AI TRANSFORMATION

The crucial first step toward AI integration companies can't skip

The crucial first step toward AI integration companies can't skip

36 months following the idea of AI integration for many products through AI-led transformation service, we faced a lot of experiences in designing and implementing AI products, some good experiences but also several failures.

Three years before ChatGPT changed the intelligent-machine paradigm, analytics, AI, and big data projects failed a whopping 85 percent of the time. And of those 15 percent of the projects that reached deployment, only 60 percent of them ended up turning a profit. (Source: Hackernoon)

We believe this is a normal journey when we dare to join the list of pioneer AI service providers. Each failure is a big lesson for us to move forward and standardize our service. If you are a company looking for AI integration, this post will be very useful for you to have an easier and safer your AI integration journey.

Why AI? - Choose your AI roadmap

The crucial first step in incorporating AI into your company is not to adopt it just because everyone else is doing so. Instead, it is recommended to organically integrate AI into your overall business strategy and goals. AI implementation should be a response to the specific needs of your company, rather than a passing trend.

If you are a Product Owner, AI integration is just one feature of your company backlog, so we should treat it the same as other features such as:

  • Priority: How much Business Value this AI integration will bring to the company in comparison with other features? How much this AI integration will help the customers, which metrics will be optimized ? ...
  • Size (Effort and cost): Considering effort and cost
  • Resource/Tech constrain: Considering resource capacities (in-house or outsourced) as well as technology constrain.

Only if the AI roadmap is shown up as a good investment in comparison with other features in the backlog, then you can add this to the action plan. At this level, you may need help from AI Consulting partners who can bring more ideas, refer successful user-cases, AI resources, and advanced technologies.

Start with data, build small, and experiment

Don't start with a fancy and completed AI feature, following the Agile approach for an MVP version of AI integration, we can call this a "Proof of Concept (PoC)" for AI. This is even more important for the AI field because the accuracy of the AI model depends on the input data, the initial model, and the Experiment roadmap.

  • Input data:  before we start with the AI model, the data comes first. We need to assess to make sure the data is in a good structure, good volume, and good quality.
  • Design AI model: to provide an outcome of the AI model, there are a lot of different approaches and models. We can come up with 02 AI models and compare them to select the better one.
  • Experiment: It will be hard to come to the perfect accuracy on the 1st try. We need to come up with an accuracy formula (or a similar way) to identify how good the AI model output is. From here we can come up with a lot of improvements, and optimizations, as long as the accuracy is always been captured.

Hosting, Scale, and Data Privacy

Implementing AI involves extensive use of data. However, compromising the privacy of your users and customers in the rush to launch is not acceptable. It is essential to ensure that information is protected and secure, complying with relevant regulations.

  • Hosting: where you host an AI model, and store data is a key. You may have the option to host on-premise (your own infrastructure) or cloud services. But with an on-premise solution, you may need to trade off some cloud solutions and narrow down your technical solution options.
  • Scale: consider the data volume and scalability needed for the platform. AI scale needs a different approach.
  • Data Privacy: if you host your full infrastructure (including AI) using cloud service, you need to have a good strategy for your data privacy. Let's start with following best practices for Data Security and Compliance recommended by AWS, Google Cloud, and Azure,... and assess the 3rd party solutions that share data with your AI product.

Work with AI Expert

AI field is still in the development market so there is a huge need for talent resources and proven solutions/user-cases. At Investidea, with the AI-led transformation service, we aim to make your AI journey easy and optimal. Starting with our proven solutions which have already been operated in some similar business, we will help you to start with the right AI approach and follow the product for the long run.

Connect us at investidea.tech or book me an appointment for more details.

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