90% Of Executives I Talked To At An AI Conference Faced The Exact Same Problem.

I was at the Databricks Data + AI Summit in San Francisco last week, and over the course of the conference, I was able to talk to about 20 or so different executives from a variety of different industries. They ranged from VPs all the way to a Fortune 500 C-Suite exec.

Of course, we talked about AI. Why wouldn’t we? This was an AI conference after all. Jensen Huang, the CEO of NVIDIA, was the keynote speaker.

And of course, all 20 executives I talked to were excited about AI. But most of them were also a bit frustrated, as I would soon find out from talking to them.

90% of the executives I talked to had the same exact problem: Their team had come up with a grand strategy to implement AI and LLMs into their company workflows, for a variety of different tasks.?

Everything from applying machine learning into factory processes, to automating marketing campaigns on social media, to even using AI for partnerships marketing with a major European football (soccer for us Americans) team.

The ideas were great, no doubt. The power of LLMs and AI still remains very uncharted territory, and there was a lot of amazing potential within these plans.

These executives and their companies were all excited about the potential of AI to save them time and money. They had invested heavily and were eager to see results.?

Only…

Their AI projects had stalled. They were months, sometimes up to a year behind. They were well over budget, sometimes to the tune of millions of dollars.

So what was the common theme amongst nearly all these projects??

Well, software development for internal tools is a complex process. A team of executives, UX designers, administrators, and product managers have to ideate and design the tool that they need.

And most enterprises aren’t AI companies. They are manufacturing companies. Or airlines. Defense contractors. Fast food chains. Steel producers. Law firms.

For this reason, instead of spending time and money to build AI workflow tools internally, most of these companies choose to contract development out to third party consultants, often in foreign countries.

While this approach seemed cost-effective initially, it led to a host of unforeseen challenges:

1. Lack of understanding: Most technical consultants fail to fully grasp the nuances of each company's unique needs and workflows.?
2. Lack of AI expertise: Despite claiming AI proficiency, many of these consulting firms were still in the early stages of understanding and implementing AI technologies themselves. They were learning on the job, at the client's expense. If they didn’t know how to do it, they would say yes anyway, and learn it later.
3. Misaligned incentives: Some consultants prioritized billable hours over delivering efficient, practical solutions. This led to bloated projects that dragged on far longer than necessary. This is common in the consulting world, and we see it time and time again, across many industries.
4. Integration issues: The AI solutions developed often failed to integrate seamlessly with existing systems and processes, causing further delays and frustrations. This problem could be solved if the consultants took the time to understand the company’s tech stack, which they usually didn’t.
5. Security concerns: Outsourcing sensitive data and processes to foreign entities raised significant security and compliance risks for many companies, and the solutions delivered would often lack proper security protocols, or would be patched with poor quality code.

As a result, what started as promising AI initiatives turned into costly, time-consuming nightmares for many of these executives. Their enthusiasm for AI's potential was dampened by the reality of failed implementations and mounting costs.

But thankfully, you won’t make the same mistake. Because you’re reading this article now, and you understand why outsourcing your AI development to teams that won’t take the time to understand your company’s needs is a huge mistake.

Artificial Intelligence is not your run of the mill software project. Unlike most internal tools, a proper AI automated workflow can completely transform a company from the inside out. For this reason, my recommendation is always to take the time and do it right.

Our case study here should give you an introduction to LLMs and what they can be used for within your company. It’s the first step to eliminating over 90% of your company’s data entry work within the next few months.

And if you’d like to build something for your company even faster, maybe in just weeks, then consider leaving us a note. We’d love to help.

You'll have a personalized, custom built demo in under a week.