Daniel Faggella

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. 

Many business leaders make the mistake of believing that AI and machine learning are like regular IT, but this could not be further from the truth. In large part, this is because, unlike simple software solutions for discreet business problems, it can be very difficult to measure the ROI of machine learning.

You might not know it when reading AI vendor websites or press releases from enterprises, but when you dig deep enough into why enterprises actual adopt AI, the pattern is clear:

Companies looking to apply AI are looking for a competitive advantage in their industry, something that will give them an edge in the market and help them grow. However, not every AI application can give a company a competitive advantage. Many AI applications are simply going to become the new normal.

One of the many concerns that business leaders have around automation is how they're going to adapt their workforce to new technology. The digital age has brought a rapid shift in the skillsets employees need to go about their jobs effectively, leaving little time for business leaders to come up with strategies for how to reskill and retrain their employees, prevent their companies from drawing disdain from the public, and how to compete with their peers.

There are numerous AI initiatives in progress across the healthcare industry; some of these are for mental health and well-being. In this article, we offer an overview of how AI is facilitating mental healthcare.

Over the last four years, interviewing hundreds of AI researchers and AI enterprise leaders, we've consistently heard the same frustrations about AI adoption said time and time again.
"Culture is hard to change."
"Leadership doesn't know what they're trying to accomplish."
"Nobody knows what to do with these data scientists we've hired."
etc...
In our one-to-one work with enterprise clients, we've taken the most prevalent, recurring challenges to AI deployment and put them together into a framework of "prerequisites" to AI deployment.

Emerj Technical Advisor, German Sanchis Trilles, PhD, defines natural language processing as:
“...everything which is related to human language. If you have a system that needs to recognize what a human wrote, that’s NLP. If you have a system that tries to understand what a human said with his voice or with her voice, that’s NLP as well. If you want a system to speak and to do some speech synthesis, that’s NLP as well.

Content marketing encompasses many ways to advertise and attract new potential customers. At Emerj, we've worked on hundreds of campaigns for AI-related products and services, and what we've learned is that content marketing for AI vendors boils down to two things:

Companies often emphasize artificial intelligence on their homepage or on services pages. While AI is currently an important topic, a simple mention of it on a website’s homepage will not get potential customers interested. Instead, it is more important to focus on the direct needs of the stakeholders who find an AI vendor website and their online properties in order to get them closer to a sale. 

Event Title: Symposium Artificial Intelligence for Science, Industry and Society