Page 177 - Cyber Defense eMagazine December 2022 Edition

organizational agility, faster go-to-market product cycles, better cost-effectiveness, and greater talent and
resource management. To further elaborate:

• Agility: The speed of technology evolution dictates organizations to be more agile. Yesterday's
tech stack may already be outdated today. The elegance of low-code is its modularity.
Independent components create a plug and play environment within an application flow.
Therefore, an organization can make small changes rapidly, literally iterating to the market.
• GTM: Low-code componentizes blocks of code, allowing their reusability, which improves
development times compared to traditional coding methods. Rapid assembly of pre-built
components into flows, nodes, and templates simplify software development. Consider the
current traditional model, where teams of developers, even in remote locations, have to manage
complex sprints to enable integrations between frontend and backend applications, legacy
systems, and data silos. Many low-code platforms claim a 10x faster app development. At Iterate,
we have measured up to 17x with our platform.
• Cost-effectiveness: Code reusability, shorter development cycles, and simplifying workloads all
cumulatively reduce software development costs. Furthermore, over the long run, these savings
make a significant impact to your budgets in building, upgrading, and integrating new applications.
• Talent and Resource Management: If we consider a macro picture, there are roughly 25 million
software developers in the world. In contrast, the global talent for AI engineers is at 300,000 (in
2017) according to Tencent. An organization with limited resources would be hard challenged to
compete for AI talent, yet at the same time, it would be foolhardy to ignore the importance of AI
application development, given that enterprise AI technology is growing at over a 20% CAGR.
Low-code brings accessibility to AI development. Using the same methods of componentizing
blocks of codes, pre-built AI/ML models can quickly be customized and deployed for commercial
applications. Iterate’s platform, Interplay, has 43 of them. From a micro point of view, low-code
upskills your existing developer team. For example, a web engineer can easily use a low-code platform,
with existing AI components, to build AI-powered chatbots/voicebots, product recommenders,
knowledge graphs, computer vision applications, and much more. With Interplay, it is possible to build
and deploy these applications at a production level, with the scalability and security requirements met.
Similarly, non-developers can embrace a low-code environment to drag and drop blocks and make
enterprise applications, not necessarily just AI ones. These can be frontend web forms, mobile apps,
HR/finance databases, etc. Upskilling with low-code in effect maximizes the productivity of not only your
developer teams, but also your entire organization.

The preceding arguments are explicit reasons to apply a top-down low-code strategy. Additionally, there
is an implicit but powerful advantage to strongly investing in low-code for your enterprise AI development.
There is a “dirty secret” about relying on external vendors to develop AI applications provided by vendors,
whether via SaaS or license models. The intellectual property that comes from developing your use
cases - the AI/ML models and algorithms - is not necessarily yours. Oftentimes, your proprietary data
that is needed to build out your AI use cases are training your vendors’ models, which is their IP.
Considering the effort required to gather, manage, and process data, not being able to own any of the
final assets is a considerably missed opportunity.

Cyber Defense eMagazine – December 2022 Edition 177
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