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How
to Approach Building AI Software?
Firstly, Pinpoint Use Cases of AI In Your Product
Answer these three questions about AI implementation in your
app honestly:
- Is it intended to be the product itself (think of cases
like OpenAI or Anthropic)?
If the answer is “yes,” do a thorough competitor
analysis. Chances are, your idea already falls into the “commodity”
category and won’t sell as a standalone solution. Or it will cost a fortune to
develop (Microsoft funnelled $10 billion to OpenAI in 2023 alone).
Unless you have access to huge funding and unique, valuable
data specific to your business domain, you should leave building solutions
where AI itself is the product to the big players. The current (as of May
2024) NVIDIA stock
prices are not a coincidence, let’s just say.
- Is it meant to be a fun gimmick,
like smart autocomplete in an email client, or “what to read next”
recommendations in an e-bookstore?
I encourage you to critically look at your idea to discern if
it doesn’t fall into the “gimmick” category. Your main goal should
always be to build a great product. Gimmicky AI features won’t make a
huge difference if the core idea is flawed. After all, a bad email client won’t
beat Gmail, even with the help of magical AI, will it?
- Does it solve a specific
business problem (e.g., calculating the amount of fertilizer needed in
farming depending on various factors)?
You have the problem, and you have the assumed solution. Or
do you? If you only want to gather the data and “you’ll find out once you get
there” — beware, it’s a trap. Know your scenarios; Are the problems you
want to solve the same ones your target user base has? If not, going
back to the drawing board will likely save you a ton of money. Our AI market research tool is a great way to start market
research for free, helping you pinpoint real issues before diving into
solutions.
Secondly, Gauge Your
Ability to Build an AI System
Assess your competencies in the AI field:
- Are you a data scientist or data engineer
yourself?
AI expertise can help tremendously but may also give a false
sense of advantage. Even if you’re familiar with the AI development process,
consider what you may not yet know about building software solutions
themselves.
- Are you aware and knowledgeable about the tools
used to develop AI solutions?
Again, without knowing the tools, you need someone who is
familiar with them. On the other hand, your familiar toolset might not be
perfect for the specific use case. For example, being an LLM expert won’t be of
much help when developing computer vision solutions.
- What’s your budget?
This might be a very limiting factor in terms of what is
possible to achieve. On the flip side, it can help in optimizing your AI
software development project to prioritize the most important things (you
should hire a solid Product Manager, by the way).
- Is your business already
generating revenue?
In AI research and development, budget underestimation is
very likely, especially when you’re not doing it in logical order — data first,
model later. Having a stable income as you move forward in development would
make it much more seamless.
- What’s your experience in
developing digital products?
As you may guess, it’s a double-edged sword. Obviously, it
seems like another item that sets you ahead among others, but for instance,
adopting a process from a huge company without being aware of the differences
between you and them may become a huge detriment to the entire undertaking.
If you and your idea both survived this evaluation, we can
think about what to do next.
What’s Required To
Build AI Software?
Here we have two possible directions to take: either develop
AI software from scratch or use existing AI platforms and products. Developing
your own model is tempting, especially since proprietary technology is highly
regarded by investors. On the other hand, coming back to what we’ve stated
previously, you want to solve a specific business problem. To choose the
optimal direction, it’s worth considering the hard-to-meet requirements you
might face:
- The AI software market evolves
dynamically, with huge amounts of funds currently invested in creating AI
technologies. Potentially, something that you’ve spent a year working on
could be replaced overnight by one announcement from a big player. You
need to take into account that developing your own AI from scratch takes
significantly more time than traditional AI-based software development,
which is understandable since it’s R&D and not just D.
- In reference to the above, time
is money. And money is needed to pay the people who specialize in AI
projects, who at the moment are not complaining about job opportunities
(to say the least), and the risk of being outbid by your competitors is
significant. Especially if you’re still in the early phase and do not have
something that’s already proven to work on the market. An employee leaving
your company at this stage will cost you double.
- People and competencies: Data
science professionals are fantastic in the theoretical aspects of their
work, and they tend to specialize in narrow tooling specific to their
field. However, they can lack knowledge and experience in the tooling and
infrastructure needed to develop a model. That means you need to add more
people with highly demanded skill sets. Data science work requires peace
and time, and in a start up environment, one needs to adopt a special kind
of mind-set (speeded!), which may not necessarily play well together.
- The most important thing: data.
If you don’t have data sources for training your models before you start
development, it’s extremely easy to miss your cost estimates. From my
personal experience with three different products, such a process setup is
a huge money vacuum. Speaking for myself, I don’t see a way this could
succeed.
- Granted you have a data source,
maybe even a data lake, what will you do next? If you pause here without
an immediate answer, that’s another indication that it might not be the
right moment to jump into developing your own AI model.
If you don’t have answers to address the mentioned issues,
you should definitely look into pre-existing solutions. That means you shouldn’t build
your own AI model in the early stage of your business. In the beginning, your
priority should be to address the problem. It’s very likely that building your
own solution in-house is only a less efficient way to address it.
How to Start Your AI
Development?
Conduct Thorough Research on Existing AI Solutions
First of all, you should do your own research about existing
AI solutions on the market. I recommend starting with the largest cloud
vendors: AWS, Microsoft Azure,
and Google Cloud.
Each of them offers services aimed at people like you. Next, you should look
into what other companies have to offer; OpenAI and Anthropic come to mind. You
can also research open-source models that will give you more control
over your infrastructure.
Choose The Right Tools and The Right People
When it comes to tools, the higher the abstraction level, the
better. If you’re not already an expert in a specific tool, you don’t want to
become one as you grow your business. Being a founder promotes the
jack-of-all-trades route.
In connection to that, you need people to work with. As
mentioned earlier, people specializing in very specific technologies may not be
the best fit for you at the moment. I recommend looking for generalists
— people who are aware of what the market has to offer and are able to set up
and run end-to-end solutions efficiently. The time for hiring
specialists will come later, once you know your exact needs. In this case, I’d
look for data engineers or even skilled software developers with experience in
AI implementations, not data scientists. In my opinion, even an average web
developer should be able to make use of services provided by major cloud
vendors.
Focus on Data Acquisition and Management
Next, data: working on data acquisition is crucial, as is
developing data pipelines — which is actually a data engineering task. You need
a robust and reliable data infrastructure that ensures a continuous flow of
high-quality data into your system. This involves not just collecting data, but
also cleaning, processing, and storing it in a way that makes it easily
accessible for training and refining your AI models. Having an amazing AI model
without data is like having a supercar without a driving license. What’s the
point? The quality and relevance of your data directly affect the performance
of your AI, making this a critical part of your development process.
Design a Scalable AI System Architecture
With your data acquisition strategy in place, the next step
is to design the architecture of your AI system. This involves outlining how
data will flow through your system, how models will be trained and deployed,
and how users will interact with your AI app. Remember, simplicity is key. You
need to build a scalable and maintainable system, prioritizing functionality
over complexity.
Monitor and Refine Your AI System Continuously
Lastly, continuously monitor and refine your AI system.
Creating an AI algorithm is not a one-time effort but an ongoing process of learning
and improvement. Use feedback from users to make adjustments, and stay updated
with the latest advancements in the AI market to keep your product relevant and
competitive.
Conclusion
To wrap things up, building AI software in 2024 demands a
strategic blend of innovation and realism. The AI field is rapidly evolving,
and the key to success lies in clear use cases, robust data management, and
practical implementation. Whether you’re utilizing existing AI tools or
developing your own, focus on delivering real value to your users. Stay
flexible, embrace continuous learning, keep refining your approach.
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