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The state of Artificial Intelligence in 2024

By Maxime Vermeir, Senior Director of AI Strategy, ABBYY

This year, we saw innovation teams experimenting with a variety of automation tools powered by artificial intelligence (AI). As enterprises navigate the potential for business value through large language models (LLMs) like generative AI, adoption of AI continues to grow increasingly widespread. According to recent research, the large majority (89%) of IT executives say that they have AI strategies in place, with 37% having a roadmap spanning three to five years.

Organisations were surrounded with AI hype in 2023 but have since had time to cut through the noise and determine the best business use cases for using it in their operations. This resulted in a realisation that despite their profound potential to generate value, the most powerful general-purpose AI tools can be unscalable, costly, and resource-consumptive, rendering them unsuitable for many enterprise automation goals. However, enterprises that don’t find a way to apply specialised AI solutions to business goals will find themselves falling behind their competitors.

In 2024, there is a need for purpose-built AI that will solve specific pain points effectively, efficiently, and in a scalable and resource-conscious way.
Key challenges and focuses for businesses in 2024 will be strategically integrating AI into organisations, measuring the success of AI implementation, and managing the ethical and legal risks of AI while staying ahead of the innovation curve.

AI strategies

In order to harness the power of AI, businesses need to anchor their AI strategies around clear, purpose-driven goals that align with business outcomes. These are three steps businesses should follow to establish effective AI strategies:

  1. Identify Clear Objectives:
    • What business objectives do you want to achieve with AI? Whether it’s improving operational efficiency, enhancing customer experience, or driving innovation, it is crucial to clearly define your goals and the metrics by which you’ll measure success.
  2. Choose Specialised AI Solutions:
    • The versatility of generalised AI can seem appealing, but opting for specialised, contextual AI solutions tailored to specific business challenges are more likely to deliver accurate and actionable insights with less cost and risk.
  3. Invest in Quality Data:
    • Relevant, high-quality data is necessary for successful AI implementations. Ensure your data is clean, organised, and accurate to real-world scenarios your AI solutions will encounter.

Measuring success of AI projects

From ABBYY’s perspective, the crux of measuring success of AI initiatives lies in the tangible impact they have on business processes, rather than just the technical metrics. Metrics like F-scores can provide useful insights into the performance of AI models, but they don’t necessarily translate to how effective they are in the real-world. Success metrics should always go back to how AI can enhance business operations.

The three main metrics we prioritise are those that reflect direct business value. These include:

  • Straight-Through Processing Rate (STPR): An increase in STPR means that more transactions or processes are being completed without manual intervention thanks to AI
  • Time Saved: Efficiency gains can be estimated by measuring the time saved by implementing AI solutions
  • Return on Investment (ROI): This captures the financial value from AI initiatives and demonstrates the cost-effectiveness and value add to the business. In 2023, an average of 57% respondents anticipated seeing at least twice the cost of investment ROI, while only 43% delivered this increase.

By focussing on these metrics, businesses can ensure their AI initiatives are delivering real value, driving process efficiency, and contributing to the bottom line. This approach can help businesses achieve meaningful enhancements in how they operate and deliver value.

Addressing the environmental impact of AI

Businesses will continue to grapple with the trade-off between generative AI capabilities and their ecological impact, such as immersive search capabilities that consume large amounts of energy. Using generative AI today to search and summarise data consumes 10 times the energy of a normal search, which is unsustainable in the global effort to reach an average planetary temperature of 1.5 degrees by 2025. There are alternative AI models that use robust machine learning and natural language processing with business rules for highly specified purposes; for example, in transportation and logistics, extracting data from the 44M bills of lading issued every year and processed by at least 9 stakeholders at 12 touchpoints with a highly accurate AI-model, trained on thousands of bills of lading.

The growing influence of regulation

As AI technologies continue to permeate various sectors, regulatory bodies will likely ramp up scrutiny to ensure ethical use and data privacy. This will also include measures to ensure that claims made by AI vendors are accurate and verifiable. These frameworks and regulations will sensitise users to the potential risks that shadow the possibilities and will bring business users back to the reality of integration challenges.

With more demand for transparency among businesses and regulators in AI decision-making, advancements in Explainable AI (XAI) will gain momentum, as it helps to demystify complex AI models and foster trust among users and stakeholders.

Embracing a human approach to AI

C-suite leaders have already begun to discover the hidden costs and ecological impact of generative AI, lifting the veil of hype to reveal practical challenges of integrating AI applications into their organisation’s infrastructure. Still, artificial intelligence has proven itself as a transformative tool that will be instrumental in modernising businesses and driving operational excellence.

