Advancing maturity when scaling AI project has been a great challenge for organizations – worldwide. No one else needs to convince us of artificial intelligence’s value for business and society.
But for us to take advantage of all that value, we need to be able to scale projects in AI. And the truth is, many companies are trying to do that right now. So the question is, where are companies at scaling and implementing AI?
Myths Around The Process Of Scaling AI Projects
While taking an artificial intelligence project from a proof-of-concept or pilot to the entire organization is a natural and desired next step, scaling is challenging in many ways.
But according to Accenture and BCG/MIT research, many of these challenges are caused by myths. The hype and mystification about technology create a disconnect between what is communicated by companies and their reality. Let’s look at some of them.
Doing It Right Is Doing It Fast
Scaling an AI project is neither a natural nor a quick process. According to the Accenture survey, organizations that succeed in this step take around one to two years, which demolishes the myth that doing it right is doing it fast in AI.
This can be summarized as follows: companies need, according to O’Reilly, to do more to put their AI efforts on solid foundations, get support from senior management, identify specific use cases linked to business objectives, build skills, define processes for data governance, etc.
Proving the value of this work – which takes time – is where agility appears. Pragmatism, not the full realization of perfection, shows itself as the ability to test, make mistakes quickly, and correct them even faster.
To Get Good Results, You Have To Spend A Lot
The myth that AI projects only generate good results when investment is high has led many leaders to believe that the few organizations that succeed in scaling AI projects are investing large sums and large human capital.
But the truth is, according to Accenture research, they are spending less – even with longer projects. This is because they are extremely careful and strategic when deciding where to spend money. They organize their data, hire the right talent and implement specific skills.
Therefore, it is not uncommon for many organizations to fail to scale AI initiatives because of inefficient use of their budget, which can even exceed that of successful companies.
Only IT Leaders Are Involved In AI Projects
According to analysts at Accenture, there is a myth that AI projects should be handled by IT leaders and professionals – such as CTOs, CIOs, and CDOs – exclusively.
On the contrary, it is not about a single leader or sector involved, who will deal with all the points related to artificial intelligence in the organization, but an interdisciplinary team. The executive doing business must be connected to the technology professional.
Every successful company has broken down silos into a team of business, finance, and technology leaders who work collaboratively.
More Data Is Better
Data is the raw material of AI projects and needs to be well engineered. And it is not uncommon for AI projects to bring up issues with data quality and completeness. However, it is a myth that the more data, the better.
Before looking for them, it is necessary to determine if we have the right processes. That is, how the data is monitored and if this is done efficiently within the business. Nor are problems such as the Dunning-Kruger effect, inferences based on very little evidence, rare.
This will help in the subsequent engineering process, identifying and structuring the data needed for the project, which has a very limited horizon and is focused on the fundamental pieces to reach the results that the organization wants.
Scaling Your Project In AI: Make This Your Next Step
Finally, scaling AI projects is critical to ensuring a good return. One more piece of data from Accenture: companies that achieved this had a 2x higher success rate. And a 3x higher ROI than those still in the POC phase.
This is no small feat. Even if it seems like an unattainable result given the challenges, efforts, and expenses that companies are facing to scale their AI projects.
Also Read: Artificial Intelligence Examples: The Best Applications