AI has been seen as one of the most promising technologies of our time. Gartner has found that 91% of enterprises have ongoing investments in AI technologies. There is a highly optimistic outlook towards the business gains that can be achieved using AI implementation. In fact, Netflix generates $1 Billion worth of revenue through its smart, personalized recommendations to its customers (Business Insider). But the application of AI is much broader for enterprises working on global scales with businesses spanning multiple sectors and industries.
The implementation of AI can be found in operations, automation, ERP, and practically every aspect of a business, including decision-making. This is where the concept of AI-at-scale has found its roots in the long-term goals of enterprises and sees such heavy investments. However, only 25% of organizations have widespread adoption of AI-enabled processes, and 33% have started implementing them in limited use cases. This is far from enterprise-wide AI enablement and can be attributed to several factors, namely:
- Lack of proper infrastructure and data management.
- Siloed development and implementation without holistic considerations.
- Lack of specialized skills and management strategy to implement AI.
With such roadblocks preventing the use and scaling of AI-enabled technologies, a strategic outlook for AI implementation seems to be missing. So let’s look at the simplest way to move towards realizing enterprise AI at scale.
Smart Infrastructure and Master Data Management
AI technologies are heavily dependent on data. Even designing the right models for AI depends on the type and extent of data available for processing and learning. Thus, implementing enterprise-wide data capture with standardized and centralized access is the foundational step to realizing AI at scale. For most enterprises, only 10% of data is structured (Forbes), which can be directly translated into their AI readiness. Thus, master data management (MDM) combined with smart infrastructure are essential technological leaps for enterprises before any scalable AI enablement.
Master data management in itself is a powerful implementation that can generate returns soon after implementation. You can read more about MDM in our whitepaper here. Having centralized access to enterprise data on all aspects of business can be used to generate insights from day one. When MDM is supported by a smart infrastructure that can provide capabilities like automation, broad-level integration, and processing power, it is easy to achieve AI readiness. As the reach of integrations and data capture penetrates different aspects of the business, AI models can be optimized and scaled to test capabilities and eventually deployed on the same systems.
Development Vs Ready-to-Use Commercial AI
One of the primary roadblocks to AI at scale is developing and testing AI models and systems. Given enough time and resources, it can be advantageous to make such an investment. However, most enterprises are limited by the availability of these resources, especially skills. In a Capgemini research, 70% of the respondents said the lack of mid to senior-level AI skills is a major challenge against developing and scaling enterprise AI. Thus, purchasing commercial AI can be a smarter investment for most organizations.
In 2021, 28 % of large EU enterprises used artificial intelligence technologies, of which 53 % purchased ready-to-use commercial artificial intelligence software or systems (Eurostat). Piloting and customizing readily available AI systems can provide considerable cost advantages in the long run, along with saved time costs. This investment can reap dual benefits by providing the foundational system to build upon and bringing in the right skills through the vendors.
SLK Software has wide-ranging experience designing and building AI-ready smart infrastructure and in-house developed AI products that can seamlessly integrate with any system. We have transformed businesses in BFSI, Manufacturing, Investment Management, and many more sectors with our smart products and cutting-edge technology implementation. We are driven by our client’s goals and provide ROI-driven services to enable scalable and future-proof growth.
AI at scale is a long-term goal and should be properly strategized, planned, and implemented for the best business outcomes. However, the implementation journey can be rewarding in its own right. The milestones required to implement enterprise AI are marked with changes and practices that can start giving results even before the overall scope of the AI at scale is achieved. Besides technology, it requires a shift in management practices, decision-making, and developing an integral and inclusive system for the entire organization. Achieving these can result in gains independent of technology and AI. And when integrated with them, they can reap unfathomable benefits.