Identify Enterprise Firms Most Likely to Spend on AI Projects

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

Identify Enterprise Firms Most Likely to Spend on AI Projects

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Identifying “movers and shakers” in enterprise AI adoption is important.

Enterprise innovation leaders want to know which of their competitors are using AI – or which companies to watch when it comes to molding their own AI strategy.

AI consultants and services leaders want to know which companies are most likely to be good prospects for their offerings.

This week, I break down some simple rules of thumb to determine which companies (by size, industry, and other factors) are most likely to have AI budgets. I’ve broken this article down into positive factors (factors that correlate well with AI spend and AI budgets), and negative factors.

Commonalities of Organizations with Substantial AI Buying Power

1 – Industry

Identifying potential clients that would go into AI services in a big way comes down to a few factors.  One of these is early adoption and investment in AI to address inherent issues in a particular industry.

If the goal is to close very large and substantial AI projects, the first concern is to identify potential customers that are already spending considerably in AI. These are enterprises or sectors that are spending money overhauling systems and making genuine efforts in AI integration in bold ways, relatively speaking, as much as an enterprise can do. 

Financial Services

The top contender in that sense is going to be financial services, which might not be great, but it is true in every geo region of the world. Anecdotally, this is the conclusion from researching the vendor landscape and speaking to AI buyers and leaders in different sectors and different places. 

When identifying financial services, this refers to banking, wealth management, and insurance. While these are not interchangeable sectors, these are clustered for a reason. For instance, there is a tendency in financial services where if you get projects in banking, it lends you credibility in insurance, although not necessarily in pharmaceuticals. 

Banking (retail, commercial) is probably the biggest market with the most dollars to throw at AI services, but wealth management is also a serious consideration. Wealth management represents a smaller market than banking, so overall it spends less on AI services, but pound for pound is more aggressive in terms of the kinds of projects it undertakes. 

The reason for such interest in financial services in AI, particularly baking and wealth management is because AI can mitigate many of the biggest problems in the industry. This includes regulatory compliance, fraud detection and prevention, cybersecurity, customer service, and so on. 

Banking has so many different facets of risk, and by extension, so does wealth management. These sectors are looking to become more profitable, and AI services can help them manage their risks to achieve that more easily. 

Aside from risk abatement, wealth management companies are also looking to investing significantly on tools for external search and discovery. This refers to pulling in various data sources to create relevant information streams to help traders make more money. 

Compared with banking, insurance is slower in adoption of AI services, despite being “the original big data business”. However, the funds being allocated to AI applications in insurance is still substantial, and it’s a portion of the financial services landscape that warrants attention from AI service provicers.

This is immediately apparent with a cursory look at the coverage of insurance in daily life. Everyone needs insurance for one thing or the other, so insurance companies are dealing with a lot of data. They are pulling in numbers, making statistical inferences and assessments of risk, and creating actuarial tables on a constantly shifting landscape. The insurance industry is a natural customer for AI services, and they are in fact spending more money than they should in areas that would benefit from AI technology simply because they had not overhauled these processes. 

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Supply Chain and Retail 

It is in the early days of AI, it is already apparent that supply chain and retail and e-commerce are major clients for the market. These are the big retail firms that are inherently aggressive in terms of revenue generation and marketing applications. E-commerce companies in particular raise the most scrutiny from AI services firms, although big players are more likely to invest significantly in AI than small and medium enterprises in the space.

For the brick-and-mortar retail sector, some chain stores might have the technical knowledge to see the potential of AI services for the bottom line and be willing to adopt AI. Generally, however, it is the very big retail firms with substantial e-commerce presence becoming more and more a part of their revenue stream that will benefit most from AI services. BestBuy might be a good example of this case. 

Most industries will use AI services in one way or the other, but in some cases, it is kluge to use AI services in that it is used as a workaround rather than a smooth and holistic transition and adoption of the technology.  That said, the potential is there for large AI services projects in the following industries. 

Telecommunications

It might be surprising to find out that the telecommunications industry is not doing much with AI services firms. In most cases, telecom companies are making significant improvements internally, but there is no huge vendor ecosystem around them currently. If the goal is to identify a potential client for huge AI projects, the telecom industry might not show much promise in general. 

