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Don't Buy AI Products Off the Shelf—Build Your Own

Controversial opinion: Instead of buying something flashy off the shelf, unlock the potential of AI by developing custom solutions tailored to your organization's needs.

Building custom AI tools utilizing your company's data unlocks valuable insights, automates processes, and provides a competitive advantage, increasing operational efficiency and personalization at scale.

Off-the-shelf AI products may lack customization and pose risks to data security, limiting their effectiveness and exposing organizations to potential privacy breaches and reputation damage.

Developing custom AI solutions by integrating existing APIs and working with fractional teams of AI experts enables tailored applications, mitigates risks, and harnesses the full potential of AI technology for innovation from within.

Join us on June 15th for our Generative AI Salon — a monthly gathering of top AI builders.

If you’re a business leader with a pulse and a LinkedIn account right now, chances are you’re losing sleep worrying about whether your team is using AI to its full potential.

Most people’s instinct is to go out and sign up for one of the many fairly affordable AI products out there and call it a day—Writer, Lavender, Jasper, ChatGPT+, the list goes on. There’s a decent chance you’ve done that already. And don’t get me wrong—these tools are awesome. But innovation leaders who want to truly capitalize on the power of AI should take things one step further—they should build their own tools.

This may sound overly ambitious at first blush. But the truth is that incredibly powerful, custom AI applications can be built in just a weekend on top of existing APIs—as A.Team recently showed at its Generative AI Hackathon.

These custom applications are especially attractive for enterprise customers. Why? Because they allow you to leverage the biggest advantage a company has when building a generative AI product: their treasure trove of data. But even a small startup has structured data of its own.

Building an AI tool can increase the value of this data exponentially by unlocking insights and automating processes that were previously time-consuming or impossible. By creating a custom AI tool, companies can integrate AI technology directly into their operations, tailoring to their specific needs, and extracting the most value from their unique data sets.

A custom AI tool not only increases operational efficiency but also creates a competitive edge. It's one thing to utilize a tool that everyone else is also using; it's another to have a unique tool that's designed to address your specific business challenges.

But building your own AI tool doesn't mean you have to start from scratch. You can leverage existing AI technologies and platforms to develop a solution that best fits your needs. The building blocks of AI—machine learning algorithms, natural language processing capabilities, predictive analytics—are all out there, ready to be utilized.

There are two ways to leverage AI: Buy something off the shelf or build your own tool

Consider these two examples. Lemlist, a cold outreach automation tool, recently introduced their OpenAI integration, which generates an "icebreaker" line for campaigns based on a LinkedIn profile.

Here's a sample output from Lemlist, generated using my personal LinkedIn profile, targeting my venture studio, Salfati Group, as an example:

While this Lemlist output does a decent job, it could go further.

My take is that prospecting is the art of building a robust network, which means personalization should go beyond a single sentence. So we decided to build our own solution capable of creating a completely personalized sequence for reaching out to prospects. Instead of generating a single sentence, our custom AI solution crafts an entire campaign with multiple LinkedIn and email messages tailored to each prospect.

Take a look at this example: 

Notice the unique attention to creating a personal connection. “Especially in gaining insights into the molecular pathogenesis of gastrointestinal cancers.” This email is only one out of a 12 touch points sequence generated by our in-house solution.

By building our own AI tool, we gained several advantages: significantly faster generation times, greater specificity, and the ability to leverage more data resources. This highlights the benefit of building your own solutions—unlocking the full potential of AI and hyper-personalization at scale.

Why settle for off-the-shelf products when you can create an AI tool that truly caters to your organization's needs?

The pros and cons of buying vs building

While off-the-shelf AI products can offer immediate functionality, they often suffer from a critical drawback: They are not custom-built to address the unique needs of individual organizations.

This lack of customization can limit the effectiveness and applicability of these AI solutions, hindering their potential impact on business operations.

On top of that, relying on a third-party for critical business functions may expose organizations to potential risks. The importance of data ownership and security cannot be overstated, especially for enterprise companies dealing with sensitive information. By relying on third-parties, organizations may inadvertently expose themselves to data leaks and privacy breaches, resulting in devastating consequences for their reputation and customer trust.

In contrast, leveraging the OpenAI API directly and developing custom AI solutions ensures better control over data protection and security measures, reducing the risk of such incidents.

How to build a custom AI application

Building a custom AI solution doesn't necessarily require creating an entirely new AI system from scratch. Businesses can still leverage existing APIs, just as off-the-shelf products do, but with the added benefit of tailoring the AI application to their specific needs.

My company built a custom AI solution by integrating a CRM platform with an AI-powered lead generation tool. If you’re serious about building your own AI application, you can do it yourself or you can bring in a fractional team with AI expertise—a group of specialists who work part-time or on a project basis to provide AI knowledge and support.

It doesn’t have to be crazy expensive.

Fractional teams can help businesses identify opportunities for AI integration and automation within their organization, ensuring that AI solutions are designed to address the company's unique challenges and goals.

And it doesn’t have to be crazy expensive. For the most basic tools all you need is an API key and one engineer logging a few hours a week. More advanced tools will of course require larger teams. ClearCo, a platform for founders to access advice and funding (and an A.Team client), brought in a software architect to integrate GPT-3 to automate coaching enablement tech in order to scale their insights across hundreds of founders.

While the convenience of off-the-shelf AI products built on the OpenAI API may be appealing, businesses should weigh the potential drawbacks and risks associated with these solutions. By developing custom AI solutions tailored to their specific needs, organizations can unlock the true potential of AI technology and drive innovation from within.

Elon Salfati is an expert builder in A.Team's AI Guild. He's also the founder of Salfati Group, an ecosystem of companies dedicated to empowering entrepreneurs on their journey towards product-market fit.

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