In order to overcome these challenges, business leaders need to embrace a more human understanding of their data and processes. This involves bridging the gaps in understanding between AI teams and the business side of the organisations they serve. By fostering collaboration between AI specialists and professionals with actionable, hands-on business knowledge, enterprises can ensure that AI is driving operational excellence in the right areas and yielding truly actionable insight. Businesses need to carry this approach through impact assessments, strategising, implementation, and measuring success.

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Technology

The UK’s Cybersecurity Landscape: Key Trends and Challenges for 2025

By Christina Kemper, Vice President of International at Armis

Almost every single organisation, large or small, is acutely aware of the need to implement robust security measures. However, this is easier said than done. As the threat landscape continues to evolve, only heightened by tools such as AI, it can be difficult to stay ahead and ensure appropriate security measures are in place. Furthermore, there are a lot of security tools out there, and many organisations have tried to implement security measures and are now overwhelmed with an influx of information trying to figure out how best to manage it.

However, though it may not be the easiest task, it’s certainly one worth doing right. So, as we look ahead to 2025, what are the main trends that organisations need to be aware of and how can they use this knowledge to stay protected?

  1. Nation-state threats will worsen

The global geopolitical landscape is increasingly influencing the cyber threat environment. Nation-state actors, motivated by political or strategic goals, are launching more sophisticated cyberattacks which target critical infrastructure, government agencies and private enterprises. These attacks are often highly targeted and can have devastating consequences that disrupt society and economies.

In 2025, we can expect an uptick in cyberattacks from nation-state actors as global tensions rise. The UK, like many other countries, has already experienced the consequences of these kinds of attacks – and new technologies such as AI and quantum computing are only making things more complex. Just last month, UK minister, Pat McFadden, warned that Russia and other adversaries of the UK are attempting to use AI to enhance cyber-attacks against the nation’s infrastructure. Worryingly, however, over half (52%) of IT leaders in the UK do not believe the government can protect its citizens and organisations from cyberwarfare.

As we move into the new year, we will increasingly see nation-state attacks move away from the direct theft of sensitive information and focus more on destabilising economies, disrupting services, or causing widespread panic. When it comes to threats such as these, catching the early warning signs is vital. Organisations need to ensure they are using proactive measures to detect and prevent threats before they materialise.

  1. Supply chain attacks will continue to cause major disruption

For the last few years, it has become increasingly evident how vulnerable organisations are to supply chain attacks. Attacks on third-party vendors and partners have been responsible for some of the highest-profile breaches this year, such as the Synnovis and the Network Rail attacks. Additionally, the estimated global cost of supply chain attacks is expected to reach $60 billion in 2025.

As such, supply chain security is now a priority for many businesses, particularly as they depend more on external vendors for critical services and products. This broadens the scope of cybersecurity efforts beyond the organisation itself to include partners, suppliers, contractors and service providers. As such, organisations need to view their cybersecurity strategy holistically. It’s no longer enough to adopt a security posture that focuses solely on internal assets – businesses must extend their scope to the entire ecosystem.

  1. Regulatory compliance becomes more complex

The importance of regulatory compliance in cybersecurity has shifted from being a mere checkbox exercise to a fundamental aspect of any organisation’s strategy. And, with new regulations on the horizon, especially in the UK and Europe, businesses are now faced with even more stringent requirements.

For example, the EU’s Network and Information Systems Directive (NIS2) and Digital Operational Resilience Act (DORA) are pushing organisations to establish more robust cybersecurity frameworks. However, meeting these compliance requirements is not just about avoiding penalties. Organisations that invest in comprehensive cybersecurity programs, those that go beyond compliance and look to proactively protect against risks, are better positioned to maintain their reputation and trust among customers.

Additionally, as the number and complexity of regulatory frameworks continue to increase, the demand for compliance-as-a-service solutions – which help organisations navigate the complex landscape of local and international regulations – will increase. These services can offer businesses tailored solutions that simplify the process of ensuring adherence while also enhancing their overall cybersecurity posture.

4. Solution consolidation will be vital

Lastly, in response to the growing complexities of the threat and regulatory landscape, another trend we should expect to see in 2025 is the move toward single-platform solutions. Currently, organisations are heavily relying on point solutions designed to address specific security concerns, such as firewalls, anti-virus software and intrusion detection systems. However, as the threat landscape grows increasingly complex, the demand for integrated solutions will increase and it’s important that organisations have the ability to easily work through the influx of information that is out there with single-platform solutions.

