Go-to-Market Strategy for an AI Product – What to Discover (Part 1 of 3)

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

Go-to-Market Strategy for an AI Product - What to Discover (Part 1 of 3)

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.

But a lot of the time it’s a go-to-market strategy. Whether it’s large enterprises or it’s companies that have raised $20 million or $100 million, they’ve got markets to attack and decisions to make to go out and win market share.

Going to market with AI products comes along with some unique considerations. We do a lot of this work with companies, and I’m forced to think about these questions all the time as we design surveys to reach executives and poll them for these kinds of projects.

So I decided I should do a three-part series about go-to-market strategy specifically for AI-related products. We’re going to talk about three themes.

This entire article, the first in the series, is going to be about what we need to discover. What kind of insights do we need? What are we ultimately looking for?

The second is going to be about how to find those insights. What does secondary research or primary research actually look like in terms of reaching executives and getting them to answer questions; in terms of finding the right kinds of insights that are already out there and available in the market so that you can make those smarter decisions and win that market share faster?

Part three is going to be about turning those insights into action. Once you’ve derived the core insights from a go-to-market strategy research project, what does it look like to take action on them? How should one transfer that hard work into something that includes winning more market share?

We’ll begin with the big questions. Ultimately, a go-to-market strategy project needs to answer three core questions:

  1. Is the market big enough?
  2. What will the market be in the future?
  3. Who spends the money here, and why?

Once one knows what they need to answer in terms of big questions, it’s worth noting that there are some unique considerations for artificial intelligence when it comes to go-to-market strategy. There are also some decisions that one can easily botch. We see AI companies pivot after raising far too much money because they didn’t understand the customer and didn’t understand the demand. That’s a shame when you’ve already burned 28 million of investors’ dollars.

Unique Considerations for a Go-to-Market Strategy for AI

We’ll talk first about the unique considerations for a go-to-market strategy for AI:

What is the Buyer’s Understanding of Artificial Intelligence?

I think about this as a continuum. In terms of a buyer’s understanding of AI on the far left, we might think about a kind of buyer who still Googling things like what is AI? These are folks that really don’t necessarily understand how machine learning functions, what this stuff is, what kinds of capabilities are available. On the far right, we have the kind of buyer who not only has a conceptual grasp of data, of artificial intelligence, but also has previous experience implementing this within their business.

Where the buyer lands on that continuum will often drastically alter the kinds of marketing messaging that we send to them if we want to win market share. It’s also going to drastically alter the amount of effort it’s going to take to convince them that we are a worthwhile solution and often the amount of handholding it’s going to take once we do sell them a product. This is one unique consideration for AI and something that one really can’t leave out.

What Level of AI Does the Buyer Company have In-house?

Again, I think about this on a continuum. There are firms that have a pretty solid conceptual grasp of AI where the leadership sort of get the big picture. They can call out less-than-truthful marketing. They understand roughly what machine learning can and can’t do and where it should be applied, but they don’t actually have any in-house talent.

There are also companies with absolutely no talent, where if a vendor sold an AI product to them, the vendor would have to be the talent. It’s a hands-on sale that often involves helping them with staffing needs for the vendor’s solution. It’s really laborious, really time-consuming,  involves dealing with companies that frankly the vendor would prefer not to.

One can’t build or integrate an AI product with just a batch of people with PhDs from Carnegie Mellon who sit in an office all alone and have for the last six months without interacting with subject-matter experts.

If you’re working with a company that has that kind of talent bench, your process of selling is often going to have to involve convincing them. Your process of integrating your technology might be made easier because they exist or there might be new objections that come up because they exist. So if you’re going to go to market, you have to understand what the talent looks like at the buyer company. How much exists there now? Where do they exist on that continuum?

What is the Data and IT Environment like at the Buyer Company?

An eCommerce client is going to have a completely different data and IT environment from a large manufacturing company, for example. They differ on where their data is stored and how their ML models might be trained. They’ll differ on how data is treated and if it’s accessible. If you’re working with heavy machinery, for example, there are certain kinds of coding languages and certain kinds of protocols that you’re probably going to have to get used to and your product is going to have to work with.

Ultimately, the majority of a go-to-market strategy with artificial intelligence is integrating with existing workflows. Can you find a way to plug into the enterprise? Sure. You might want to revolutionize the way they collect data and revolutionize the IT structures that you have to work within. But the fact of the matter is that’s not going to happen overnight. Vendors that get traction in big existing sectors like banking, insurance, pharmaceuticals, have to figure out how things are done currently at the buyer companies.

In terms of a continuum, the buyer company might have old legacy systems with almost no regard for data, its use, its structure, it’s accessibility. On the other end of the spectrum, the buyer company might understand the value of data. They’ve worked hard to upgrade their IT structures so that data is accessible and so they can access APIs and maybe integrate new technologies.

