Daniel Faggella

This article was originally written as part of a PDF report sponsored by Daitan, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.

This article was originally written as part of a PDF report sponsored by expert.ai, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

Increasingly, technology and business leaders look to AI project managers to make the execution (and success) of their AI projects more predictable. Executives and decision makers want AI projects to mature so they are more like the software development projects that have been with us for a generation. But, any AI project manager hoping to deliver on those expectations knows that success in AI projects requires an end-to-end thinking rarely found today.

In the enterprise world, more and more companies are crossing the chasm to test, and then deploy, their first AI solutions. To navigate this sometimes unfamiliar territory, enterprise leaders increasingly scrutinize the AI project selection process.

About AI Power: AI Power is an article series focused on the long-term consequences of AI, and how power is or will be influenced by AI technologies. Some previous AI Power articles - including "The SDGs of Strong AI" and "AI Ethics at War" have been popular over the years - but I've taken a hiatus from writing AI Power articles but suspect that I'll be creating more in 2022. I'm grateful to my friends for helping to put this first piece together - I hope you enjoy it. - Daniel Faggella

As far back as 2018 when we surveyed over forty banking industry leaders to discover the biggest issues with AI adoption, enterprise culture was already emerging as top of mind. Since then, we have found similar frustrations around enterprise culture in every industry. More than lack of data science talent, lack of appropriate culture serves as the largest and most consistent barrier to adoption.

Successful AI vendors know that 90% of the value they bring to the table lies in a deep understanding of the client's context, including:

Most early stage AI consulting firms don’t have the budget to hire expensive machine learning talent. For non-technical founders who can’t do the ML engineering themselves, this means getting creative when it comes to AI project delivery.

How AI project leaders provide the best chance at sustainable success in AI adoption?

The answer is simple: Frankly communicate both near-term and long-term value, and help leadership understand the importance of seeing measurable results, and the value of building a stronger AI foundation for future projects.

Artificial intelligence projects are more like R&D than they are like traditional IT. It is experimentation as much as it is adoption, and this difference is one of many reasons that AI projects take longer to integrate, and often hit bottlenecks that prevent them from being used in production.

Most AI product firms are founded and grown in a similar way.

It usually goes something like this:

One of the biggest hurdles to AI adoption and integration is a lack of proper expectations about applying AI in an existing business. Executives and their teams often go into the process blind because so few companies have learned these important lessons and challenges and because even fewer have successfully adopted AI in a way that delivers ROI.

The financial services industry is buried in paperwork, and the NLP use-cases in banking and insurance grow every year.

As artificial intelligence makes its way into more industries and workflows, more and more non-technical team members will be charged with leading AI projects. The next wave of AI catalysts will be familiar with AI at a conceptual level (read: executive AI fluency), but will mostly be expert in bridging AI's capabilities to important business workflows and objectives.

When most professionals think about “AI consulting” they tend to think about technical machine learning services, like: Building our data infrastructure, crafting and testing new algorithms, interesting AI systems into existing IT infrastructure.

In the vast land of opportunities that AI creates, how do we select the projects that will generate ROI? Do we gain inspiration from reading AI use-cases relevant to our industries? Do we search through our own lists of existing priorities and hope the applications for AI will become clear?

AI adoption involves more than educating stakeholder groups (SMEs, IT, leadership) on the technical nuances of AI. It involves navigating human motives and incentives.

While overt AI "flops" are less common than they were three years ago, the pattern of failure for AI projects is still much the same.

You can invest in AI maturity and future capability - or you can have "quick wins" with surface-level AI applications that have relatively short-term, narrow ROI.

Picking first AI projects in challenging - and leadership is right to be wary of making the wrong investment. The challenge lies in both (a) identifying the right projects, and (b) ranking and determining the right ones.