Looking ahead

When it comes to cybersecurity, playing catch-up is not an option. In 2025, UK organisations need to ensure that they are staying one step ahead of bad actors. By being aware of the current trends in the threat landscape, businesses can make better-informed decisions regarding their cybersecurity posture. The threat landscape is always evolving, but organisations that stay informed, adopt a proactive cybersecurity approach, and make the most of the latest technologies will be far better positioned to protect themselves.

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Business

Wearable AI: How to supercharge adoption of consumer wearable devices 

By Kevin Brundish, CEO of LionVolt 

As we look toward the future, the global wearables market is projected to reach $265.4 billion by 2026. This growth is further fuelled by advancements in AI, which promise to enhance the functionality and performance of wearable devices. For instance, in the healthcare industry, artificial intelligence (AI) may use the massive volumes of data gathered by wearables to communicate with patients and offer precise diagnosis, advice and support.

Despite the remarkable features and capabilities of modern wearable devices, battery life remains a significant challenge. Most smartwatches, for example, still struggle to last a full 24 hours, making it difficult for users to monitor sleep patterns and daily activities continuously without frequent recharging. With the use of AI and applications that demand increasing amounts of data, this limitation prevents wearables from becoming fully integrated tools in our daily lives.

Advances in battery technology are looking to address this issue. At LionVolt we are working on a 3D lithium-metal anode technology which helps to significantly enhance lithium-ion battery performance.

Smaller Batteries, Same Energy 

The most significant advantage of lithium-metal anode batteries is their ability to provide the same energy from a smaller size battery. This gives designers greater freedom and opens new possibilities for wearable technology by enabling the miniaturisation of existing wearable designs. In addition, lithium-metal anodes may allow manufacturers to lower overall prices by moving away from costly cathode materials they use now, to cathode materials being used in automotive industry, where there is a cost advantage through economies of scale. 

Higher Energy Density and Faster Charging Times 

When we compare conventional lithium-ion batteries to lithium-metal anode battery technology, the lithium-metal anode batteries have a superior energy density. For users of wearable devices, this translates to longer usage periods and fewer charging interruptions as well as faster charge times, which minimises downtime and guarantees that gadgets remain operational when needed.

Enhanced User Experience 

Fast charging periods and increased energy density which is key to longer usage periods improve wearable technology’s overall performance, enabling consumers to maximise its benefits without sacrificing dependability or quality

Lithium-metal anode powered batteries also improve wearable gadgets’ dependability and durability. Users can count on their wearables to function reliably day or night and to enable a variety of applications, such as health monitoring and exercise tracking. These batteries are made to endure the demands of regular use, guaranteeing that gadgets continue to be reliable and operational for long stretches of time. 

The use of the highest performing materials in wearables typically comes at a high cost. However, with the advancement of new technology, it becomes possible to utilize more widely available and cost-effective anodes without compromising on performance. This approach allows for the efficient operation of wearables while also offering a cost benefit, addressing the economic challenges associated with high-performance materials.

Overcoming Adoption Barriers 

One of the key reasons for the slower adoption rate of consumer wearables is the charging rate. The utility of these products can be increased, along with their consumer appeal by extending their battery life and charging timeframes. The advantages of the next generation of batteries—faster charging, longer battery life, and improved device dependability—can greatly accelerate wearables’ uptake.  

Advancing Wearable Technology 

By tackling the crucial problem of battery duration, coupled with a fast charge capability, lithium-metal anode technology would propel the wearables business forward. An emphasis on sustainability and safety guarantees that these developments help both consumers and the environment, while our smaller, more efficient batteries provide designers the freedom to develop creative new gadgets. 

Transforming the Landscape of Wearable Technology

Lithium-metal anode battery technology brings numerous benefits to the consumer wearables sector: 

  • Longer Battery Life: Wearable devices will last much longer on a single charge, addressing a significant pain point for users. 
  • Increased Monitoring Time: Faster charging means users can monitor their health and activities for extended periods without interruption. 
  • Reduced Equipment Needs: With longer battery life and faster charging, users will need fewer duplicate products to cover charging times, simplifying their tech ecosystem.

Imagine being able to monitor your heart activity and more to manage health conditions without worrying if your device has enough power? With improved battery longevity, users can rely on their wearables for consistent health insights, making it easier to identify trends and make informed lifestyle changes. This seamless integration into daily life not only promotes better health management but also empowers users to take proactive steps towards their well-being.