Manufacturing

Manufacturing is a big space for AI services firms with the potential for lucrative projects, and the industry is certainly awake to the fact that big data and AI can benefit it enormously. However, integration of AI into manufacturing systems is notoriously difficult to do. Experts use the word kluge to refer to AI adoption in manufacturing, meaning that it is often a clumsy and inefficient workaround that is difficult to scale and maintain. In terms of smooth integration and low-cost adoption, manufacturing might be the one with the most issues that go beyond interest and investment.  

Pharmaceuticals 

Pharmaceutical companies have huge resources, operating with substantial margins, which make them a potentially lucrative space for AI services. However, the hitch occurs in resolving the differences in culture between the life sciences and computer science. This makes the situation a real kluge for AI services firms. It can be very difficult to integrate AI services with existing systems. As a result, there has not been anything significant happening in the pharmaceutical industry, 

In addition, AI services firms have found it a kluge to get a foot into the vendor ecosystem in the major pharmaceutical companies. While this perspective is anecdotal, it is quite obvious that the industry is generally very rolled up, particularly in the geo locations. It can be very difficult for an AI services company to make a pitch.

Defense

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It goes without saying that the military has significant resources and will keep investing in technology despite recent issues impacting private defense companies.  However, the defense industry is like pharmaceutical industries in that it is very rolled up, tending to stay with established vendors. AI services firms that have not previously worked with the military will have to invest time and money in building relationships before they can even have a chance to bid for projects.

That said, it might be worth making the effort as the military has not always been good at making the best use of AI in the past, mostly because it is a big bureaucracy. It moves slowly, and perhaps not always in the right direction. Efficient and strategic use of AI requires an agile approach, without which it would be difficult to deploy properly. For AI services firms coming in from the cold, defense is not an easy target for big projects.  

2 – Location

The second factor to consider in assessing the potential of an AI services firm to land large projects is location. While it might not make a lot of sense, the location of a firm can have a significant on its chances to get a slice of the pie. 

USA

An AI services firm in the US has the best chance of landing lucrative projects in any industry in the US and other countries. This is largely because US tech giants are widely considered to dominate the AI space.  However, the location of the company within the US also matters. 

Generally, firms in major cities have more credibility than those based in minor ones. For example, two companies, one headquartered in New York City, and the other headquartered in Madison, Wisconsin, compete for the same project. They are similar in size and revenue, and the firm in Madison is doing some innovative developments in AI. That might not matter in most cases. A potential client poised to award a large project will most likely choose the firm in New York City by virtue of its location. 

This tendency might be peculiar to AI services, and it might not always seem logical, but it is a recognized factor in success. Potential clients are looking to hire a firm in major tech cities in the US when it comes to projects that involve a substantial investment. 

Canada

Canada might be the exception to this rule. While AI development is slower in Canada, companies based there believe they are unique, and are not open to working with vendors in the US most of the time. Market research reveals this bias, and includes tech vendors. 

AI services firms in Canada have the advantage when it comes to bidding for projects for Canada-based companies, despite the fact that tech companies are not on the cutting edge of development. There are pros and cons to that culture, but they facts is they are moving slower than in the US. 

Western Europe

AI services firms should understand that industries in Western Europe involve unique data concerns as there is much less risk tolerance. They are mostly interested in finding precedents in AI technology rather than be the first to try anything in Europe, including AI services. 

This aversion to risk is behind the popularity of Emerj.com AI strategy reports and Emerj Plus subscriptions in Europe. All things being equal, Western Europe companies convert at a higher rate on Emerj products than companies in the United States. 

The opportunities in Western Europe is there for AI services firms, but they are not going to take any big risks when implementing or adopting AI even if they are familiar with AI technology and trends. AI services firms looking to get into large projects will have a greater chance of success if they can produce case studies demonstrating the effectiveness of a proposed technology. 

3 – Size

Market Leaders: When determining the potential for a big pay-off in AI services, the size of a company is definitely a major factor. Size refers to the actual size of a company, as well as its market share and revenue.

Generally, AI services firms can expect companies with the truest strategic appetite to be in the top 5 to 10 in their respective industries. The industry itself does not really matter. Top companies in any given industry tend to have a much higher risk tolerance and aggressiveness than companies further down the ladder. 

For example, Emerj’s AI Opportunity Landscape data shows that the top three banks in the United States are doing much more with AI than the rest of the top 10 banks (by revenue) combined. It would be accurate to say that the top three banks are doing as much as the rest of the top 20 in terms of AI investments, initiatives and forward momentum. 

