Becoming the AI champion for your organisation

How does your organisation leverage AI?  

The “AI conversation” in engineering teams has transitioned from understanding the latest developments to active project implementation within organisations. Companies with successful AI projects are already reaping the benefits of increased efficiency, reducing time and resources spent, and automations in their workflows. 

Engineering AI applications for forecasting results based on experiments, detecting failures based on test data, and validating large datasets are accelerating the R&D process.  And these applications are just the beginning.  You have probably already encountered or heard of other use cases from colleagues, competitors, or industry peers. using machine learning.  

For example, in the EV market, 58% of engineering leaders developing EVs believe AI is crucial to stay competitive in the EV battery market. (Source: a 2024 study conducted by Forrester Consulting on behalf of Monolith). 

The question to ask 

As the industry transforms, you should be critically evaluating your position and asking yourself:  

“How mature is your organisation in its ability to adopt and scale using a new technology like AI?” 

To answer this question, you don’t have the luxury of only looking inward.  You must gain a better understanding of current trends and cutting-edge uses of AI across different engineering applications. For example: 

• What competitors are doing with AI?
• Where is AI getting applied across my industry? 
• What is the currently available technology?
• How is AI applied in other industries that have similar traits as yours? 
• How does AI adoption vary across different regions of the world? 
• How, and where, are multinational companies addressing AI opportunities? 

At Monolith, we speak with many engineering leaders across several sectors (automotive, aerospace, etc.) and they are frequently surprised to hear about the various possibilities of AI. (Learn more in our case study white paper:  Case studies: AI solutions for battery validation). 

Gaining a business ROI from AI goes beyond the technical challenges.  Scaling from a pilot project to a transformational change in your process impacts a wide range of areas.  Check out our blog on gauging your organisation’s AI maturity: How mature are your engineering AI capabilities? 

Accelerating your AI adoption 

The most effective way to start applying a new technology like AI is defining and implementing real projects.  This blog outlines a practical, step-by-step approach for onboarding AI into your organization.  

At Monolith, we help guide AI adoptions from both an engineering and business perspective. Our customer success team has implemented AI projects directly within client teams across a range of machine learning use cases, and engineering projects.  

We have seen what works to help speed up projects so they happen faster and more efficiently, and we know exactly the kind of blockers you may encounter that slow projects down. This blog will cover: 

• How organisations typically approach AI project
  
• How to accelerate the change toward AI across your team 
• Common project blockers and how to overcome them 
  

Getting started: Guiding your team in the right direction 

The Monolith customer success teams work closely with engineers on AI initiatives to understanding their objectives and leverage their expertise to achieve desired outcomes. 

Who can be an AI Champion? 

Typically, although there may be many people familiar with machine learning, there is one primary driver of the change who will be responsible for the project. That person can be referred to as the “AI Champion” and is usually someone who is well-versed in machine learning but more importantly, an expert in in-house processes, workflows, and objectives.

The importance lies in having the combined understanding of what machine learning can do and what the current processes are to generate ideas on where both could be combined to see the most benefit.

Getting to the pilot project 

Eventually, the next big step is the one that gets the ball rolling: Commissioning a pilot project. This is where the use cases will be evaluated to see whether the benefits to be reaped are what they say they are, to have an estimate for the resource investment required, and ultimately to decide whether it is feasible to go ahead and plan a full-scale implementation project.  

Typically, the results from pilot projects can be quite positive and showcase potential benefits to the point that organisations will look to proceed with either the fully-fledged project, or the pilot may have led to discoveries of other potential use cases which would be more efficient to pursue. 

Typically, the positive results from pilot projects showcase the benefits and the organisation then proceeds to undertake a fully-fledged AI project. Either that project will be a larger-scale adoption of the pilot, or it can even be a different use case that was discovered during the pilot.  

Summary of the AI Champion's role

The key step to get the transitioning to AI moving is: commissioning a pilot project. This is where the use cases will be evaluated to see whether, the benefits to be reaped are what they say they are, to have an estimate for the resource investment required, and ultimately to decide whether it is feasible to go ahead and plan a full-scale implementation project.  

Typically, the positive results from pilot projects showcase the benefits and the organisation then proceeds to undertake a fully-fledged AI project. Either that project will be a larger-scale adoption of the pilot, or it can even be a different use case that was discovered during the pilot.  

