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Over the past couple of years, we’ve seen nearly every tech giant — including Amazon, Salesforce, Oracle, Microsoft, and Google — either announce their own AI initiative or acquire AI startups that fit their product development roadmap. These new tools aim to let companies automate parts of their operations using various applications of AI.
AI is the enterprise tech opportunity of the moment, and there’s a whole new crop of AI-driven software products emerging as a result. They are transforming companies in nearly every area, from customer service to marketing, security, and beyond.
At this point, most companies are thinking about how to invest in AI. But with all the noise, how do you know which products will provide actual value and how to get the best deal on them?
There are three types of companies in the AI business software space, each with their own needs and inherent risks. Understanding what motivates them enables you to cut through the AI hype and leverage your insight during negotiations.
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Seeing the opportunity in AI, large enterprise companies started working on AI-based offerings to add to their core suite of products. These companies are frequently marketing well in advance of having a market-ready product. Once they do release it, the AI offering has to be generic enough to work for tens of thousands of their customers out of the box. This makes perfect sense because adding an “AI-capability line Item” becomes a fantastic upsell opportunity to an already solid customer base.
If you’re interested in their core products, you can use this knowledge to your advantage. These companies may be willing to give you a deep discount for the first one or two years of licensing in order to get your company onboarded with their AI solution. Further, these companies face pressure to deliver on their AI promise; any success story they can demonstrate will quickly justify the investments they’ve made to their shareholders and respective boards, so they’re motivated to help you.
1. It’s important for you to show value and positive ROI on the investment your business has already made in their core, non-AI offerings.
2. Volunteer to be one of their “success stories” once the AI is in place and delivers some value.
3. Conduct your research about competing vendors offering similar core functionality and slightly more advanced or mature AI features. This way you can keep your options open and negotiations fair.
Like the larger companies in the space, early-stage companies, which often use a “dot-ai” startup name (“cool-name.ai”), usually don’t yet have a market-ready product. Rather, they are selling you on a concept or an alpha version of some vision they have for using AI in a certain business function.
Because of their early stage, these companies are often willing to provide hours of free consultative work in the hopes of winning your business — a great way for you to educate yourself about the emerging AI space. They are also often willing to do custom work for you, likely packaged at no additional cost into their “pilot fee.”
When negotiating with these startups, know that you can request these perks. As one of their first customers, you will also be able to heavily influence the product’s direction.
While these startups offer an abundance of benefits, they are also the riskiest vendors to work with. AI is still a new technology, and working with an unproven company means you could be wasting a whole lot of time and resources on the wrong team. These companies will not be able to provide a full suite of services and support, and they could even go out of business or get acquired in a year — right when your team has started depending on the technology for a core part of their job.
1. As an early customer, you will be able to negotiate hard on price, and could even get a free pilot deployment or proof of concept.
2. Do your research on the startup before jumping in — find out who the founders are, how deep their team’s AI-research capability is, how many customers they already have, who their investors are, when their last funding round was, etc.
3. Finally, be prepared to adapt quickly in case the startup goes out of business, gets snapped up by a larger competitor, or pivots their product in a completely different direction.
Note: There are, of course, some notable outliers that have a “.ai” domain name but have demonstrated a significant product-market fit and already deliver value to thousands of customers.
Narrow-focused AI companies
Somewhere between the large companies and the early stage startups are narrow-focused AI companies. These companies hit a sweet spot because their expertise and core competency is in making AI practical in a specific vertical or business function (e.g., sales, marketing, or customer service). They are not quite as risky as the early stage startups because they have already proven their product in the market and can demonstrate referenceable results, and they are far more focused than the big players.
These companies concentrate on solving one specific problem with AI, whether it’s taking the grunt work out of customer service or creating better marketing campaigns. This is where AI is most useful today, since it is best at doing specific tasks, rather than replicating complex human intelligence. Narrow-focused AI companies that have been in business for a few years are now at the point where they can concretely demonstrate their value — something that neither early-stage “dot-ai” startups nor big players cannot do.
While these companies already have successful case studies, the space is still new, with only a few established market leaders, each one fighting to come out on top. They likely will give you a better deal if you agree to do a public case study, share your story, or add some other non-monetary value to their company.
1. Come prepared with a good understanding of what the company actually does (i.e., don’t ask a customer service-focused AI company to run your marketing campaigns).
2. Listen to the success stories and the metrics they’ve been able to achieve with other customers before making your buying decision.
3. Bring non-monetary value (case studies, PR, references) to the table in order to get a better deal and build a long-term partnership.
Choosing between these three types of AI vendors is a matter of your specific business objectives and the amount of risk you’re willing to take on. Regardless of which one you choose, understanding that your vendor’s needs and lifecycle stage will ultimately help you negotiate the best deal for your team.
Mikhail Naumov is cofounder and president of DigitalGenius, a venture-backed artificial intelligence company that aims to transform the customer service industry.
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