Daniel Faggella

The following is a case study for Emerj's AI Opportunity Landscape research. To learn more about how we help companies develop winning AI strategies and identify the highest-ROI applications, watch the two-minute video summary of our AI Opportunity Landscape research.
Problem
The bank had many scattered AI projects, but struggled with:

In July 2017, The State Council of China released the “New Generation Artificial Intelligence Development Plan," outlining China's strategy to build a US$150 billion Chinese AI industry in a few short years, and to become the leading nation in AI by the year 2030. Other nations followed suit quickly with national AI strategies of their own - with the US trailing behind by nearly two years before developing a semblance of an AI initiative. The proposed 2021 budget for the national security budget in the US is $740 billion - with a billions of dollars being earmarked for AI specifically (learn more: US Public Sector AI Opportunity Report). 

This week we kick off the first episode in our new AI Futures series. This 12-part series will focus on the near-term and long-term governance of artificial intelligence. Our intention with this series is to take our grounding in the near-term applications or artificial intelligence, and extend the conversation forward to the long-term implications of AI.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is a coaching program involving one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks based on insights from AI leaders at the world’s largest enterprises (AI adopters and buyers), and successful AI vendors and service providers. Learn more and apply emerj.com/catalyst.

At Emerj, for over three years, we've been tracking the development of artificial intelligence startups and solution providers across industries, speaking to founders and team members and communicating with the enterprise leaders and buyers who depend on this ecosystem of AI solutions. 

Emerj serves enterprises to form their AI strategies, and data audits are part of Emerj's framework for identifying high-ROI AI projects. In this article, we'll break down a slice of this framework, walking through some pragmatic steps leaders can take to drive toward industry-leading outcomes in their organization.

After spending the last two weekends putting the finishing touches on our new AI ROI Cheat Sheet, my mind is swirling with quotes and AI ROI "rules of thumb" from some of our smartest interviewees and research advisors.

What to do when your clients want to cut costs?

Help them cut costs, and be part of a bigger vision beyond cost-cutting.

Despite the billions of dollars being poured into AI startups and applications - it is common knowledge that most enterprise AI initiatives fail.

The COVID-19 pandemic has caused incredible disruption in healthcare systems across the world, as well as an immediate demand for innovative solutions to the growing number of coronavirus patients. Many AI vendors are already trying to find ways they can serve this demand through augmenting their machine learning-powered products - from diagnostics interfaces to radiology solutions and everything in between. 

The coronavirus pandemic has ushered in a new era of digital payments; those who once mailed checks and made purchases in person are now paying their bills electronically and shopping online. As the economy is rattled by the coronavirus, there are some AI startups in the payments space that will succeed and others that will fail. All are pivoting rapidly to eCommerce, if that wasn't already their focus to begin with.

Fraud, money laundering, and other cyber crimes often increase in times of economic strife, and the pandemic is no different. In light of the coronavirus crisis, we believe that fraud detection applications are among the AI use-cases that are most likely to be adopted and deployed even when funds dry up for other kinds of more long-term, strategic innovation investments.

Consultants and professional services leaders face one of the hardest times in business in the last century.
Sales are drying up, companies are pulling back budgets, millions are jobless, and the business world scrambles to find some solid ground for a “new normal.”
For consultants whose livelihoods depend on closing deals, delivering value, and maintaining lucrative contracts, the economic conditions alone make things challenging.
But the economy is one of two great threats.
The second challenge for consultants doubles as an opportunity: Technology priorities are changing quickly.
At Emerj Artificial Intelligence Research, we’re fortunate to have a finger on the pulse of enterprise priorities in real time. Our research connections, our renowned AI podcast (closing in quickly on 3 million total downloads), and our enterprise client list allow us to gather intelligence fast. At no time has that been more important than during the coronavirus crisis.
Through polls of hundreds of enterprise leaders, in-depth interviews with directors of strategy and innovation at some of the world’s largest companies, and conversation with countless startup leaders, the new priority is clear:
Efficiencies.
It goes by many names: “Getting lean”, “streamlining operations”, and above all else - “automation.”
The era of Robotic Process Automation (RPA) and Artificial Intelligence (AI) will be ushered in by sheer necessity, and enterprises know it.
Consultants with experience in specific IT domains or traditional means of “digital transformation” will find clients with entirely new demands and questions, and the vast majority of consultants will lose out.
Our research has lead us to two requirements that the COVID-19 era consultant must have:

The insurance industry is being disrupted like it hasn't in decades. Unlike other events like Hurricane Sandy or even the 2008 financial crisis, the coronavirus is impacting essentially every corner of the world and more or less every industry.

In the last two articles in this 3-part series, we discussed how AI priorities will shift in response to the coronavirus pandemic, as well as how companies can further leverage the advantages they have (and can create) to overcome the challenges they are facing in this uncertain time. 

At Emerj, our research involves tracking AI and innovation across industries, dialing into where AI is driving ROI, which we do through our AI Opportunity Landscape research. In these hard times, we're expecting many AI startups to fade away and many technology priorities within large enterprises to completely shift to be more in line with what we're going to be articulating in this article. 

The COVID19 outbreak has changed the world faster than anyone could have imagined.

Forced isolation has shifted meetings and activities to go on through web collaboration tools.

The AI conversation has made it into the C-suite at large banks. Leaders from Citi to JP Morgan are considering how to respond to their competitors' press releases and looking to craft winning AI strategies and adopt low-hanging fruit AI applications in their business.

The artificial intelligence space is increasingly competitive with new AI companies and products being developed every day. Every industry is getting more and more crowded with products from startups and from established companies.

In the 1990's, Ben Horrowitz described a product manager as follows:
"A good product manager is the CEO of a product."
That definition isn't always a perfect fit, but it can be a good way of summarizing the responsibilities of a product manager; they are wholly in charge of bringing an in-house product from inception to generating an ROI.