Build vs. Buy: The AI martech conundrum

30-second summary:

• All companies face the same dilemma around whether to build or buy a martech solution.
• The key factors to determine the best option include: the problem, budget, and timeline.
• Some of the risks of building your own software solutions boil down to opportunity cost, quality concerns, and technical debt, among others.
• Buy if it enables you to start generating revenue sooner. Build it if it enables you to start generating revenue sooner if you have the resources to do it successfully.
• Overall, working with an AI martech SaaS partner can be the best solution.

All companies face the same dilemma around whether to build or buy an AI martech solution. This is an important decision for businesses of all sizes due to resource constraints.

Making the wrong decision could have severe consequences to the long-term success—or even viability—of the business.

Here are the key factors to determine the best option:

The problem

The first step is to clearly define the problem you are attempting to solve. Is this a common problem, or a unique one facing your company specifically?

For example, developing ways to get smarter in acquiring new customers is a common problem, but most companies don’t currently leverage an AI intelligent machine to solve this problem.

The most common approach is to hire more user acquisition managers, consultants, and/or agencies, so more humans can analyze the data and optimize the campaigns. This can be an expensive, high risk proposition.

It’s always good to first look at how other companies are trying to solve the problem—are there any external third-party solutions you can leverage? If it’s a problem specific to your company, you may have trouble finding an existing workable solution.

Even if the problem is already well addressed, it’s possible your business needs fall closer to edge cases not encompassed by the products currently on the market, which could be an argument for the decision to build.

At most companies, building an AI martech solution isn’t a good option because they don’t have the dedicated resources available to build and support a complicated AI project of this magnitude. Most have a limited number of technical and data resources and need to focus on their own core products.

The budget

The next concern is budget. Do you have the necessary funds to see this project through to completion as well as extra resources in case you go over budget?

Most companies do not have a big budget to invest into building their in-house artificial intelligence (AI) capabilities. This is why it can often be easier to justify a monthly recurring payment or even an annual expense for a third-party SaaS product.

A good analogy to use is the decision to buy or rent a home. If you do not have the necessary funds to make a down payment on a house, then it becomes necessary to rent, even if the rental fee is equivalent to what the mortgage payment would be.

When deciding if you should build a solution to your problem, the budget must include the long-term technical debt (mortgage) associated with hosting and maintaining your solution, in addition to the up-front costs (down payment).

The timeline

The next consideration is the time horizon. Is your problem a threat to the survival of your company or just a nagging annoyance that could be improved? What is the impact to your company if this problem isn’t solved soon?

You must consider whether or not the problem will compromise the performance of the business. If you need a solution now, it can be an easy decision. Is there a solution in existence? If yes, buy it. If no, then, well… you’re going to have to build it as soon as possible.

There are risks awaiting you at every turn as you navigate this framework and ultimately make the final decision to build or buy. Let’s discuss some of these risks so you can make the most informed decision possible.

Risks of building an AI martech solution

The end goal in building an AI martech solution is to help your marketing team to make smarter data-driven decisions on the right optimization levers to pull to efficiently spend your budgets and resources to help accelerate growth. Some of the risks of building your own software solutions boil down to opportunity cost, quality concerns, and technical debt, among others. Here are the main ones to keep in mind:

Is marketing AI your core competency?

Most companies are not set up to have marketing AI as their core competency unless that is their main product focus. There are high costs to support AI—building teams of data scientists and machine learning engineers, building data infrastructures, and maintaining all of these resources.

The reality is that you need your internal resources to focus on developing and supporting the unique product capabilities that you offer.

Building an AI solution is a huge undertaking even with the right internal resources in place to support this project. Companies that build out of their circle of competence risk building inferior products compared to companies dedicated to solving the problem.

How often will it need to be updated?

The ability to dedicate resources to maintain and manage your AI project is very important because the machine learning needs oversight to ensure the right data is going in.

The algorithms need to be validated to confirm they are making the right decisions to help you acquire new customers cost effectively. This isn’t going to be something you build once and never touch (as if that ever happens). It’s going to need constant updating—further taking time away from your core product development.

Is it worth it? This is generally the big challenge with trying to prioritize in-house resources to maintain an AI project that isn’t the top priority for the business and getting in-house technical resources excited about maintaining it.

What is the opportunity cost?

The trade-off in any company is the opportunity cost of resources being deployed to support Project A compared to Project B while considering the timeframe of either project being deployed.

An example would be the costs in time and money of employees (data scientists, engineers, quality assurance, etc.) building and maintaining an AI project versus leveraging those resources to work on something else like improving your core product user experience (which is most likely the reason they joined your company).

Your decision to build may be to the detriment of other projects that will likely hurt morale and postpone any major technological breakthroughs with lost productivity.

Another cost to factor in is any delays in the deployment of an AI solution (including the necessary machine learning training) that would result in your marketing team not spending their budget as efficiently as possible.

Taking time to think considerably about how the pricing structure of an off-the-shelf solution compares to a custom solution when considering organizational growth will allow the most effective, responsible, and successful decision making.

Technical debt

This is a common concept in programming that reflects the extra development work that arises when code that is easy to implement in the short run is used instead of applying the best overall solution. Technical debt can be taken on intentionally when a quick fix is not the ideal solution but necessary

given the timeline and budget. Other times technical debt is the result of poor planning and architecture. The long-term costs associated with building and maintaining an AI solution internally can lead to expensive issues down the road with quality, performance, lost time, and money.

This is bad because technical debt is one of the largest and most impactful issues affecting software development today with companies under pressure to deliver projects on time.

No economies of scale

Are you disadvantaged when it comes to sourcing tools that contribute to the AI build?

Unanticipated expenses such as server fees and monthly database charges as well as hiring talent like data scientists and engineers could be a huge risk of building.

Companies that service many customers are able to distribute the costs of software operations and maintenance evenly across their clients. These economies of scale can allow them to charge less for a product or service than you would be able to achieve by building it yourself.

If a third party’s economies of scale and other factors put your build at a disadvantage you may strongly consider the option to buy, but not before evaluating the risks associated with buying. It’s important to look at the long-term ROI on this project that factors in the economies of scale.

Risks of buying an AI martech solution

Most AI solution partners will offer a free-trial or proof-of-concept (POC) period to give you the ability to evaluate their capabilities with your data.

Before moving forward with a trial, demo, or quote, review some of the surface-level risks of buying a software solution versus building one yourself (you need to do a thorough job on the due diligence process to mitigate these risks).

Weighing it all out

The overall goal is to minimize cost now and cost later. Therefore, the deciding factor is delivering something of value that your marketing team can leverage to start generating revenue from it.

Buy if it enables you to start generating revenue sooner. Build it if it enables you to start generating revenue sooner if you have the resources to do it successfully.

Overall, working with an AI martech SaaS solution can be the best solution for most companies to get farther faster.

Lomit Patel is the Vice President of Growth at IMVU. Prior to IMVU, Lomit managed growth at early-stage startups including Roku (IPO), TrustedID (acquired by Equifax), Texture (acquired. by Apple) and EarthLink. Lomit is a public speaker, author, advisor, and recognized as a Mobile Hero by Liftoff. Lomit’s new book Lean AI, which is part of Eric Ries’ best-selling “The Lean Startup” series, is now available at Amazon.

source https://www.clickz.com/build-vs-buy-the-ai-martech-conundrum/261821/