Why Low-Code AI Is Needed Now More Than Ever
Solomon RaySolomon Ray

Why Low-Code AI Is Needed Now More Than Ever

By Solomon Ray, Director of Innovation, Strategy, and Special Projects at Iterate.ai

As a business leader and executive, a “low-code strategy” may not be on the top of your mind, but it presents a valuable opportunity to transform your organization digitally.  At Iterate, we take an even more bullish view on low-code adoption; it will become table stakes for every digitally agile organization in the near future.  The trends have been shifting.  Gartner notes “By 2025, 70% of new applications developed by organizations will use low-code or no-code technologies, up from less than 25% in 2020. The rise of low-code application platforms (LCAPs) is driving the increase of citizen development, and notably the function of business technologists who report outside of IT departments and create technology or analytics capabilities for internal or external business use.”  John Selvadurai, Ph.D., VP of R&D at Iterate, has seen the immediate impact of implementing a low-code strategy.  He emphasizes, “Maintaining enterprise applications long-term is a very costly operation. These enterprises can reduce that maintenance overhead significantly by having an organizational level low-code strategy.”

The relevance of low-code application platforms is not limited to software developers or IT teams.  It is certainly befitting of a top-down business strategy. Low-code adoption is a catalyst for increased 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  Yesterday’s tech stack may already be outdated today.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.

Before you assume that owning your AI/ML IP necessitates recruiting a team of data scientists and AI engineers, low-code offers a very practical solution.  While pre-built AI/ML models and open source libraries can be easily integrated into the environment, these resources can be customized (by your existing developer team as previously established) into your own proprietary IP.  Training these models with your own data, you can create your own “derived” AI, which you can tailor to your own unique business cases.  Brian Sathianathan, CTO and Co-founder of Iterate.ai, affirms, “AI development follows the 80/20 rule; 20% of the AI methods can address 80% of your AI solutions in the market.  Derived AI is more than sufficient for most organizations to capture those 20% of methods, and leaders should treat it as a competitive advantage.”  Keeping your own AI model development in-house ensures your complete stewardship of your valuable data and its privacy, as well as the flexibility to use those models and their algorithms for any other use cases.  Again, adopting low-code within your organization affords innumerable benefits for your business strategy.

The general philosophy around digital innovation is to resist inertia, be agile, and adapt to change.  As such, digitally forward leaders and their organizations should always have a sense of urgency about technology.  When thinking about innovation, today is the day to start.  Tomorrow is too late.  Low-code is no exception; the opportunities you embrace with its strategy will pay off the sooner you start.

About the Author

Why Low-Code AI Is Needed Now More Than EverSolomon Ray has extensive experience in management consulting, startups, and technology. He has worked at companies such as Samsung, Applied Materials, and Xerox PARC, advised seed-stage startups, and consulted large enterprises. He holds a BS in Electrical Engineering from UCLA and an MBA from the Johnson School of Management at Cornell University.

February 20, 2023

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