Three Findings from MIT Technology Review Insights to Shape Your Infrastructure Investments in 2024

Generative AI is revolutionising the business world, and to get the most out of it, you need a robust digital infrastructure and automated processes. Telstra International has collaborated with MIT Technology Review Insights (MITTR) to produce a global report that provides valuable insights into the essential foundations necessary for the successful deployment of generative AI.
· 10 May 2024 · 3 minute read

How Companies Use AI to Improve Operations

Many companies are using AI to automate repetitive, low-value tasks that require minimal human intervention. According to the MITTR study, 54% of the surveyed businesses are using generative AI this way (Figure 3). Additionally, 33% of respondents are using AI in product innovation, as they believe that generative AI can transform how products are developed, marketed, and delivered to customers. Incorporating strong data management, predictive analytics, and generative AI can form the foundation for taking product innovation to the next level.

Figure 3: How companies plan to use generative AI
In which areas are you currently using generative AI technology or will you begin in 2024?

Figure 3: collection of responses from surveyed businesses in which areas they are currently using generative AI, vs those that will begin in 2024. ; automating repetitive, low-value tasks: 54% vs 31%. ; customer service: 29% vs 48%. ; strategic analytics: 20% vs 54%. ; product innovation: 33% vs 41%. ; supply chain logistics: 13% vs 38%. ; Sales: 12% vs 35%. ; compliance purposes: 8% vs 35%. ; coding and IT development: 18% vs 24%. ; risk and legal matters: 7% vs 27%. ; none 22% vs 2%. ;
Figure 3: How companies plan to use generative AI

Generative AI Seen More as Opportunity than Threat

The study shows how corporate leaders perceive generative AI as pivotal for gaining a competitive edge in crucial strategic and operational areas, while helping navigate disruptions from technological shifts, evolving customer preferences, and market competition.

More than 50% of respondents plan to use generative AI to drive strategic analysis, while 41% are using it in product innovation and 24% for coding/IT development, starting from 2024. With the market growing rapidly, adopting this new technology sooner rather than later can offer a true first-mover advantage. In fact, early adoption will quickly highlight resource and capability gaps and give first movers a leg up in investing in people, technology, and process change.

Implementation Challenges

Despite the bullish sentiments around generative AI, there are still significant challenges for companies wishing to adopt intelligent, cognitive computing processes in their operations. This is borne out by the fact that a substantial 67% of companies have done a generative AI POC (Proof of Concept) but haven't yet deployed the technology widely, and only 9% of executives said they are using generative AI widely across the organisation. (Figure 2).

Figure 2: The state of generative AI adoption
Which of the following best describes your company when it comes to adopting generative AI technology across the business?

Figure 2: The states of companies’ generative AI adoption by percentage. ; Have not adopted any generative-AI powered apps and no plans to do so: 6% ; Have plans but not yet adopted: 18% ; Conducted at least one trial: 30% ; Using in specific areas with plans to deploy widely: 37% ; Widely used across organization: 9%
Fig 2: The state of generative AI adoption

Often, the reason is that while businesses are broadly aware of the importance of adopting generative AI technology and the advantages it can offer, they fail to consider whether their IT assets can accommodate the required data ingestion, sharing, storage, and processing of massive and diverse data sets.

How to Build Robust AI-Ready Infrastructure

Many enterprises aiming to disrupt their industries with generative AI underestimate the requirements for effective deployment of the technology. While 19% of respondents recognised that they needed to uplift their volume of data for their envisioned generative AI use cases, only 13% and 7% indicated that they feel confident having the necessary data storage infrastructure and computing platforms for their AI adoption requirements, respectively (Figure 4). Building enterprise-grade large language models (LLMs) requires expertise in collecting high-quality data, setting up the accelerated infrastructure, and optimising the models. Organisations looking to deploy AI services must ensure they have the right foundations to support them.

Figure 4: Few perceive their IT assets for deploying generative AI as highly conducive
Percentage who consider the following highly conducive to the rapid adoption of generative AI at their companies (on a scale of not at all conducive (1) to highly conducive(5).

Figure 4: Percentage of businesses who consider below the factors highly conducive to rapid adoption of generative AI ; Quality/accuracy of available data for large language models: 28% ; Ease of creating processes to flag possible hallucinations: 27% ; Effective of mitigating cybersecurity risks: 21% ; Ability to protect privacy of customer/supplier data: 20% ; Volume of data available for large language models: 19% ; Data storage infrastructure: 13% ; Suitability of existing in-house hardware or outsourced platforms: 7% ; Technical and business considerations related to timely capture and analysis of data: 6%
Fig 4: Few perceive their IT assets for deploying generative AI as highly conducive

AI is transforming every industry and business. To stay ahead, organisations can look to integrate AI into every aspect of their operations as part of AI-Integrated Business Dynamics. This aligns AI technologies with business processes to optimise performance and efficiency. By investing in AI-enabled machine-to-machine and human-to-machine technologies, businesses can deliver compelling digital experiences and gain a competitive edge.

Why Telstra

Telstra's Hyperconnected Digital Infrastructure strategy revolves around three core principles: right fit, right size, and right locate. By ensuring the technology stack aligns with applications and data estates (right fit), optimising resources to match workloads efficiently (right size), and placing applications, AI models, and data in suitable environments (right locate), Telstra International aims to help its customers leverage their digital infrastructure to the fullest.

For a deeper understanding of these insights and more detailed findings, we encourage you to explore the full MIT Technology Review Insights report. Discover how these strategies can be applied to enhance your infrastructure and stay competitive in the rapidly evolving digital landscape.

Related articles