Everyone involved in the early stages of enterprise AI adoption wants to get ROI from AI/ML projects as fast as possible.
We all know artificial intelligence is the future, and organizations are rushing in to capitalize on this world-changing technology before AI disruption makes them irrelevant in the marketplace.
In a previous blog post, I covered how to get ROI from AI/ML projects. However, there are things that your organization needs to have in place before any ROI is realized.
AI adoption carries risk – more so than any traditional IT project. As of last year, approximately 85% of AI/ML projects fail. Broadly speaking, these projects fail because the project managers fail to anticipate key elements of success.
What can your company do to minimize AI adoption risk and position itself to get ROI from AI/ML projects?
Start with these 3 things:
1) Company culture
It all starts with the proper company culture.
All stakeholders involved in enterprise AI adoption must be aligned under the same mission to bring artificial intelligence into the organization to unlock new business value – whether internally or externally for their customers.
Everyone must also understand the AI adoption methodology: Setting hypotheses, creating experiments, measuring results through predefined metrics/KPIs, and iterating accordingly.
The ‘waterfall’ approach used in software development is a recipe for disaster in AI/ML projects. If your organization is already comfortable with agile methodologies, then you have a head start.
When it comes to culture, it’s also important that all stakeholders understand that artificial intelligence is not here to replace them or take away their jobs (this concern is common in the enterprise). AI is here to enhance their jobs and make everyone better, faster, and more efficient.
My first enterprise AI client was a great example of having the right company culture in place to successfully implement AI/ML projects that create business value. All stakeholders, from senior management to software developers, understood that things may not work the first time.
They created a culture where people were allowed to try new things, fail, and iterate/improve until they succeed. This lean startup type of culture is one of the main things that has allowed them to thrive in the marketplace for over 100 years (96% of all businesses are gone within their first 10 years).
They also understood that AI is not here to take away their jobs. They understood (through coaching and education) that AI is here to enhance their jobs, enhance processes, enhance operations, enhance products.
2) Understanding AI business fundamentals
Before data scientists or machine learning engineers deep dive in the technical implementations, they need a manager/leader that has a strong grasp of AI business fundamentals.
Business will always be more important than the AI itself. The only reason why we implement AI/ML projects is to unlock and create business value.
Here are some things AI/ML project/product managers need to know before implementing a project (and certainly before getting ROI):
- How to create and develop AI strategies
- What makes AI projects successful
- How to assess business value and impact
- How to measure ROI
- How to assess and manage risk
- How to lead AI teams successfully
One of the biggest mistakes companies make when adopting artificial intelligence is only hiring highly technical people who are unable to see the bigger picture.
Successful enterprise AI adoption requires business leaders who have a deep understanding of the business elements of the organization, while also being competent in AI/ML. The intersection between business and AI is one of the most important roles in the organization – more than the purely technical roles.
3) Data infrastructure
Data is always the biggest bottleneck in any AI/ML project.
Or rather, lack of a data infrastructure that produces quality datasets in a timely fashion.
These are some of the most common problems with data:
- No good data to begin with, not enough data, or no data at all (for a given use case)
- No labels (for supervised learning use cases)
- Imbalanced datasets that result in biased machine learning models
- Missing values or features in structured datasets
- No smooth process to produce quality datasets
Without a strong data infrastructure in place, it will be difficult to produce high performance machine learning models capable of solving the proposed business problem(s). Also, data scientists or ML engineers will not be ‘sitting around’ waiting for the data.
Make sure you invest resources upfront in creating a strong data infrastructure to make sure your organization has a process in place to generate the datasets needed to train great machine learning models. This investment in data infrastructure will also pay off immensely in the long-term as you produce high quality datasets over time.
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 firstname.lastname@example.org and I will be happy to help you.
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