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Replaced, Reskilled or Redesigned? How Will AI Impact Job Security?

Helen Starling, CEO of Paragon Workplace Solutions

We are living in the middle of a technological revolution, where artificial intelligence (AI) has emerged as a dominant force. Fuelled by the exponential growth of data, computing power and machine learning, the AI industry has the potential to reshape the very fabric of our societies, economies and industries. The industry is expected to be worth £321.83 billion by 2027. AI advancements and applications are sweeping across the globe, threatening to disrupt our industries and ways of working as we know them, but it creates opportunity – and organisations are facing an unprecedented challenge:

How can my business harness the power of AI while maintaining control, ensuring accountability, and developing the talent needed to thrive in this new world?

The truth is, that this shift will require businesses to adapt, innovate, and reimagine the future of work, training, and leadership. This is not just a technological shift; it’s cultural, economic, and societal, demanding a fundamental change in how we collaborate and think. The winners will be those who master the art of control, monitoring, and talent development to unlock the full potential of AI and ensure success in the years to come.

Today, businesses can truly harness the power of AI within their operations in the quality and assurance space. However, the application of AI should be approached cautiously considering the lack of insight and regulation on how public data sets are composed and which data sources are utilised. This is a big concern, considering the copyright and infosec requirements many organisations must uphold. I believe the true winners in the AI space will be organisations that give AI the focus it deserves and train internal AI models on closed data sets that mirror the intricacies of their operations.

Understanding the Current State of AI Adoption

On the 30th of November 2022, the birth of Chat GPT marked a significant milestone for AI. Nearly two years later, it has transitioned from being a novelty to a tangible reality, with 85% of businesses envisioning their companies becoming AI-driven by 2028. What does this mean? AI is now being leveraged to drive business value, improve operational efficiency and unlock new revenue streams. Its evolutionary journey closely mirrors that of computers but on a dramatically accelerated timeline. 

As the pace of progress accelerates, the ever-expanding capabilities of state-of-the-art AI models will garner more attention from businesses, similar to the way they did with computers. However, despite this promising start, many businesses are still struggling to move beyond the pilot phase when it comes to AI. This is largely due to challenges such as a lack of AI talent, inadequate data infrastructure, and unclear metrics for measuring return on investment. Moreover, the AI landscape is also marked by a growing digital divide. Some countries and regions, such as the EU, are forging ahead on implementation and regulation while others lag behind. Understanding these nuances and disparities is vital for businesses seeking to navigate the complexities of AI adoption and stay ahead of the curve.

As they do with digital transformation initiatives, organisations need to stay close to their expertise and the specific solutions they provide to the market, and apply AI models to enhance the experience of their products and services. They must also avoid the pitfall of thinking AI is a strategy to solve all their problems. AI has great power to enhance the experience of our products and services, but without succinct and clear application of the problem statements we wish to solve, we run the risk that the disruptive power of AI might overshadow its value.

The Future of Work and How AI Will Change the Way We Operate

What jobs will be replaced? How will my job and my industry change? When it comes to AI, there are still going to be many unanswered questions – and this is simply because we are evolving alongside it. However, one thing is certain: the future of work will be drastically different and this change is coming hard and fast. AI will not only augment business capabilities but also fundamentally transform the way we work together. We are on the precipice of an AI-driven workforce that will be characterised by unprecedented levels of automation, efficiency, and precision. Gone are the days of mundane, repetitive tasks. AI-powered bots and algorithms will take over the grunt work, freeing humans to focus on high-value, strategic decision-making. This should be seen as a positive – creative and more “human” work leads to further innovation. 

Recruiters and future candidates should remember this: The most sought-after talent will be those who can seamlessly integrate human intuition with AI skills, creating a symbiotic relationship that unlocks new possibilities for business growth, increasing salaries by up to 31%, accelerating career prospects, redefining all job roles, and creating new job categories. All are born from the intersection of human creativity and machine intelligence.

Building an AI-Ready Workforce.     

