AI Best Practice Guides

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.

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.

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.

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.

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?

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 is 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.

In the classic business book Good to Great, author Jim Collins talks about the different approaches for technology adoption between high-performing and average companies. Collins' research indicated that high performers tend to adopt technology as an accelerant to an existing, working strategy - while underperformers tended to adopt technology in an attempt to jumpstart a change in direction or strategy that they haven't yet undertaken.

The firms that will gain a genuine advantage from AI deploy the technology in a way that achieves short-term ROI, alignment to a long-term vision, and conscious development of AI maturity - including skills, data infrastructure, and more.

AI is not IT- and adopting artificial intelligence is almost nothing like adopting traditional software solutions.

Making AI work has a lot to do with "getting things right" even before a project starts, including:

Artificial intelligence deployments are fraught with technical and tactical elements that have to be executed well in order to see a return on investment: The data must be accessible, cross-functional AI teams have to work together, and even after an AI pilot seems promising - it often needs to be integrated into legacy systems to be deployed successfully.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

This is a contributed article by Ian Wilson, Founder at Strategy 4 AI - learn more about Ian's online AI strategy courses here. Ian is also the former Head of AI for HSBC, one of the largest financial institutions in the world. To inquire about contributed articles from outside experts, contact [email protected].
As the use of AI to support business operations moves through its maturity cycle we have passed a number of key milestones along the way. However, following a pattern reminiscent of previous emerging technology introductions, organizations initially hired experts only to balk when they used blasphemous words like "infrastructure," "industrialize" or "strategy" rather than soothing words like "use case," "quick win" or "easy ROI." 
Unfortunately, instead of sacrificing their misaligned expectations, many businesses sacrificed their experts... 
Fast forward a couple of years and many of those businesses, having attempted to cut corners and seeing mainly failure, now have a visceral understanding of what their experts were advising and are looking, with more experienced eyes, at how to move forward from this point.
However, many businesses are still making avoidable mistaken assumptions when it comes to the use of AI Capabilities to support business objectives. I like to call the most egregious of these assumptions The 4 Horsemen of the AI-Pocolypse:

Artificial intelligence isn't making its way into enterprise deployments easily. Despite a relatively widespread understanding that AI is an inevitable force for winning market share and serving customers, by name estimates some 80-90% of AI projects fail.

Setting expectations.

In our ongoing poll of new Emerj Plus members indicates that in addition to "accessing more AI use-cases" and "measuring AI ROI," our members consistently tell us that they join in order to "set expectations for AI projects" - especially with leadership.

Many books could be written on the subject of AI strategy, and we've seen that "strategy" means something different from one enterprise to the next. This article is a brief overview of the common steps in creating an AI strategy - in roughly the order that the steps are usually executed.

Vetting AI opportunities implies a hodge-podge of contextual knowledge that are almost never to be found together in a single expert, including (but not limited to) an understanding of:

Artificial intelligence is poised to change every industry - and to create trillions in economic value over the coming decades.

This article was a request from one of our Catalyst Advisory Program members. The Catalyst Advisory Program is an application-only business growth coaching program  for AI consultants and AI service providers. The program helps AI consulting and services leaders win more deals and deliver more client value. Members receive one-to-one advisory, group coaching, and proprietary Catalyst AI best-practice frameworks. Learn more or apply at: emerj.com/catalyst.

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.

In the final installment of the "Pitfalls to AI Adoption" series, we talk about predicting the ROI of AI. There are a lot of misconceptions running rampant around the ability to gauge the return on investment of artificial intelligence. In this article, we talk about what can and can't be done when it comes to investing in artificial intelligence and predicting what the return might be.

In this second installment of the "Pitfalls to AI Adoption in the Enterprise" series, we're going to talk about underestimating the integration needs of artificial intelligence and machine learning.

What's harder than training an algorithm to detect images or automate a process? Collecting and cleaning the data in the first place.

This article is part 5 of a 7-part series called “AI Zeitgeist,” where we’ll be mapping out the details of AI adoption over the next 10 years and explore the critical changes in the AI ecosystem that business leaders need to understand.

Most of the time when we have requests for speaking engagements here at Emerj, they're from business leaders. At the time this article was published, I just came back from a presentation at National Defense University in Washington DC. Presenting there was unique in many regards. Obviously, the use cases for tanks and submarines are quite different than they are for drug development or selling more products off retail shelves.

Almost all young companies and companies in the start-up ecosystem today would like to talk about how they leverage AI or machine learning. In most cases, this is to make their product sound special and exciting. However, the fact is most young companies do not have the resources or the talent needed to leverage artificial intelligence in a way that would add to their value proposition.

Chances are you have already been bombarded on social media or in your inbox about all these “revolutionary AI” this and “game-changer AI” that.

It's easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for "machine learning" since 2012 - but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems.

