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How to successfully scale your AI Project from Pilot to Production

Four practical tips on how to take a successful pilot project and adapt and scale it on Production.

Photo by Amélie Mourichon on Unsplash
Photo by Amélie Mourichon on Unsplash

No one else needs to convince us of the value of artificial intelligence today, both for business goals and society.

But for us to take advantage of all this value, we need to scale up AI projects. And the truth is, many companies are trying to do that right now.

In this scenario, AI pilot projects or AI Proofs-of-Concept are frequently used when introducing AI within an organization because they are quick to initiate a new process, product, or partnership while being a low-risk alternative more rigorous and costly "standard" projects.

But if your Pilot project is successful, how will you be able to scale it?

Going door-by-door in your company, saying that everyone should implement your new AI solution or tool does not sound good strategy.

A fundamental part of enabling adaptation to your unique pilot circumstances is establishing conditions that encourage teams across the company to continue adapting to the pilot.

There are some aspects and factors that are crucial to be observed when scaling AI projects. Those factors are, in general, determining which initiatives will be more likely to be successful.

Some success factors can be the bar that separates organizations that manage to scale their AI projects from those that are not.

Here I am listing some of these critical factors that can determine the success of the most common AI implementation in business:

  • Projects anchorage in business objectives and sponsored by senior management (C-level)
  • Ability to create an AI strategy and scale model with a well-defined organizational structure and governance
  • Multidisciplinary teams, not just led by IT leaders or restricted to the IT silo.
  • Clear and realistic expectations about the duration of AI projects and focused on the essentials.
  • Ability to develop data skills to extract value from analysis
  • Experimental Mindset
  • Availability of prepared Collaborators for the project.

Tips for a successful implementation

Having most of the previous critical factors in mind, I’ve prepared here a list with four essential tips to help you to design and manage a successful AI implementation plan to scale your Proofs-of-Concept and prototypes to the production level.

Do the right thing at the right speed!

Scaling an AI project is not a natural process, nor is it fast. In general, to succeed in this step, it will take about one to two years.

In addition to being driven by business purposes, success is due to careful preparation.

It means that compWehat is very pratic and prepares to establish the right data foundation, implement the right processes, allocate the right leaders, with the right skills, and do this.

To be successful, you will need to put your AI efforts into getting a concrete buy-in from the top management identifying specific use cases linked to business objectives, and redefining processes for data governance.

To have good results, spend the right!

It is not true that AI projects only yield good results when the investment is high.

Companies that are successful in scaling AI projects are spending less on longer projects because they are meticulous and strategic when deciding where to spend money.

They organize their data, hire the right talents and implement specific skills.

You certainly do not want to be the new member of the club of the organizations that fail to scale AI initiatives due to inefficient use of their budget, right?

Break your internal technology silos

Who said that AI projects must be run only by IT leaders and professionals?

Most of the time, successful AI initiatives are those that a single leader or sector is involved in, who will deal with all points related to Artificial Intelligence in the organization, but an interdisciplinary team.

The business must be connected to the technology because AI projects reported only to the company’s technology area are twice as likely to succeed than when under the command of the business area or the CEO.

Successful companies break the silos to reach the Business, financial, and, of course, technology leaders who work collaboratively in the AI initiatives.

Do the right thing with the right data.

Data is the raw material of any AI project, and there is no doubt that it needs to be well engineered.

In my experience, I saw many AI projects that bring up consistent problems in terms of data quality and completeness.

However, it is not always true that the more data, the better. Before you run across the company screaming for data, it is necessary to determine if you have the right processes: how the data is monitored and done efficiently.

This way, you will help in the engineering process in identifying and structuring the project’s necessary data, which has a minimal horizon and focused on the fundamental parts to reach the results that the organization wants.

Conclusion

For the most part, an AI pilot project is only the beginning of a long-term, collaborative, and iterative process until Production and ensures a good return of investment.

Revealing your business hidden potential to innovate with AI is something that you’ll be able to do if you allow the process to unfold.

One more thing…

If you want to read more about AI and how you can learn it, and how it will impact business and our society, the following articles can be interesting for you:

A (very) Brief Introduction to AI in the Industry 4.0

How AI and Digital Transformation will change your business forever

The most impressive Youtube Channels for you to Learn AI, Machine Learning, and Data Science.

And If you want to continue to discover new resources and learn about AI, In my ebook (link below), I am sharing the best articles, websites, and free training courses online about Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Business Intelligence, Analytics, and others to help you start learning and develop your career.

Learn AI online: More than 200 resources online to start learning AI today

Also, I’ve just published other interesting ebooks on Amazon, and I’m sure that some of them may be interesting for you… let’s keep in touch, follow me and let’s do it together.

https://www.amazon.com/dp/B08RSJRNSN

References

  1. Seven out of ten artificial intelligence projects fail …. https://labsnews.com/en/news/technology/seven-out-of-ten-artificial-intelligence-projects-fail-according-to-study/

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