As AI continues to transform the modern workplace, it’s essential to recognise that the most significant challenge lies not in the technology itself, but in the people working alongside it. This will be a huge undertaking for businesses, considering a recent AWS report found that only 50% of employers provide adequate support for AI skills training for their workforce. An AI-ready organisation demands a workforce that is not only comfortable with change but also equipped with the skills to navigate and implement AI solutions.

Building this workforce requires a deliberate and strategic approach to upskilling and reskilling. It will involve identifying the critical skills gaps that exist within an organisation and creating targeted training programmes that address these deficiencies. It is no longer sufficient to simply possess technical expertise; employees must be able to work collaboratively with AI systems, understand the nuances of data-driven decision-making, and possess the creativity and adaptability to thrive in an environment of rapid change.

The future of business can be written by the power of AI. Only those businesses which master control and monitoring, invest in the right talent, and apply their AI models with clear problem statements in mind will be able to unlock the full potential of AI and stay ahead of the curve. The key to success lies not in resisting the change, but in embracing it and harnessing the transformative power of AI to drive prosperity for our own businesses and our clients.

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Driving business success in today’s data-driven world through data governance

Source: Finance derivative

Andrew Abraham, Global Managing Director, Data Quality, Experian

It’s a well-known fact that we are living through a period of digital transformation, where new technology is revolutionising how we live, learn, and work. However, what this has also led to is a significant increase in data. This data holds immense value, yet many businesses across all sectors struggle to manage it effectively. They often face challenges such as fragmented data silos or lack the expertise and resources to leverage their datasets to the fullest.

As a result, data governance has become an essential topic for executives and industry leaders. In a data-driven world, its importance cannot be overstated. Combine that with governments and regulatory bodies rightly stepping up oversight of the digital world to protect citizens’ private and personal data. This has resulted in businesses also having to comply e with several statutes more accurately and frequently.

We recently conducted some research to gauge businesses’ attitudes toward data governance in today’s economy. The findings are not surprising: 83% of those surveyed acknowledged that data governance should no longer be an afterthought and could give them a strategic advantage. This is especially true for gaining a competitive edge, improving service delivery, and ensuring robust compliance and security measures.

However, the research also showed that businesses face inherent obstacles, including difficulties in integration and scalability and poor data quality, when it comes to managing data effectively and responsibly throughout its lifecycle.

So, what are the three fundamental steps to ensure effective data governance?

Regularly reviewing Data Governance approaches and policies

Understanding your whole data estate, having clarity about who owns the data, and implementing rules to govern its use means being able to assess whether you can operate efficiently and identify where to drive operational improvements. To do that effectively, you need the right data governance framework. Implementing a robust data governance framework will allow businesses to ensure their data is fit for purpose, improves accuracy, and mitigates the detrimental impact of data silos.

The research also found that data governance approaches are typically reviewed annually (46%), with another 47% reviewing it more frequently. Whilst the specific timeframe differs for each business, they should review policies more frequently than annually. Interestingly, 6% of companies surveyed in our research have it under continual review.

Assembling the right team

A strong team is crucial for effective cross-departmental data governance.  

The research identified that almost three-quarters of organisations, particularly in the healthcare industry, are managing data governance in-house. Nearly half of the businesses surveyed had already established dedicated data governance teams to oversee daily operations and mitigate potential security risks.

This strategic investment highlights the proactive approach to enhancing data practices to achieve a competitive edge and improve their financial performance. The emphasis on organisational focus highlights the pivotal role of dedicated teams in upholding data integrity and compliance standards.

Choose data governance investments wisely

With AI changing how businesses are run and being seen as a critical differentiator, nearly three-quarters of our research said data governance is the cornerstone to better AI. Why? Effective data governance is essential for optimising AI capabilities, improving data quality, automated access control, metadata management, data security, and integration.

In addition, almost every business surveyed said it will invest in its data governance approaches in the next two years. This includes investing in high-quality technologies and tools and improving data literacy and skills internally.  

Regarding automation, the research showed that under half currently use automated tools or technologies for data governance; 48% are exploring options, and 15% said they have no plans.

