When starting a new AI project within an enterprise, there are a number of best practices to follow and pitfalls to avoid.
Here are the top 3 things to consider before embarking on a new enterprise machine learning project:
1) Choose a problem you are already solving.
One of the biggest mistakes companies make when adopting AI is choosing a complex problem to solve. Often, this is because AI has been hyped as a ‘magical’ technology that can achieve the impossible.
Instead, choose a problem you are already solving. This allows you to start small with a performance benchmark to aim for. Prove AI’s value and capabilities for your organization before expanding to more complex use cases. This will allow you to understand what success means in an AI initiative, and what true ROI looks like.
This approach also allows you to break out of ‘AI pilot purgatory’, where several POCs and pilot projects have been developed without a clear ROI.
2) Coach business leaders on AI fundamentals.
The biggest problems in enterprise AI projects stem from poor expectations. This often happens because business leaders have not received proper coaching on the difference between hype and true ROI.
Therefore, it is paramount that non-technical business leaders receive proper training about what AI is, how it works, why it works, and common pitfalls to avoid.
An effective way to do this is to organize in-house trainings for relevant stakeholders. These trainings should be led by a technical leader with enterprise AI experience and strong communication skills. You may also hire senior AI consultants with the aforementioned attributes to assist with these initiatives.
In-house AI expertise and talent development should become an integral part of the company’s culture. Regular training and coaching sessions that develop team members’ understanding and skills will ensure the AI transformation is successful long-term.
3) Collaboration between AI engineers and business leaders.
Before, after, and during the development of an enterprise AI project, AI engineers and business leaders should stay in constant communication.
This involves constantly asking questions about the desired business outcomes, setting proper expectations, solidifying the data strategy, and iterating based on feedback/performance.
It’s also critical that conversations take place upfront about how the machine learning models will be deployed in production. In some cases, the data and labels you collect depend on how the models will be used in production. Therefore, start with the end in mind – the business question(s) AI will answer – and work backwards to determine the optimal data strategy.
These conversations should constantly take place between machine learning engineers and business leaders to ensure the project is delivered as efficiently as possible.
If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at email@example.com and I will be happy to help you.
Subscribe to this blog to get the latest tactics and strategies to thrive in this new era of AI and machine learning.
Subscribe to my YouTube channel for business AI video tutorials and technical hands-on tutorials.
Client case studies and testimonials: https://gradientgroup.ai/enterprise-case-studies/
Follow me on LinkedIn for more content: linkedin.com/in/CarlosLaraAI