Explore How AI Trends Will Shape Tomorrow in our Tech Trends 2024 series, where experts share insights into the transformative impact of AI on the tech and business landscape. Shape your strategy with the combined wisdom of our experts, gleaned from helping tech leaders develop clear AI strategies and roadmaps across the enterprise.

 

Navigating AI Tech Trends Across the Enterprise

 

With Our Experts’ Views:

 

• Ben Jarvis: Identifying AI Use Cases
• Elton Nitschke: AI Enhancing Human Thinking and Work
• Ed Tucker: Elevating Cyber security with AI Advancements

 

 

Identifying AI Opportunities Beyond the Hype:

 

Ben Jarvis: CTO, Data & AI, Telefónica Tech UK&I – The initial excitement surrounding generative AI has given way to a more measured approach. Tools like Azure OpenAI Service and Copilot, now widely available, are set to increase AI adoption across sectors. This shift opens doors for organisations to leverage generative AI tools across diverse domains, from content creation to software development.

 

1. Tailored AI Integration Strategies:

 

Certain sectors are already witnessing advanced AI integration within operations, boosting productivity and efficiency. Examples include using AI for diagnostics in healthcare to optimise processes and enhancing worker value in manufacturing.

 

Customising industry-specific AI integration strategies is crucial to effectively address challenges and align with organisational goals while complying with regulations.

 

2. Seamless AI Integration into Daily Tasks:

 

In the future, AI will become more seamlessly integrated into daily tasks, augmenting human capabilities rather than replacing them. Some of the most compelling current AI use cases we are seeing include:

 

• Intelligent Document Search: Provide users with an interface to delve into highly technical documentation, asking questions easily.
• Bid Management: Automate the bid management process, identifying responses to detailed questions posed in RFPs.
• Automated Quality Assurance: Utilise image processing in construction to verify if a building adheres to the correct standards.
• Supply Chain Optimisation: Aggregate data from diverse sources to construct a comprehensive view of the end-to-end supply chain, pinpointing areas for optimisation.

 

3. Gaining Value from Data:

 

Simultaneously, amidst these AI developments, the hype surrounding Big Data is subsiding. Organisations need to claw back tangible value from their substantial data investments. Expect to see an increased focus on ensuring data assets produce business value and align with clear outcomes. Attention to data quality has heightened, leading to trends like data observability and the rise of DataOps practices.

 

4. Shift from Platforms to Ecosystems:

 

Data experts, like engineers and scientists, now prefer simple solutions without complex infrastructure. Trends like data fabric consolidate tools into user-friendly platforms, such as Microsoft Fabric. This makes it easy for individuals to access tools efficiently for quick results and faster value.

 

5. Key Considerations for AI Adoption:

 

• Baseline Platform/Ecosystem: Establish a baseline data platform to meet ongoing needs, with initiatives such as our Accelerators Service, facilitating best practice setup and faster time-to-value.
• Robust Data Governance/Management: Implement a robust data governance process, managing both people and processes. Tools like Purview and CluedIn offer technical capabilities to ensure proper data discovery, cataloguing, integration, and maintenance.
• Strategic Planning/Adoption: While the technology itself is relatively straightforward, strategic planning is more complex but crucial. Enlisting the support of external experts such as Telefónica Tech can help avoid common pitfalls and add long-term value to the AI adoption process.

 

 

Effective AI adoption involves balancing risks and rewards. Rigorous testing is critical to mitigate biases in AI models, and regularly updating models is essential to adapt to changing scenarios.

 

 

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AI Enhancing Human Productivity:

 

Elton Nitschke: CTO Modern Workplace, Telefónica Tech UK&I – AI is poised to revolutionise the modern workplace by enhancing productivity and streamlining tasks. This transformative shift enables humans to focus on creativity and complex decision-making, thereby significantly boosting innovation as well as employee well-being.

 

1. Transparency and Ethical AI Implementation:

 

As AI becomes an integral part of the workplace, leaders must prioritise transparency in AI implementation. Robust data governance policies and continuous audits of AI algorithms are vital to ensure fair and equitable AI-driven decisions within the workplace. It’s worth noting Gartner predict by 2025, 70% of support requests initiated through GenAI-powered chatbots will demand human oversight due to customers’ mistrust, increasing service costs by 40%.

