Lomit Patel, Chief Growth Officer at Tynker, provides an overview of the pros and cons when building or buying an AI Marketing Technology (martech) 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 severely affect the business's long-term success—or even viability.
Here are the critical factors in determining the best option:
The first step is clearly defining 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 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, your business needs may fall closer to edge cases not encompassed by the products currently on the market, which could be an argument for the decision to build.
Building an AI martech solution isn't a good option for most companies because they don't have the dedicated resources to develop 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 core products.
The next concern is budget. Do you have the necessary funds to see this project through to completion and extra resources in case you go over budget?
Most companies do not have a big budget to build 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 funds to make a down payment on a house, it becomes necessary to rent, even if the rental fee is equivalent to the mortgage payment.
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 and the up-front costs (down payment).
The next consideration is the time horizon. Is your problem threatening your company's survival or a nagging annoyance that could be improved? What is the impact on 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 not, then, well… you'll have to build it as soon as possible.
Risks await you as you navigate this framework and decide to build or buy. Let's discuss some of these risks so you can make the most informed decision possible.
Building an AI martech solution aims to help your marketing team make smarter data-driven decisions on the right optimization levers to pull to spend your budgets and resources to help accelerate growth efficiently.
Some risks of building your software solutions boil down to opportunity cost, quality concerns, and technical debt, among others. Here are the main ones to keep in mind:
Most companies do not 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 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 enormous, even with the right internal resources to support this project. Companies that build-out of their circle of competence risk making inferior products compared to companies dedicated to solving the problem.
The ability to dedicate resources to maintain and manage your AI project is essential because machine learning needs oversight to ensure the correct data is going in.
The algorithms must 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 will need constant updating—further taking time away from your core product development.
Is it worth it? This is generally the big challenge with prioritizing 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.
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 deploying an AI solution (including the necessary machine learning training) that would result in your marketing team not spending their budget as efficiently as possible.
When considering organizational growth, taking time to consider how an off-the-shelf solution's pricing structure compares to a custom solution will allow the most effective, responsible, and successful decision-making.
This common concept in programming 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 is 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 most significant and impactful issues affecting software development today, with companies under pressure to deliver projects on time.
Are you disadvantaged regarding sourcing tools that contribute to the AI build?
Unanticipated expenses such as server fees, monthly database charges, and hiring talents like data scientists and engineers could be a considerable risk to the building.
Companies that service many customers can distribute the costs of software operations and maintenance evenly across their clients. These economies of scale allow them to charge less for a product or service than you can achieve by building it yourself.
Suppose a third party's economies of scale and other factors put your build at a disadvantage. In that case, you may strongly consider the option to buy, but not before evaluating the risks associated with buying. It's essential to look at the long-term ROI on this project that factors in the economies of scale.
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).
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 allows 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 further faster.
Lomit Patel is the Chief Growth Officer of Tynker, with 20 years of experience helping startups grow into successful businesses. Lomit has previously played a critical role in scaling growth at startups, including Roku (IPO), TrustedID (acquired by Equifax), Texture (acquired. by Apple), and IMVU (#2 top-grossing gaming app). Lomit is a public speaker, author, and advisor, with numerous accolades and awards throughout his career, including being recognized as a Mobile Hero by Liftoff. Lomit's book Lean AI is part of Eric Ries' best-selling "The Lean Startup" series.
This article was first published here.