Is Your Tech Stack Ready for the Future of AI + Machine Learning
An enterprise’s tech stack is like the foundation of a modern business. If your foundations are unsteady, then it creates risks throughout the organization. Yet in a rapidly changing business landscape, identifying, understanding, and implementing the right tech stack is harder than ever.
A major global study undertaken by researchers at RepData LLC revealed that 80% of IT Managers in the US, UK, and Australia agree that our tech stacks need to change. The good news is that more than half of IT decision makers increased their software budgets in 2022—investing in a fundamental part of the tech stack.
Software, however, is only one part of an increasingly integrated and interwoven tech stack, which brings together software tools, apps, platforms, third-party services, and data storage technologies.
This ecosystem of technology now faces radical new shifts, as the growing role of artificial intelligence (AI) and machine learning (ML), and changing digital priorities, create new pressures and demands on tech stacks that are already straining under the accelerated pace of change.
Tech stacks for the new normal
The COVID-19 pandemic has focused minds across many industries as to the need for significant digital transformation, and its implications for legacy tech stacks for businesses, according to Lenovo’s Global CIO study. A remarkable 82% of CIOs believed their role had become significantly or somewhat more challenging in the two years preceding the study, which was published in 2022.
Tech stack transformation is a major part of that challenging landscape, with more than half of CIOs (57%) said they would replace half or more of their company’s current technology if given the chance. This is increasingly important in an area of accelerating telecom technologies such as 5G networks.
Some industries have obviously faced a more jarring disruption during this period, with the likes of telecoms facing significant changes as remote working and virtual operations placed growing pressure on the industry. CSPs are now increasingly part of an integrated ecosystem, and one which some reports indicate could unlock up to USD700bil in new revenue opportunities if the right technology partnership and digital strategies are in place.
The gap between those tech ‘have-got’ and ‘have-nots’ is also widening according to research by Accenture. The top 10% of tech-enabled organizations ranked by adoption of key technologies, penetration, and culture, were increasing revenues at twice the speed of the bottom 25% of tech laggards as of 2019. Updated research shows that gap has increased as of 2021, with tech leaders now increasing revenue five-times faster than laggards.
Realizing the potential for tech-success in telecoms means organizations must wrestle with the legacy nature of existing tech foundations, which threaten to lock in legacy processes and ways of working. CSPs need modular, adaptive, and AI-ready solutions.
Machine learning and AI represent an interlinked platform opportunity that will be fundamental to future telecoms success, and an appropriate tech stack and telco systems will be critical to delivering on this. The evidence is clear—AI enables improved operations.
In a report by TMForum and Accenture, published in September 2021, it reveals that an AI-enabled IT framework can deliver a 25% reduction in customer complaints, 20% reduction in network faults, 25% increase in service availability, 30% reduction in manual operations, 35% reduction in energy consumption, and 32% growth in OTT revenue. Those are figures that any telco would be eager to embrace, and present a compelling picture of the future of AI in telecoms.
Modular solutions from AI and ML pioneers
Neural Technologies has over 25 years of experience delivering modular solutions for revenue protection, data integration, signaling, AI-machine learning (AI/ML) software for the telecoms industry.
Our solutions are designed to slot into existing tech stacks to enhance operations across a wide range of applications. They integrate seamlessly with existing and new data sources, meaning CSPs can start to unlock value from existing sources alongside new, emerging technologies. That means a scalable solution that delivers that adaptive capability that is essential for companies seeking to gain a reputation for tech leadership.
Our own AI/ML solutions also allow for automated solutions that enable CSPs to react quickly and effectively in a dynamic and high-volume environment, providing informed business decision making alongside automated response to critical events. That’s vital in a landscape where 83% of executives agree that their organization’s business and technology strategies are becoming inseparable, and even indistinguishable.
CSPs are increasingly reassessing their tech stack in the face of this evolving industry landscape, and it’s true that the solution will require an equally important evolution in that technology strategy. The good news is this doesn’t automatically require a demolition of existing tech stack, just a recognition that modular, scalable solutions can help empower future operations, with the right partners in place.