Artificial intelligence deployments are fraught with technical and tactical elements that have to be executed well in order to see a return on investment: The data must be accessible, cross-functional AI teams have to work together, and even after an AI pilot seems promising - it often needs to be integrated into legacy systems to be deployed successfully.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/plus.

AI vendors and enterprise buyers struggle to get on the same page about the return on investment (ROI) of AI solutions.

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.

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.

What is the state of AI in business today - and what do mid-market business leaders need to know about AI now?

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.

The en masse shift to online shopping that transpired during and post-COVID appears as if it's destined to last. According to the Brookings Institute, one third of US adults have used delivery apps to order from a restaurant or store in the last year. 

Relying entirely on consultants or vendors is not a viable strategy - as AI adoption inherently involves new capabilities to be built up within the company, both technical and non-technical. The only reason a company would put AI technical development and implementation entirely in the hands of a vendor is ignorance of how AI works.

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

Novice AI project leaders measure projects entirely by (unrealistic) near-term financial benchmarks.

Artificial intelligence isn't making its way into enterprise deployments easily. Despite a relatively widespread understanding that AI is an inevitable force for winning market share and serving customers, by name estimates some 80-90% of AI projects fail.

Vetting AI opportunities implies a hodge-podge of contextual knowledge that are almost never to be found together in a single expert, including (but not limited to) an understanding of:

This article was a request from one of our Catalyst Advisory Program members. The Catalyst Advisory Program is an application-only business growth coaching program  for AI consultants and AI service providers. The program helps AI consulting and services leaders win more deals and deliver more client value. Members receive one-to-one advisory, group coaching, and proprietary Catalyst AI best-practice frameworks. Learn more or apply at: emerj.com/catalyst.

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.

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

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. 

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.

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.

In this article, I'll showcase 6 examples of nontechnical professionals who used their business and subject-matter expertise (not their coding ability) to have more exciting careers in AI, and I respond directly a number of questions and comments from Emerj subscribers about AI knowledge for career advancement.

This is the second article in our "AI for Career Acceleration" series - be sure to read the first installment (and watch the video on that page).

Last month we ran a podcast series on the AI in Industry podcast on the theme of “Advancing Your Career in the Era of AI”, with a focus on how non-technical professionals can become more valuable in the market, and can become involved in AI projects and initiatives, without ever learning to code.
I received twice as much feedback on this series as any other series we’ve ever run on the podcast - which surprised me.
It surprised me because I think about everything on Emerj.com as being useful for nontechnical professionals. We’ve built our editorial calendar and our products around the needs of nontechnical professionals who want to make the most of their careers, but this recent series spoke to that topic directly.
But hitting directly on the theme of “Advancing Your Career in the Era of AI” clearly hit a cord.
For that reason, I’ve decided to release a three-part video and article series on that same topic, breaking down the lessons that were most important for me - and sharing a bit of my own story going from small-town martial arts instructor to international AI speaker and strategist.
Before getting into the small-town martial arts instructor part, I’d like the share a pivotal Silicon Valley conversation that changed the course of my career:

Should I attend an artificial intelligence event or not?

What event should I go to?

Should I go to this event?

We talk a lot about the concept of connective tissue here at Emerj, the fact that a company that wants to apply AI not only needs to have access to data, not only needs to hire normally very expensive artificial intelligence talent, but also has to have the connective tissue of related subject-matter experts who can work with that talent.

We have discussed the importance of having the right talent in place when it comes to AI adoption in enterprise quite thoroughly here at Emerj. The scarcity of data science talent and its price point are one of the main reasons small businesses are not likely to adopt AI successfully at this time.

For a tech subsector on everyone's proverbial lips' at the moment, things can hardly be described as 'easygoing' for the AI vendor startup market in 2023.

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.

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

It usually goes something like this:

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.

This article is the third in a series part in a series about AI product development.

In the first installment in this series, we covered how to develop AI product ideas with both near-term adopt-ability and long-term potential.

So you've decided you want to take an AI product or service to market.

Before you sell anything - you'll have to decide what kind of product or service to develop.

Whether you're a startup or an enterprise, developing AI products is challenging.

Not only do you have to wrestle with the challenges of finding a use-case that where AI can actually deliver value into an enterprise workflow, but you also have UI concerns, and - often - much higher demands to monitor algorithmic drift and other technical issues.

In this article, I'll explore some of our lessons learned in getting the value of AI products or services to stick with enterprise buyers.

The firms that will gain a genuine advantage from AI deploy the technology in a way that achieves short-term ROI, alignment to a long-term vision, and conscious development of AI maturity - including skills, data infrastructure, and more.

Over the last three years, Emerj has had the privilege to work on hundreds of individual thought leadership and lead generation campaigns for AI companies around the world - via our Creative Services arm.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

While AI vendor companies proliferate in every sector, enterprises need much more than advanced technologies to actually adopt and deploy AI. AI services and consulting firms are expanding to fill the gap - offering services that enterprises often don't have the in-house talent for, including:

This article was a request from one of our Catalyst members.