These enhancements not only improve the user experience but also pose the potential to increase the adoption rate of consumer wearables.

Looking Ahead: Shaping the Future of Wearable Technology 

Wearables have a bright future because of AI and cutting-edge battery technology, which will greatly enhance their usability, dependability and functionality. The next generation of batteries are revolutionising the wearables market and paving the way for a new era of technological innovation by emphasising sustainability, increased energy density, quicker charging times, and improved safety features. 

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Business

The Future of Observability: Empowering businesses through data-driven transformation

 Karthik SJ, General Manager AI, LogicMonitor

The tech industry is at the cusp of a revolution, where digital transformation has shifted from aspiration to necessity. At its heart lies observability – a critical enabler for organisations navigating the complexity of modern IT infrastructures. Observability goes beyond monitoring systems or tracking performance; it transforms vast streams of system data into actionable insights that drive real-time decisions, improve operational efficiency, and ensure business resilience. 

Observability: The foundation of digital transformation

The digital transformation journey requires businesses to adopt a more sophisticated approach to managing their IT ecosystems. As organisations scale and evolve, they rely on a growing array of technologies, from cloud services to hybrid infrastructures, microservices, and containers. Parallel to increasing complexity, is a need for more granular visibility into system performance, security, and user experience.

This is where observability becomes essential, unlike traditional monitoring which typically tracks basic metrics like uptime and system health, observability provides a much deeper understanding of how systems are functioning and why. It enables businesses to not only detect issues but also diagnose the root causes, empowering data-driven decisions that improve performance across the organisation.

Converting raw data into insightful knowledge is vital in a world where companies need to function more quickly and efficiently. Beyond simply detecting issues, observability’s power lies in its ability to help organisations foresee problems before they cause operational disruptions. This proactive strategy helps businesses maintain uptime, optimise resources, and, ultimately, deliver superior customer experiences.

The rise of AI-powered observability

As organisations grapple with increasingly complex hybrid IT environments, AI-powered observability has emerged as a cornerstone of innovation. These solutions go beyond ensuring uptime-they provide actionable intelligence that enables businesses to optimise IT operations and address challenges proactively.  With 68% of organisations leveraging AI tools for anomaly detection, root cause analysis, and real-time threat detection, the demand for advanced observability tools is surging. This trend reflects a growing recognition that these tools are no longer just a technical necessity but a strategic enabler of business success. Observability empowers enterprises to stay ahead by driving efficiency, resilience, and adaptability in an ever-evolving digital landscape. 

The path ahead: The convergence of AI and observability

As we approach 2025, businesses harnessing AI-powered observability are poised to gain a significant competitive edge over those still relying on traditional monitoring solutions. This shift is underscored by the fact that 81% of enterprises plan to boost their AI investments in the coming year focusing on predictive analytics, automation, and anomaly detection to further optimise data centers and support AI-driven innovation. The integration of AI with observability is not just about identifying problems – it’s about enabling businesses to anticipate challenges, enhance operations, and sustain a competitive edge.

For LogicMonitor, the coming year is about driving innovation in an industry that’s evolving as fast as our customers’ needs. By working closely with our clients like TopGolf and Franke, we’re helping them navigate this transformation with confidence. As observability technology becomes increasingly essential, we’re committed to empowering businesses to thrive without being held back by technological limitations.

Observability’s ever-more-important role in 2025

As 2025 approaches, observability is set to become even more integral to IT operations, compliance, and innovation. Regulations like the EU’s Digital Operational Resilience Act (DORA) which mandates robust ICT risk management and incident reporting for financial services,highlight the critical need for continuous observability throughout the development cycle. This shift will accelerate the adoption of Observability-Driven Development (ODD), a strategic approach to managing the complexities in distributed systems and microservices architectures.

The expansion of observability is driven by the increasing necessity to monitor applications, infrastructure, and services across diverse and dynamic environments while staying resilient and improving customer experience. As data volumes grow, organisations will face increased scrutiny over observability spending, making it even more crucial that they align with regulation to enhance operational resilience and compliance. AI-powered observability systems will continuously learn from new data, user feedback, and past incidents, allowing them to improve over time and become more accurate and effective at identifying anomalies, reducing noise, and pinpointing root causes.

One thing is clear as the observability landscape develops further: businesses that make investments in cutting-edge, AI-powered observability solutions will be better prepared to meet tomorrow’s problems and thrive in the rapidly shifting digital economy.

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