Part 1 of this series (A Year of Continued Necessity – Stemming the Tide) discussed the measures that organisations are taking to stem the tide of varied external factors (the ‘polycrisis’) facing them in 2023. This article discusses the measures organisations are simultaneously taking to go forward in new ways, leveraging the latest disruptive technologies.

Disruptive technologies, upon being first introduced to market, will have a limited array of use cases. Additionally, vendor maturity will be low, commercial models will be opaque, and public policy will fast follow.

Nonetheless, leading organisations continuously invest time and money to understand and determine how to immediately apply these technologies to their businesses. Highlighted here are three of these new, disruptive technologies, explaining how organisations are using them today and how they are positioning them for the future.

ROI Advantage Leaders: 29% of R&D Laggards: 18% of R&D Leaders consistently allocate more resources to disruptive vs. incremental innovation [1]

Generative AI

The momentum that ChatGPT has achieved in the market is taking generative artificial intelligence (generative AI) to the fore. Generative AI is a particular type of AI technology which focusses on the generation of various forms of new content, derived from its original source data. Most organisations are actively thinking about and assessing this technology. Some, however, are already using it.

They are using it for text generation, expediting the creation of outbound marketing communications, business / legal documents, and software code. Some are using it for image generation; generating new images for advertising purposes, design purposes, and logistical purposes (e.g. new map images from satellite images). They are also using the technology for business insights as an alternative secondary research tool.

Going forward, generative AI will be given expanded roles within organisations across all industries. It will provide anything from high-resolution image conversion (e.g. cancer diagnosis), to conversation-based business automation, to domain knowledge, with the goal of giving everybody in the organisation access to trusted, relevant information at the pace that their customers demand.

Content Explosion: By 2025, generative AI is expected to generate 10% of all data [2]

Success of a generative AI strategy lies in understanding what generative AI should do versus what other complementary technologies should do; having a flexible, multi-layered architecture; having curated and targeted data.

AI Cybersecurity

As AI continues to expand into organisations’ critical business functions, their cybersecurity risk profile expands in proportion. AI systems can be seized: their outputs can be altered, exposing an organisation to a wide array of business risk. AI input data can be seized: given most current AI models rely on centralised, aggregated data, the risks of large personally identifiable information (PII) breaches are high. Even if a cyber threat cannot directly access AI systems or data, ‘data poisoning’ can supply the AI model with invalid training data, causing unexpected decisions to be made in live operation.

Organisations are taking immediate measures to protect their assets. Containerisation of their cloud-based services provides organisations with a rich set of security features out of the box. MLOps techniques help organisations standardise and automate their AI systems development process, inclusive of cybersecurity risk and compliance.

As they move forward, organisations will increasingly make use of federated learning to protect their AI systems. Federated learning allows AI systems to act on disaggregated data sets, keeping the data in its local, more secure environment. A further measure is explainable AI (XAI). XAI brings accountability to AI models. It enables transparency of simple and complex models, enabling organisations to be able to quickly notice and respond to errors and threats.

41% of organisations in the US, UK, and Germany had experienced an AI privacy/security breach [2]

Leveraging cloud security capabilities, making security part of the software development lifecycle, and minimising data aggregation and movement are vital elements to expanding the use of AI within an organisation.

Private 5G

5G is the most advanced mobile broadband technology today. Like AI, it will enable a vast array of innovation across all industries. To-date, the majority of 5G’s benefit has centred around providing faster wireless broadband speeds.

However, a pivot has begun towards fulfilling high-performance connectivity and digitisation use cases for organisations, through the use of private 5G deployments. Breakthrough use cases have already emerged.

• On-premises 5G networks are being used for data communications, within office complexes, ports, and other large sites, where large amounts of data traffic must be managed and optimised.

• Inventory optimisation and supply chain management is another cross-industry breakthrough theme. Product levels are being closely monitored against demand. Supplies are being tracked within large sites/complexes, improving process efficiency.

• 5G, AI, and edge computing are being synergistically used for remote operations and quality control. In the case of the former, large and/or remote sites are being remotely monitored, risk and impact is being assessed in real-time, and activities are being streamlined. In the case of the latter, 5G-enabled computer vision is providing on-site, real-time quality assurance (e.g. for product packaging).

Business Benefit Early private 5G solutions are on pace to improve processing time by 30% [3]

Identifying strategic connectivity and digitisation opportunities, designing a results-driven execution path, and maintaining stakeholder confidence and commitment are critical to 5G business transformation success.

Sources

[1] IDC

[2] Gartner

[3] 5Gradar