The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more and apply emerj.com/catalyst.

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.

When we're called into an enterprise for our AI Opportunity Landscape research, it's common for me to discover that the reason we've been called in is that the company has (a) already spent millions with vendors they didn't see results with, or (b) they have 3-4 AI pilots underway with very little traction.

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.

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.

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.

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. 

Welcome to part three of this three-part series on go-to-market strategy for an artificial intelligence product or service. In part one we talked about what kind of insights you want to get out of a go-to-market strategy. In part two we talked about how those insights are gleaned.

In part one of this series, we talked about what insights to get out of a go-to-market strategy project. In this second installment, we're going to be diving into how to get those insights.

When clients come to us for custom research projects, sometimes it's because it's a big firm that's looking to make acquisitions or it's a company that's looking for some kind of competitive intelligence. They're looking at who else is offering things like them and how they can position themselves against those other competitors in the market.

We've seen a lot of what we call "fake AI rebrands" in the last 18 months, and I suspect that as long as AI is a buzzword, we will only see more and more of this. Business leaders are going to have to keep their eyes peeled for these kinds of companies in their midst.

One of the biggest problems facing business executives when it comes to adopting AI is determining whether a company is truly leveraging AI or simply using the term as a marketing strategy. We have discussed rules of thumb for assessing the authenticity of AI companies in previous articles based on insights derived from hundreds of interviews with industry experts and AI researchers over time.

A few months ago, we spoke about three rules of thumb for assessing companies and trying to figure out if they are really doing AI or just using it for marketing purposes in our How to Cut Through the Artificial Intelligence Hype piece. Since it was so popular, I was inspired to write this follow-up.

Artificial intelligence and machine learning are hot terms right now, and many companies are eager to talk about how their products rely on these cutting edge technologies. Established companies and startups want to explain how AI is a critical part of their business.

Insurers have been long aware of the perfect storm of converging market trends pointing to the permanency of digital disruption and change. Yet, the industry – especially the big players – always seemed to move their collective house further away from the shoreline. 

DHL is a German logistics company that offers parcel delivery, express mail, freight forwarding, and third-party logistics. Its parent company is Deutsche Post (‘DPDHL’), the world’s largest logistics enterprise, operating in over 220 countries and employing more than 510,000 people worldwide. 

Although not often regarded as a technological first-mover, the insurance industry has recently seen robust, even rapid, adoption and deployment of AI capabilities, particularly in those related to natural language processing (NLP). 

A paradigm shift is happening in the manufacturing industry. Advancement in big data and machine learning is changing traditional manufacturing processes into the era of intelligent manufacturing. The concept of what gets called "industry 4.0" encourages the use of smart sensors, devices, and machines – going beyond the motives of collecting data about production. 

The manufacturing industry has changed in recent years. Humans on the shop floor have always used their senses and experience to anticipate machine failure before it occurs. Now, AI can be used in conjunction with human expertise.

ExxonMobil is the largest investor-owned company in the world and the largest oil company by revenue in the Western world.

Insurance is a growing arena for AI adoption and in many cases, automation is leading the way to streamline customer experiences and the organizational pipelines behind them along the entire customer journey.

The main difficulty in starting with AI is knowing exactly where to begin. As we will elaborate in this article, there are many exaggerated and misleading claims about the various types of AI technology. Moreover, some executives are too trigger-happy, wanting or needing to integrate AI into their enterprise sans a foundational education or understanding of the challenges. 

What does developing an AI strategy look like? Most executives openly embrace the technology and the automation possibilities within their organization. They may just need a bit of help getting started.

It is not hyperbolic to state that the autoregressive language model known as GPT-3 (short for ‘Generative Pre-trained Transformer 3’) is an unparalleled evolutionary step in content creation. In fact, the potential outputs the model can produce are so advanced that many academics have cited GPT-3 as Exhibit A in calling universities to reconsider what constitutes academic plagiarism. 

Actionable, decision-augmenting data can be obtained internally or externally. Of course, external data is a far richer and more diverse source, as it comprises every other piece of digital information outside of the four walls of an enterprise. 

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

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.

This article is the third in a series part in a series about AI product development.

In the first installment in this series, we covered how to develop AI product ideas with both near-term adopt-ability and long-term potential.

Over the last three years of AI Opportunity Landscape research, we've examined many broad capabilities across the AI ecosystem, from computer vision to conversational interfaces to anomaly detection and beyond. Some of our earliest client research work focused on back-office automation - mostly in financial services and healthcare - and it brought us face-to-face with an array of vendors, use-cases, and opportunities for applying AI for document search and discovery.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

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.