This shows us a clear appetite for data governance investment, particularly in automated tools and new technologies. These investments also reflect a proactive stance in adapting to technological changes and ensuring robust data management practices that support innovation and sustainable growth.

Looking ahead

Ultimately, the research showed that 86% of businesses recognised the growing importance of data governance over the next five years. This indicates that effective data governance will only increase its importance in navigating digital transformation and regulatory demands.

This means businesses must address challenges like integrating governance into operations, improving data quality, ensuring scalability, and keeping pace with evolving technology to mitigate risks such as compliance failures, security breaches, and data integrity issues.

Embracing automation will also streamline data governance processes, allowing organisations to enhance compliance, strengthen security measures, and boost operational efficiency. By investing strategically in these areas, businesses can gain a competitive advantage, thrive in a data-driven landscape, and effectively manage emerging risks.

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The Benefits of EV Salary Sacrifice: A Guide for Employers and Employees

As the UK government continues to push for greener initiatives, electric cars have become increasingly popular. The main attraction for both employers and employees is the EV salary sacrifice scheme.

By participating in an EV salary sacrifice scheme, both employers and employees can enjoy cost savings and contribute to environmental sustainability along the way! This article will delve into the specifics of how these schemes operate, the financial advantages they offer, and the broader positive impacts on sustainability.

We will provide a comprehensive overview of the mechanics behind EV salary sacrifice schemes and discuss the various ways in which they benefit both employees and employers, ultimately supporting the transition to a greener future in the UK.

What is an EV Salary Sacrifice Scheme?

An EV salary sacrifice scheme is a flexible financial arrangement that permits employees to lease an EV through their employer. The key feature of this scheme is that the leasing cost is deducted directly from the employee’s gross salary before tax and National Insurance contributions are applied. By reducing the taxable income, employees can benefit from substantial savings on both tax and National Insurance payments. This arrangement not only makes EVs more affordable for employees but also aligns with governmental incentives to reduce carbon emissions.

For employers, implementing an EV salary sacrifice scheme can lead to cost efficiencies as well. The reduction in National Insurance contributions on the employee’s reduced gross salary can offset some of the costs associated with administering the scheme. Additionally, such programmes can enhance the overall benefits package offered by the employer, making the company more attractive to prospective and current employees.

Benefits for Employees

1. Tax and National Insurance Savings

By opting for an EV salary sacrifice scheme, employees can benefit from reduced tax and National Insurance contributions. Since the lease payments are made from the gross salary, the taxable income decreases, resulting in substantial savings.

2. Access to Premium EVs

Leading salary sacrifice car schemes often provide access to high-end electric vehicles that might be otherwise unaffordable. Employees can enjoy the latest EV models with advanced features, contributing to a more enjoyable and environmentally friendly driving experience.

3. Lower Running Costs

Electric vehicles typically have lower running costs compared to traditional petrol or diesel cars. With savings on fuel, reduced maintenance costs, and exemptions from certain charges (such as London’s Congestion Charge), employees can enjoy significant long-term financial benefits.

4. Environmental Impact

Driving an electric vehicle reduces the carbon footprint and supports the UK’s goal of achieving net-zero emissions by 2050. Employees can take pride in contributing to a cleaner environment.

Benefits for Employers

1. Attract and Retain Talent

Offering an EV salary sacrifice scheme can enhance an employer’s benefits package, making it more attractive to potential recruits. It also helps in retaining current employees by providing them with valuable and cost-effective benefits.

2. Cost Neutrality

For employers, EV salary sacrifice schemes are often cost-neutral. The savings on National Insurance contributions can offset the administrative costs of running the scheme, making it an economically viable option.

3. Corporate Social Responsibility (CSR)

Implementing an EV salary sacrifice scheme demonstrates a commitment to sustainability and corporate social responsibility. This can improve the company’s public image and align with broader environmental goals.

4. Employee Well-being

Providing employees with a cost-effective means to drive electric vehicles can contribute to their overall well-being. With lower running costs and the convenience of driving a new EV, employees may experience reduced financial stress and increased job satisfaction.

