Four Requisites for Strategically Scaling Your AI Investments
While almost 75% of global CEOs believe AI to be crucial for their businesses, the average return on artificial intelligence (AI) investments is just 1.3%. Companies at the early stages of AI adoption are more likely to show flat returns than the AI leaders who have a 4.3% return over AI investments.
To generate returns, firms must identify the correct business use cases, acquire and prepare the correct data, and build, test, refine, and deploy the working models. Considering the frequently high and upfront costs in people development, technology adoption, and data readiness, it takes time and investments to generate significant results.
There is a need for fundamental shifts in the organization and a strategic approach towards scaling AI. Here are the four requisites that you, as a business leader, must nail to scale your AI investments and generate positive returns.
Train Multi-disciplinary Teams to Ensure Success
According to Accenture, 92% of the companies implementing a strategic approach leverage multi-disciplinary teams to ensure success. Inter-disciplinary collaboration is the key to appropriately framing widespread and multi-faceted business problems into AI problems.
For that, you require an environment/platform that allows the technical experts, operational teams, and business leaders to share pipelines, models, and other assets across the enterprise. Such a platform will enable the various data stakeholders to efficiently collaborate, build, and deploy AI/ML models quickly to drive business value.
Build an AI Lab
To truly explore the AI capabilities and build long-term solutions, you must ensure that your data scientists have the liberty to play and experiment with new ideas, datasets, and algorithms without being constrained by downtime, resources, and roles. They need an environment that is available 24*7 to experiment, break, build, and repeat the cycle.
Have a Collection of AI Software and Packages
You and your team do not need to start and build everything from scratch. Several open-source libraries, notebooks, and AI software built after years of research and hard work are available to the AI community for continuous innovation and efficient development. You must provide your team with their “choice of stack” to facilitate them in innovating and building AI solutions faster and better.
Leverage a Platform That Allows Linear Investment
One way to reduce the break-even time is by choosing a platform approach that allows you to invest linearly as you realize value. Although you can choose to build the entire enterprise AI stack, it calls for a considerable upfront investment.
Building and deploying AI applications is vastly different from building software solutions. Scaling AI at the enterprise level is even more complex and challenging. It takes a significant amount of time for a typical firm to break even its initial AI investments. However, it’s all worth it as the rewards are much higher due to real-time insight-led automated and assisted decisions.
Read this whitepaper to learn more about how Newgen’s AI/ML-based data science platform, built on a low code framework, can help scale your AI investments and accelerate digital.