Scaling AI: Exploring a Successful Approach to Building AI

For most companies there is a large and growing gap between the potential for AI to improve their business, and their ability to respond to that potential.  Why is that happening and what can be done to accelerate AI adoption at most companies?

Scaling AI

There are many reasons why companies struggle to implement AI, some of which we have discussed before and summarized here.  As companies struggle to either get started with AI or find success in their AI efforts more anxiety creeps in over the fear of missing out or falling behind your competition.  This anxiety motivates “build versus buy” conversations.  In the context of AI, the “build” path means investing in the AI-specific talent and technology (data scientists, engineers, and programmers, with AI experience along with hosting and processing technologies) to ideate, create, collect, adapt, deploy, and manage.  The technology isn’t such a challenge but the market factors to acquire and maintain the talent necessary to do this well means the investment in time and money is significant. You can outsource this but if AI is seen as a long-term necessity that may be central to the value of your customer experience outsourcing AI activity surrenders IP control, competitive advantage, and long-term scalability.

If you’re wondering how other companies have responded to the build versus buy decision, one study from January 2022 showed that of those companies that adopted at least one AI technology, 60% purchased an off-the-shelf solution or outsourced AI development and only 40% developed their own.

Clearly there are advantages for those companies who design, develop, and deploy their own AI solutions but the process to identify helpful models, adapt them to your specific needs, and connect them to other models to achieve your desired outcome is difficult.  Here’s a two part article describing what the typical experience is like to create one relatively simple AI solution.  In this case it has a relatively modest scope of extracting insights from text reviews.  Still, it requires significant know-how and effort and doesn’t touch on the additional steps required to properly integrate an AI automation with your customer-facing solutions or other work processes and tools.

What can be done to make AI deployment easier?

This gap between AI’s potential and most companies' ability to do anything about it screams for an innovative solution.  Something that allows companies to build their AI solutions themselves.  Something that simplifies the creation of world-class intelligent engines that possess the following capabilities:


Make it less cumbersome to embed AI into traditional software development.


Solve one of the biggest challenges with AI models, their need to be frequently re-tuned with the latest data to ensure accurate results, even as the data it analyzes evolves over time. 


Change AI’s monolithic nature of each model solving for one problem at a time and simplify how multiple models can work together to address practical needs. 


Enable an AI development and deployment experience with built in tools to manage state or memory more closely replicating the human intelligence experience and simplifying repetitive or iterative tasks that operate beyond a single session.

Domain agnostic

Arrange the AI building experience so that it is domain agnostic by default but can be tuned for specific purposes.


Deliver AI deployment with security features accounted for in its design and easily implemented upon deployment. 


Design the AI development experience so integration simplification is accounted for, and deployment and its varied software and workflow integration tasks are accelerated.


Being aware of how your AI is performing and measuring the deviation from desired results gives you proactive control over mitigating risk.


Make it easy to not only deliver the analyzed result to meet the primary objective, but to also easily leverage the analyzed results to enhance other solutions, processes, or to share insights in a way that improves enterprise-wide knowledge, decision-making, and impact.

That’s a lot, we know, because we’ve been hard at work creating just this experience.  Follow us at Qualetics Data Machines and you won’t miss our upcoming release announcement.  In the meantime, if you would like to learn more feel free to contact us.

AI development