Mistakes to Avoid When Planning a Go-to-Market Strategy for AI

There are decisions that can be flubbed if one doesn’t have some solid grounding in the markets that they’re entering. If one doesn’t get clarity on the market, what are the consequences?

We get to see a lot of this. Sometimes the people who pay us to do this work didn’t do it the first time around, and oftentimes we’ll see companies that one year was advertising a totally different product than they were three months later. Sometimes it’s for reasons that are outside of their control; sometimes it’s because they didn’t get a good enough grounding of what they were walking into in the first place.

Determining What Markets to Enter and What Problems to Address

The first decision that can get botched is that sometimes a company selling into a sector will have a founder who has experience in the market, and they go in with a healthy assumption of a real problem in the market. As it turns out, however, that one founder worked in a company that was a little bit different. They used technology differently.

They made decisions differently. Or maybe as it turns out, most people in that sector actually don’t think about the problem in that way or consider the technology the vendor company wants to update with AI to be an issue, even if maybe it really is. The founder might assume that there’s a need in the market for their solution, but the fact of the matter is that no money is being spent to change this and that most people operating that legacy system frankly don’t care to change because they have bigger problems that they’re addressing right now. The vendor company might learn that they’ve hired the wrong people or attacked the wrote problem.

This doesn’t mean the company is going to fail, but it does mean that we’re getting off to the wrong start. We’re pointing in the wrong direction altogether. So the biggest opportunity to mess up a decision is deciding to address a problem that others don’t see as a problem. Whether you’re a startup or whether you’re an enterprise overhauling your existing offerings, you don’t want to mess this up. This is where the most money is wasted.

Determining Who to Target for Sales and How to Reach Them

The second question that you’ll be able to gain clarity on through a proper go-to-market strategy is determining who to target for your sales and how to reach them. Understanding how the stakeholders buy will give you an opportunity to staff up on the right kind of talent. If you are selling into the healthcare space and your first conversations are going to be with doctors, that’s a very different sales team than you need to sell to the CEOs of hospitals, for example.

One of the companies that we did a recent go-to-market strategy project with sells into hospitals. They ended up having to deal with executives first but doctors were the actual stakeholders for their particular product. But if they staffed up just to reach doctors and later found out the hard way that ultimately their first conversations needed to be with the C-suite, the vendor’s sales staff would have been woefully under-prepared for the kind of sale they’re making and woefully under-prepared to speak the language of their target stakeholders.

The language of an executive is return on investment. The language of a doctor has to do with patient outcomes. These warrant very different conversations.

Determining Your Landing Pages and Sales Collateral

This is arguably the easiest mistake to fix, but it’s still obnoxious and doesn’t necessarily need to happen: that’s messing up one’s landing pages and sales collateral. If one is going out into the marketplace because they presume buyers purchase for one reason or the stakeholders are one type of person, they might build landing pages designed to reach those people. They might build white papers and PDFs and outbound marketing campaigns designed for that value proposition. The redesigning of the homepage and the redesigning of PDF collateral is actually not that big of a deal.

Here’s where this does become a big deal. Let’s say you sell something in eCommerce and your core value proposition all has to do with targeting an audience of one. But your salespeople might actually learn from buyers the hard way (because you didn’t do the research) that ultimately “audience of one” is a buzzword and it by itself doesn’t move the needle. It’s actually all about improving cart value.

If that turns out to be the case, you’ve all of a sudden marketed yourself as the audience of one company. You’ve spent lots of money for the people who you could potentially sell to see you on social media, for example, and now they associate your brand with audience of one when, in fact, you want them to think of you for something else.

If you become known for something that you then have to change drastically, often that comes along with a costly rebrand.

So often people will ask what the deliverables are for a go-to-market strategy project. Ultimately, it’s going to vary client to client, but almost all go-to-market research is going to inform the following:

  1. What sectors or subsectors have the money, and the need to buy what we might offer to them?
  2. What are the accessible stakeholders who control the budget? Who are these people? Where do we find them?
  3. What do we have to say to them to hook their attention and close deals?

Go-to-market strategy research has to nail those three issues. If you can nail those three issues, you can spend money with confidence.

Next week we’re going to talk about developing this actual strategy by learning the insights. How do you go into the world and actually learn the things that I talked about in this article? How do you decide who the stakeholders are? How do you decide what the key issues are that are going to get them to pull the trigger?

Read part 2 of this 3-part series here.

 

Header Image Credit: The Irish Times

Stay Ahead of the AI Curve

Discover the critical AI trends and applications that separate winners from losers in the future of business.

Sign up for the ‘AI Advantage’ newsletter:

Subscribe
subscribe-image
Stay Ahead of the Machine Learning Curve

At Emerj, we have the largest audience of AI-focused business readers online - join other industry leaders and receive our latest AI research, trends analysis, and interviews sent to your inbox weekly.

Thanks for subscribing to the Emerj "AI Advantage" newsletter, check your email inbox for confirmation.