. As teams become more comfortable with the system, they can begin to explore new applications and use cases, driving further adoption and value creation.

Who needs to be able to ask, not just who needs the answer

When implementing AsscherAi, it's easy to focus on who needs the answer, but it's equally important to consider who needs to be able to ask the question. This might include team members who aren't typically involved in decision-making, but who have valuable insights to share. By empowering these individuals to ask questions and explore data, organisations can unlock new perspectives and ideas.

This approach also helps to drive a culture of curiosity and experimentation, where teams feel encouraged to explore new possibilities and challenge existing assumptions. As our platform continues to evolve, we're seeing more and more organisations recognise the value of empowering their teams to ask questions and seek answers.

What to stop doing if the comparison fails

If the comparison between AI-powered insights and existing knowledge fails, it's essential to take a step back and reassess. This might involve stopping certain practices or processes that are no longer necessary or effective. For instance, if AsscherAi reveals that a particular report is no longer providing valuable insights, it may be time to stop producing it.

By being willing to stop doing things that no longer add value, organisations can free up resources and focus on higher-priority initiatives. This approach also helps to ensure that the AI system is being used to drive real change and improvement, rather than simply perpetuating existing practices.

When this approach is not enough

While the approach outlined above can be highly effective for getting started with AsscherAi, there are situations where it may not be sufficient. For example, if an organisation is facing a major crisis or disruption, it may need to take more drastic action to adopt AI-powered insights. In such cases, it's essential to have a clear understanding of the organisation's goals and priorities, as well as the resources and support needed to drive adoption.

If you're struggling to get started with AsscherAi or need guidance on how to drive adoption, our team is here to help. You can contact us to learn more about our platform and how it can support your organisation's goals.

Ultimately, getting started with enterprise AI adoption requires a willingness to take small, tangible steps towards a checkable goal. By choosing a first case with a known answer, attaching new capability to existing routines, and empowering teams to ask questions, organisations can begin to unlock the value of AI-powered insights and drive real change and improvement.