How to Implement an EV Salary Sacrifice Scheme

1. Assess Feasibility

Evaluate whether an EV salary sacrifice scheme is feasible for your organisation. Consider the number of interested employees, potential cost savings, and administrative requirements.

2. Choose a Provider

Select a reputable provider that offers a range of electric vehicles and comprehensive support services. Ensure they can handle the administrative tasks and provide a seamless experience for both the employer and employees.

3. Communicate the Benefits

Educate your employees about the advantages of the scheme. Highlight the financial savings, environmental impact, and access to premium EV models. Provide clear guidance on how they can participate in the programme.

4. Monitor and Review

Regularly review the scheme’s performance to ensure it continues to meet the needs of your employees and the organisation. Gather feedback and make adjustments as necessary to enhance the programme’s effectiveness.


The EV salary sacrifice scheme offers a win-win situation for both employers and employees in the UK. With significant financial savings, access to premium vehicles, and a positive environmental impact, it’s an attractive option for forward-thinking organisations. By implementing such a scheme, employers can demonstrate their commitment to sustainability and employee well-being, while employees can enjoy the benefits of driving an electric vehicle at a reduced cost.

Adopting an EV salary sacrifice scheme is a step towards a greener, more sustainable future for everyone.

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Machine Learning Interpretability for Enhanced Cyber-Threat Attribution

Source: Finance Derivative

By: Dr. Farshad Badie,  Dean of the Faculty of Computer Science and Informatics, Berlin School of Business and Innovation

This editorial explores the crucial role of machine learning (ML) in cyber-threat attribution (CTA) and emphasises the importance of interpretable models for effective attribution.

The Challenge of Cyber-Threat Attribution

Identifying the source of cyberattacks is a complex task due to the tactics employed by threat actors, including:

  • Routing attacks through proxies: Attackers hide their identities by using intermediary servers.
  • Planting false flags: Misleading information is used to divert investigators towards the wrong culprit.
  • Adapting tactics: Threat actors constantly modify their methods to evade detection.

These challenges necessitate accurate and actionable attribution for:

  • Enhanced cybersecurity defences: Understanding attacker strategies enables proactive defence mechanisms.
  • Effective incident response: Swift attribution facilitates containment, damage minimisation, and speedy recovery.
  • Establishing accountability: Identifying attackers deters malicious activities and upholds international norms.

Machine Learning to the Rescue

Traditional machine learning models have laid the foundation, but the evolving cyber threat landscape demands more sophisticated approaches. Deep learning and artificial neural networks hold promise for uncovering hidden patterns and anomalies. However, a key consideration is interpretability.

The Power of Interpretability

Effective attribution requires models that not only deliver precise results but also make them understandable to cybersecurity experts. Interpretability ensures:

  • Transparency: Attribution decisions are not shrouded in complexity but are clear and actionable.
  • Actionable intelligence: Experts can not only detect threats but also understand the “why” behind them.
  • Improved defences: Insights gained from interpretable models inform future defence strategies.

Finding the Right Balance

The ideal model balances accuracy and interpretability. A highly accurate but opaque model hinders understanding, while a readily interpretable but less accurate model provides limited value. Selecting the appropriate model depends on the specific needs of each attribution case.

Interpretability Techniques

Several techniques enhance the interpretability of ML models for cyber-threat attribution:

  • Feature Importance Analysis: Identifies the input data aspects most influential in the model’s decisions, allowing experts to prioritise investigations.
  • Local Interpretability: Explains the model’s predictions for individual instances, revealing why a specific attribution was made.
  • Rule-based Models: Provide clear guidelines for determining the source of cyber threats, promoting transparency and easy understanding.

Challenges and the Path Forward

The lack of transparency in complex ML models hinders their practical application. Explainable AI, a field dedicated to making models more transparent, holds the key to fostering trust and collaboration between human and machine learning. Researchers are continuously refining interpretability techniques, with the ultimate goal being a balance between model power and decision-making transparency.

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