Getting Ahead With AI-Enabled Apps
Q&A With Amol Ajgaonkar, CTO of Product Innovation
Contributor Amol AjgaonkarCTO, Product Innovation
An AI-enabled app is essentially an app that is infused with some parts of the functionality of AI. This means that certain actions will be taken for you in the backend. The user interface could change based on what you’re trying to do.
For example, when you search for something, the technology will take what you’re searching for and get you more relevant results using natural language processing and generative AI.
Essentially, any app that is taking a few actions by itself, based on what it thinks you need, could be considered an AI-enabled app.
We use AI-enabled apps all the time, and most of the time, we don’t even notice. Built-in AI features — such as machine learning, generative AI or computer vision — provide the power that make apps AI-enabled. If architected and designed correctly, users gain a lot of value in apps that are more interactive.
Some of the advantages of AI in apps include improved workflows for the user, personalized suggestions, and an extra layer of data, which will make an app more useful for the end user. One major benefit is enabling all tiers of an organization to get answers to the questions they want in a way that is more natural to them.
With our clients at Insight, we see this trend of adding AI to apps across the board. And it’s very valuable for our clients and for the general public to have something they can hold in their hand, which enables instant insights. This way, opportunities become clear, along with the impact they want to achieve with an app.
A common example is on the support side of things, such as customer service chat bots. That’s a perfect use case for an AI-enabled app. Chat bots are often limited in their answers. You ask a question and get a limited number of options. Then, none of them relate to your problem, and you’re frustrated. That’s a poor customer experience.
If you take that same concept and turn it into an AI-enabled app, you can leverage natural language processing capabilities. This technology quickly figures out exactly what the customer is asking and what they need.
Now you’ve created a better customer experience. It’s more conversational. And you’ve reduced the dependency on someone else to look at it and answer it.
AI-enabled apps are also useful in healthcare, manufacturing or any other domains where a lot of data is produced at the edge. You have data from Programmable Logic Controllers (PLCs), Customer Relationship Management (CRM), and other systems, and they are all connected. You also have your inventory data. Now, you can ask questions like, “Tell me what my inventory was compared to how much I produced.” Or “What was my output and which machines have more wear and tear?” With the help of natural language processing, generative AI, vector databases, and other emerging tech, there’s no need to access four different systems. Or contact four different people and then combine it all. Instead, you’d have your answer in seconds.
Organizations must do a couple things to better prepare for AI-enabled apps. The first step is to identify the business use case and the value/impact it will have on the organization.
The second step is technical readiness. You must prepare the data — identify which data sets to use and structure them in a way that will enable the app to quickly consume and support the desired functionality.
The third part is about people. There’s a cultural shift in the sense that organizations need to understand that AI is not everything, but it’ll be a big part of building new applications. You need to discuss AI at the start to understand whether or not it fits in the application or ecosystem you’re creating. And if it does, what kind of a role does it have? And after you determine the role, then it comes down to the required data to make it useful.
AI without a purpose won’t benefit anyone. It will not deliver any business value. Establishing the business value — the culture that makes people consider AI early on — and then having that data estate ready are the high-level conversations teams must have now.
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