Sharks with laser beams on their heads :dr_evil_face:
Just because it’s possible (and kinda sounds cool) doesn’t mean you should do it. That was true when the Internet of Things was mainstream (looking at you connected toaster oven), when blockchain was mainstream (ICO scam after ICO scam), and is true now with AI.
There are lots of ways in which AI can help solve current problems and make us more efficient - tools like Grammarly or Copilot for writing come to mind. But let’s also think critically about when, where, and how we’re implementing it in our products so we don’t waste everyone’s time and money.
Or if we’re going to waste time and money. Let’s make sure we have a lot of it and are having fun doing it (have you seen some of these AI movie trailers?!).
Trying to figure out if your project is sharks with laser beams, or actually useful? We have a list of five questions to ask before diving in.
1. Is there an existing AI product that suits your needs?
One of the most challenging parts of implementing AI is if you’re trying to develop something completely new. As it stands right now, there are a handful of things that AI tends to do really well – generating text, summarizing content, etc.
If an existing AI product already does what you’re looking for, the build is way less complicated. There's still the process of integrating the AI tool with your product. But, it’s fairly straightforward.
That said, if you’re trying for something totally new, things get a lot more complicated. There are lots of metaphorical ”how do we attach the lasers to the sharks?” type questions. You can always try the duct tape route. But don’t be surprised if your evil lair catches some stray laser beam shots along the way.
2. What’s your budget?
If you’re planning on investing in AI it’s best you have a healthy budget (struggling very hard not to make another Austin Powers reference…) Even in the best-case-scenario where you can connect to an existing LLM (this stands for large language model and is essentially the AI’s “brain”) there are upfront costs like connecting it to your system and designing then building a user interface. Not to mention the ongoing costs like paying for tokens to access an existing LLM, and recurring maintenance.
Depending on your project, it's possible you’ll have to augment or train an existing LLM extensively, which takes a lot of time and resources. Or, in the most extreme cases, it’s possible you’d need to build your own LLM. Which would likely cost in the millions, on the low end. For reference, OpenAI reportedly spent over $100 million developing their LLM. How many AI movie trailers could that have been?
Understanding your own goals with AI will inform the budget. If you’re truly trying to create a new AI application, go all in with a multi-million dollar plan. If you’re feeling pressure from a board or senior leaders to make progress with AI, start to explore what an LLM integration might look like and share the associated cost with leadership.
3. Do you have someone on staff, or retainer, with technical ability?
AI is evolving at a very fast pace. That’s exciting for potential new capabilities, but it also means that frequent updates and changes to AI products will likely be needed. If you don’t have someone on staff, or retainer, to handle those changes your AI investment may never have the chance to succeed.
That type of shepherding isn’t just limited to the evolving nature of AI, it’s also crucial to monitor the quality and output of the AI’s work internally. It’s kind of like having a five-year-old make you breakfast. Everything may look right, but one bite in you find out peanut butter and glitter isn’t a stellar pairing.
Similarly, AI might generate good looking code, but it could be buggy or downright wrong. So, it’s crucial that someone checks the work. Otherwise, you might be in for a rough time.
4. Is there a non-AI solution that could be better suited for your use case?
There are lots of things that AI can do, but that doesn’t mean it’s always the best or most cost-effective option. Recently, a real estate company asked for our help creating a tool that would use AI to alter existing property photos to show improved landscaping, lighting, etc. We declined.
It wasn’t a bad idea. And AI is fairly good at generating images. But it’s not good at determining what additions to an image would make it “better.” It’s always important to remember, AI can’t actually think. It may appear to think – a la, walking Weekend at Bernie’s style – but what it’s actually doing is making highly educated guesses.
In the end, it was actually more cost effective for the potential client to outsource the editing work instead of trying to build the tool. AI isn’t a magic wand and it’s important to remember just because it’s a solution, doesn’t mean it’s the only solution.
5. Will AI make your offering better, or just different?
AI is an investment that takes time and resources. It’s a business decision. And just like any other investment, you should have a clear understanding of the benefits and risks it brings.
It’s easy to get caught up in the hype and trying to keep up with the virtual Joneses, but if AI won’t bring real, measurable value it’s not worth the time. For now, we see a lot of products simply being made different by AI, not actually better or more efficient.
Map out expectations for your AI investment, and make sure it’s one worth making.
Looking to the future
In some form, AI will probably be around for years to come. But, it’s still an evolving technology and one that isn’t right for everyone right now. Be honest with yourself about why you want to add AI into your business and know what you want to accomplish with it. Is it to look cool, or actually do something useful? If you don’t have a clear understanding, it may be worth exploring other options and setting a future date to revisit the AI conversation.
If you want a fresh set of eyes to help answer these questions, or you’re ready for the sharks to meet the lasers, feel free to get in touch with us here. We’re happy to help.
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