$1B and Up: This rule of thumb is worth bearing in mind when entering a new industry. To have any hope of success doing AI at all, companies with more than $1 billion plus in revenue are the most likely to make the leap.

That said, there are some exceptions to this rule. E-commerce and online media are among these because companies in these industries are much more digitally native. It would not be necessary to target only the big companies to be able to take on AI projects. The thing is, companies in these industries are also much more likely to in-house talent to handle their AI needs, so AI services firms will have to offer a unique value proposition to sell them on the idea of taking on a project. 

4 – Reputation

When profiling a company as a prospect for pitching a large AI project, it is important to look at its reputation when it comes to technological adoption. In some industries, the top 5 or 10 of the industry with a reputation for being farther along in terms of digital transformation and more aggressive in terms of technology use are most likely to get onboard with AI services. 

For example, some companies in the banking industry have this reputation. Capital One is not the biggest companies in the credit card space or in other financial services. In fact, it is in a smaller niche than the biggest companies in that industry. However, in terms of banking in the United States, Capital One is known for being more aggressive in terms of technology, investment and involvement than other companies of a similar size. 

Similarly, there are four very big banks in Australia, of which ANZ ranks third in terms of revenue. It has the reputation of being more interested in tech projects, consequently being farther ahead and more aggressive in terms of technology than other banks in the same category. Because these companies already have an intense interest in technology, they are most likely to listen to a pitch for any large project in AI services. Even if they are not the biggest company in their space, their reputation shows they are receptive to the idea of cutting-edge technology projects. 

Factors That Bode Poorly for AI Spend and AI Budgets

1 – Industry

Healthcare: Healthcare is not a good prospect for AI services firms in general. Pharmaceutical companies might have substantial resources for technological innovations, but that is not the case for hospitals. Additionally, there is the issue of complicated stakeholder arrangements in healthcare. 

To illustrate, if there is a need to integrate a diagnostic tool for the benefit of a patient, the CEO of the company that owns the hospital might look at the cost-benefit analysis and decide against it. There is also the issue of training the doctor or nurses, who might not be open to it. There are competing needs at work, and successfully deploying new technology into healthcare requires a nuanced and intensive understanding of these workflows, and this can be enormously challenging.

That is not to say that healthcare is an essential industry, or that AI is not a noble thing to be brought into healthcare. However, firms selling AI-related services should be aware of these considerable hurdles to success. Unless a firm has a particular focus on healthcare technology, this might not be the best industry on which to expend much attention. 

I’m not a pessimist about AI in healthcare in the long term, I simply believe it’s among the most painful sectors to get budget and buy-in.

Most Small, Niche Markets: Companies in small markets can only grow so large, and so can only have so much research and development budget. A company selling RVs or tent rentals is simply never going to have the money that Citibank or HSBC do. Firms in relatively obscure niches are often vastly more likely to be followers of the trends of their larger, not-so-niched peers – and they’re much less likely to kick off ambitious projects themselves.

2 – Size

Companies in any industry under $1 billion in revenue might approve projects, but they are not going to be of the size and scope that would qualify it as a large, ambitious project. While it might be worthwhile to target these companies for smaller projects, they would not be of a caliber to qualify as the best enterprises for AI services firms looking for a lucrative deal.

Firms with well under $1 billion in revenue are often able to pay for executive education, and potentially some modest AI projects – mostly involving AI “tools and scripts”, not custom-built solutions.

There are some smaller venture-backed firms (fintech comes to mind) where AI talent, research budget, and digital savvy are all present. These firms will never be the biggest AI buyers, but they are able to adopt and move quickly. When I say “firms under $1 billion”, I mean traditional firms who are still wrestling with digital transformation, nevermind AI.

3 – Location

Outside the US and Western Europe: Large, ambitious AI projects are less likely to happen outside the US or Western Europe. China and Japan are exceptions, but for Western AI services companies (the majority of Emerj readers are Western), it’s often remarkably hard to sell into Chinese firms.

Southeast Asia, India, South America: Based on interviews with many venders and experience, this is particularly challenging to sell substantial AI projects to firms in India, South America, and Southeast Asia. Not only are the markets for AI often far too nascent, but the budgets for longer-term AI investments also don’t exist, and most vendors would waste their time fielding calls from these regions. 

 

Note: The guidance and instincts listed in the article above draws on Emerj’s one-to-one interviews with AI leaders from the most powerful organizations (the AI in Business podcast), and our AI Opportunity Landscape research across major industries. 

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