To summarise, from a high level, the adoption of an AI project begins with interest from someone (the AI Champion) who then gets the team more involved by sharing resources, white papers, blogs, webinars, etc. until a pilot project is undertaken. Once that pilot project bears fruit, the fully-fledged project goes ahead, and the benefits (increased efficiency, additional validations, etc.) are achieved. 

In the next section, we will walk through the steps on how this can happen in more detail so that you can bring the benefits of an AI project to your organisation.  

Sharing resources to drive interest 

Sharing resources can generate interest, stay updated on developments, and encourage your team to delve into machine learning.

Starter resources: 

These resources are enough to get interest piqued, and to start familiarising your team with machine learning concepts, developments, and use cases. From there, the conversation transitions from theory to finding applications for your use case.  

To help get more involvement, you can start having discussions with experts already actively implementing such projects. 

Taking the conversation deeper

At this stage, you have gained the buy-in and support of your team, who are eager to explore the potential of a machine learning project. While reading blog posts, white papers, and case studies can provide some insight, having more focused discussions is crucial to getting started. Here are a few ways you can achieve that:

What are the benefits of undertaking an AI project? 

The terms AI and machine learning get thrown around a lot and it is important to avoid AI for AI’s sake. Finding a use case that is beneficial to your organisation is crucial. 

Check out our white paper on finding a good AI use case. 

Additionally, the benefits are best illustrated when looking at case studies which showcase the savings in terms of cost, resources, etc. These can even be used to estimate the potential gains for your own use case.  

Check out our latest white paper on 8 case studies of battery validation and AI. 

Starting with the pilot project 

During this stage, the pilot project should be achievable. This is where most of the uncertainty will be dispelled through the results of the pilot. To start, it is feasible to design a project drawing inspiration from the previously mentioned resources.

 Points you may be interested in comparing:  

• Methodology (setup, techniques, etc.) 
• Targets (accuracy, efficiency gain, etc.) 
• Data Processing (scaling, regularisation, etc.) 
• Tools used (coding libraries, etc.) 
• Model setup (model type, model configuration, etc.) 
  

These references can speed up a project and minimise wasted time.  

Common “hurdles” and how to overcome them 

Our customer success teams have worked with engineers on the frontlines of AI implementation projects. Our team has supported not only from an engineering and ML standpoint, but also from a business and project management approach.  

Below are some common topics that can stand in the way of a successful AI implementation.

Do you have the right data? 

Every engineering project will have its own way of collecting, storing, and capturing data. For a successful machine learning implementation, you need to have the right data. To find out more, check out this blog. 

Data safety 

AI data security is always a hot topic. Machine learning can sometimes be referred to as a “black box”, with uncertainties about what really happens to the data provided to models.  

There have been instances where large language models (LLMs) would provide outputs based on proprietary information and lead to copyright infringements. One big concern is fear that the model takes in proprietary information and makes it publicly accessible to others using the model.  

These issues are relevant to be aware of, but many of the engineering use cases we work with do not fall within the same category where those issues may come up.  

One way to reassure all parties involved around data security is to communicate the fundamentals behind how the models work, where they draw information from, and set up adequate data management strategies in place to satisfy your organisation’s requirements.  

Learn more in our webinar and blog on data strategy. 

Do we have the right people for this? How do we upskill our engineers?  

There are numerous free online resources and courses available to help upskill and train engineers in machine learning. These resources range from understanding the specific benefits of their use cases to learning about models and data limitations.

However, the most valuable learning experiences often come from engaging with industry experts and engineers who have undertaken similar projects.

To facilitate this, you can access the Monolith webinar hub, which offers a selection of on-demand webinars tailored to these needs. 

Conclusion: Your journey as the AI Champion 

The primary role of the AI Champion is to generate interest, facilitate discussions, and involve the team in exploring the potential of AI and to initiate a pilot project.

To achieve this, the AI Champion needs to engage various stakeholders, develop project timelines and budgets, and involve engineers in the discussions.

If you require support from experts with experience across different engineering disciplines, use cases, and clients, our Monolith Customer Success team has extensive experience in assisting engineering clients with their AI adoption. 

Whether they have a clear vision and need assistance implementing it using a no-code AI solution like Monolith, or if they need guidance on how they can reduce their testing requirements using machine learning, our team can help.

Get started today 

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