Has AI/ML (Finally) Become an Enterprise Mainstay After a Long Period of Experimentation?
The past few years have witnessed the rapid expansion of the enterprise technology portfolio. The verdict is that this expansion is only going to continue, and the pace of change is only going to accelerate. More organizations will move to the cloud, adopt edge computing, and employ AI and ML applications for business. Businesses are also looking at leveraging the benefits of efficiency and insights that come from vast volumes of data generated by these technology applications.
Without exaggeration, digital transformation is accelerating at light speed, and the decree is that it will only increase in velocity as the world of work becomes more hybrid and boundaryless.
The Maturing of Key Technologies
Technologies such as AI and ML that were ‘new and upcoming‘ even half a decade back are now seeing widespread application and implementation across enterprise portfolios. Their use cases and applications were previously piecemeal or restricted and immature to enable digital-forward businesses. On the contrary, today, enterprises are deploying AI technologies more broadly and realizing their benefits across the business.
Open AI, for example, is now an integral part of the Microsoft office suite. CRM and ERP systems are powered by AI and ML. Business analytics is leveraging the AI/ML advantage. ChatGPT has taken the enterprise world by storm. An increasing number of companies are now working in the space of generative AI. The list of AI and ML use cases keeps on growing.
A 2022 study further reveals that 90% of businesses are using multi-cloud technologies to achieve their business goals. The cloud hosts 75% of enterprise workloads and 64% of AI workloads as well. Core applications like CRM and ERP are hosted on the cloud by default now.
Enterprises are also deploying AI technologies more broadly to realize their business benefits. Previously, a survey found that:
- 73% of respondents agreed or strongly agreed that their AI deployments have already transformed the way they do business.
- 86% of organizations surveyed have middle- or late-stage AI deployments. 80% witnessed at least some measurable benefits from AI.
- While most businesses use AI to automate routine or inefficient processes, those in later stages of AI deployment use the technology to innovate and differentiate.
According to a more recent analysis, 87% of businesses believe that AI and ML are central to realizing business success in terms of growing revenue, enhanced operations, and improved customer experience.
The new technology ecosystem is expected to only expand as these experimental technologies prove their business benefits. This rise in adoption can be attributed to the pursuant infrastructure advancements that support the performance needs of these technologies. As these technologies become more mainstream, enterprises have to also look at concerns associated with tech adoption.
Identifying ways to contain cloud costs that can balloon by 3-4 times than what was initially planned, for example, is crucial for enterprises. Organizations also want to increase the use of microservices, containers, and Kubernetes to leverage the true power of AI and ML.
Along with all this, enterprises are also now looking to explore technologies such as AR, VR, Blockchain, and generative AI and build up enterprise applications based on these.
The Increasing Complexity of Tech Adoption
As the enterprise tech ecosystem expands, it becomes more exciting. It also becomes more layered and complex. This complexity of the enterprise technology ecosystem is closely matched by its growing criticality to enterprise performance.
One of the key reasons that impeded adoption in the past was the infrastructural needs of these technologies. Organizations had to face major challenges in ensuring the infrastructure performance, availability, and scalability to deliver AI/ML applications and use-case performance. These remain crucial consideration points even today, especially as the world of work becomes hybrid.
Today, organizations are expanding more digital connections with their end users and looking towards leveraging new technologies and their applications to meet customer expectations and leverage market opportunities. For this, they need to create scalable and resilient technical foundations that boost performance and also lower costs.
The Road Ahead
As technology applications and systems become the workhorses of enterprises, elements like IT reliability, application reliability, and site reliability engineering emerge as crucial practices. These practices ensure that the technology stack can deliver the required performance, availability, and scalability on an ongoing basis. Solutions like Digital Experience Monitoring and APM-AIOps are, as such, becoming important priorities.
Digital Experience Monitoring and AIOps help organizations navigate growing operation complexity and help them deliver new-tech-powered proactive, personal, and dynamic services. These technologies are becoming crucial to ensure that the infrastructure is engineered for security, scalability, availability, and performance.
The growing enterprise-wide technology stack and cross-domain correlation of IT infrastructure components come with great complexities. As the enterprise tech stack becomes increasingly interconnected, even the slightest change in security, networking, infrastructure, or application layers can lead to large-scale butterfly effects.
As the consumers of technology applications build zero tolerance towards choppy and slow experiences, using technologies such as APM AIOps becomes crucial to maintaining and managing the Quality of Service. Technologies like AIOps are becoming crucial to capably deliver proactive, personal, and dynamic services. With AIOps, organizations can:
- Dynamically allocate the right network and infrastructure resources to drive peak performance
- Gain visibility into all the layers, and identify the factors affecting the end-user experience
- Employ visual-based metrics to track the user perception of every page load
- Measure workflows with form data submission and user actions to test business logic and performance of back-end systems
- Monitor websites, critical business transactions, and API endpoints
- Measure workflows in their entirety across multiple pages, simulate end-to-end customer journeys, and monitor user actions
- Gain insights into the benchmarking data for clear competitive positioning
- Forecast and resolve issues before they have a business impact
Conclusion
Performance, availability, and scalability challenges in enterprise-grade applications can have serious business impact. They can jeopardize brand image and impact the bottom line. These applications that use new-age technologies have their parameters for performance. Organizations, as such, have to make sure that not just the application but the entire enterprise tech stack and all its influencers are rigged for peak performance.
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