 

2. AI-Driven Technologies Impact:

 

The impact of AI-driven technologies, such as natural language processing (NLP), robotic process automation (RPA), and predictive analytics, is significant. NLP facilitates efficient communication, RPA streamlines repetitive tasks, and predictive analytics provides actionable insights. This combined effect enhances efficiency and fosters innovation through data-driven decision-making. Moreover, it contributes to improved employee well-being by allowing a more focused approach to meaningful and challenging tasks.

 

3. Comprehensive AI Workforce Training:

 

Job roles will evolve with AI, focusing more on tasks requiring creativity, problem-solving, and emotional intelligence, while routine tasks get automated. Leaders must prioritise comprehensive workforce training to ensure employees can effectively use AI tools. Fostering a culture of continuous learning and adaptation is key to maximising the benefits of AI in the workplace.

 

4. Change Management for Smooth Integration:

 

Effective change management strategies are crucial for the smooth integration of AI into existing workflows. Addressing concerns and uncertainties about AI’s role, emphasising its augmentation rather than replacement of human roles, will help to ensure seamless integration. This involves understanding existing workflows, encouraging user and employee feedback, and ensuring interoperability with existing systems.

 

 

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Elevating Cyber security with AI Advancements:

 

Ed Tucker: CTO Cyber Security, Telefónica Tech UK&I – Within the broader AI domain, machine learning has long played a crucial role in cyber security. The anticipated advancements in 2024 present an opportunity to further enhance defensive capabilities. Although still in its early adoption phase, the application of AI within security operations and threat intelligence is expected to blossom, highlighting its potential to fortify the cyber security landscape.

 

1. Phased AI Integration Approach:

 

Organisations adopting a phased approach to AI integration, with thorough testing and validation, will position themselves well for ongoing developments. A strategic, hypothesis-driven approach, akin to the adoption of previous technologies like big data and machine learning, is essential to avoid disillusionment and ensure a positive return on investment.

 

2. Next Stage AI in Cyber Security:

 

Cyber security, having embraced early AI capabilities, is now at the dawn of the next stage. Machine learning has been integral to threat detection and Endpoint Detection and Response capabilities. The next stage of AI adoption requires a similar mindset and approach as with the early adoption of ML. While the compelling use cases will take time to prove, careful integration into existing capabilities could see significant developments in 2024.

 

3. Cyber Areas Ripe for AI Adoption:

 

We’re seeing early signs of improved detective capabilities using AI in security operations and threat intelligence. These advancements are ready to be embraced, especially in data-heavy areas. Integrating AI with additional sources, making inferences, and building confidence in decision-making is essential. While these changes won’t happen overnight, integrating them carefully into existing cyber security solutions and capabilities could lead to significant progress in the year ahead.

 

4. AI TRiSM Framework for Decision-making:

 

AI TRiSM simplifies decision-making by consolidating common aspects into a unified AI framework. Each component is fundamental to business decision-making and, when applied together, enhances risk management. Utilising TRiSM consistently, especially under stress, is crucial. Incorporating these elements upfront can significantly improve the foundations of risk management with the accelerated capabilities of AI.

 

5. Navigating Cyber Security Hype:

 

There is, as ever, much hype in Cyber Security as we head into 2024 and beyond. The hype is valid; however, the key lies in assessing hype with a healthy dose of reality. For example, if you are more concerned by AI-generated threats rather than opportunities, then you are arguably approaching the problem from the wrong end.

 

AI represents a real potential for a step change in Cyber Security capabilities and defensive effectiveness, but only for those organisations in a ready position to adopt. For others, ensuring fundamental security measures are in place, such as having no Remote Desktop ports open to the internet, takes precedence.

 

 

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Conclusion:

 

Tim Richardson, VP&CTO

 

Before embarking on your AI journey, strategic insight and sector-specific considerations are crucial. Beyond the hype, identifying use cases that deliver real value is paramount. Whether maximising productivity, optimising operations, or elevating cyber security, success lies in an analytical, iterative, and business-value-focused approach. The future is AI-shaped – but you need to navigate it strategically, collaboratively, and with a realistic outlook.