Supply chains contain every material, component, product and packaging for the objects that together compose the world we live in. However, there is an often invisible ingredient to successful supply chains: data. 

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:

Business leaders, managers, and consultants with an eye on AI aren’t just trying to learn what AI can do, they’re trying to discover ways to gain an AI advantage.

There is always a fascination with what the biggest and most powerful companies in any given sector are doing. If you look in the world of eCommerce, everybody is ultimately referencing Amazon. In the world of banking, firms like JP Morgan are referred to and everyone's interested in their newest hires in terms of the C-suite and their newest innovations in terms of technology. Analyzing bigger firms seems to have a level-setting effect.

We’ve made it to article seven of seven in this “AI Zeitgeist” series. It’s been a while building up to this, and I’ve kept the competitive dynamics of AI as the topic of this seventh article because to me everything builds up to this.

We come to the sixth installment in the "AI Zeitgeist" series, and this one is about the changing landscape of artificial intelligence priorities for business leaders.

This article is part 5 of a 7-part series called “AI Zeitgeist,” where we’ll be mapping out the details of AI adoption over the next 10 years and explore the critical changes in the AI ecosystem that business leaders need to understand.

This article is part 4 of a 7-part series called “AI Zeitgeist,” where we’ll be mapping out the details of AI adoption over the next 10 years and explore the critical changes in the AI ecosystem that business leaders need to understand.

This article is part 3 of a 7-part series called "AI Zeitgeist," where we'll be mapping out the details of AI adoption over the next 10 years and explore the critical changes in the AI ecosystem that business leaders need to understand.

This article is part 2 of a 7-part series called "AI Zeitgeist," where we'll be mapping out the details of AI adoption over the next 10 years and explore the critical changes in the AI ecosystem that business leaders need to understand.

The following statements are all true, but not in the way you might think:

AI is hard for most companies to adopt.
Machine learning talent is hard to find and hard to hire.
AI gives some companies new ways to compete in the market.

Here at Emerj we’re dedicated to cutting through the AI hype that’s permeating the current zeitgeist in the business world. Although we’re skeptical about many of the claims that AI vendors make on their websites about what they’re AI software can do, it seems unlikely that the AI hype is going to disappoint venture capitalists and governments enough to usher in a third AI winter.

Something that dawned on me very early on in reading biography and history is that incentives rule the world, that company's, nations, individuals ultimately do things primarily for their own self-interest. And there's nothing inherently wrong with that, but it's important to bear it in mind.

If one tunes into social media, it's easy to be convinced that every AI startup is making a lot of money, making a big difference in the companies that they're working with, and driving real revenue. However, as we've mentioned many times here at Emerj, most AI applications are really in pilot mode.

Finding the right people to do a job has always been a problem, especially when it requires a high level of expertise. Hiring professionals (84%) are relying more and more on social media to find the right talent, and B2B executives often look to LinkedIn for leads when it comes to finding the right companies to provide crucial services. It is no wonder that people and companies hoping to catch their attention make a point of putting up a robust profile on LinkedIn. However, businesses looking for companies to provide artificial intelligence services need to look at these profiles carefully.

Broadly, artificial intelligence involves a machine doing something that only a human would be able to do. That said, computer scientists disagree on if certain computing capabilities from several years ago still constitute AI. Nowadays, many of these capabilities might just be called software.

We discussed the difficulties large businesses may have in adopting AI in our previous article; despite this, last month we fleshed out the reasons why it’s still more difficult for small businesses to apply AI than the enterprise and how they might catch up to larger businesses in the future.

This article is based on a presentation given by Emerj CEO Dan Faggella at a recent conference “Artificial Intelligence and Business Ethics: Friends or Foes?” held at the University of Notre Dame. 
The conversation about artificial intelligence and ethics ranges from the broad to the specific. On the one end, there are governments making laws and policies on national, state, and local levels. In the middle, you have industry-wide agreements on certain protocols on privacy expectations, ways of handling customer data, and other ethical considerations within the framework established by the government.

Artificial intelligence and machine learning are hot terms right now, and many companies are eager to talk about how their products rely on these cutting edge technologies. Established companies and startups want to explain how AI is a critical part of their business.

AI is being likened to a "Fourth Industrial Revolution" because of it's potential for creating profound change in people’s lives, much like the invention of steam engines (First Industrial Revolution), electricity and mass production (Second Industrial Revolution), and the rise of the digital age (Third Industrial Revolution) had in the past.

Many books could be written on the subject of AI strategy, and we've seen that "strategy" means something different from one enterprise to the next. This article is a brief overview of the common steps in creating an AI strategy - in roughly the order that the steps are usually executed.

Amidst the uncertainty of COVID19, our polls and surveys of enterprise leaders have show two trends to be consistent:

Artificial intelligence is poised to change every industry - and to create trillions in economic value over the coming decades.

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. 

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.