Thought Leadership News

The Department for the Economy unveiled an ambitious plan to boost the Northern Ireland economy in 2021, but will it be up to scratch? Professor Anne Marie Ward, Dr Esmond Birnie and Dr Stuart Henderson crunch the numbers to find out if the 10x Economy vision can deliver. Some argue that the Northern Ireland (NI) economy has strong potential given its apparent unique trade position as a halfway house between Europe and Britain, combined with the Department for the Economy’s (DfE) ‘10x Economy’ policy, which targets innovation, inclusion and sustainability. Yet, despite experiencing 25 years of peace, NI continues to suffer from political uncertainty and lower economic productivity relative to Britain and the Republic of Ireland (ROI). Moreover, ongoing uncertainties associated with Brexit continue to dampen potential foreign direct investment, which has been vital to the strong economy in ROI. It is against this backdrop that the DfE introduced a new growth policy in May 2021 aimed at achieving a 10-times better economy (‘10x economy’) by 2030.  The 10x vision is underpinned by objectives grouped into three pillars—innovation, inclusive growth and sustainability—and focuses on six priority sectors:  1. Agricultural technology (agritech); 2. Life and health sciences; 3. Advanced manufacturing and engineering; 4. Financial services and financial technology (fintech); 5. Software (including cybersecurity); and 6. Screen and low carbon.   The data The Northern Ireland Economic Trade Statistics (NIETS) is a new dataset that provides details on trade between NI and Britain for the first time. We have analysed this dataset, which covers the period 2014–2020 and comprises a sample of enterprises that are VAT or PAYE registered and trade in NI.  Approximately 5,000 to 7,000 enterprises respond to the survey annually. As part of our research, we examined the 10x priority sectors over the period 2014–2020.  Data on financial services and fintech are not included in the dataset and due to GDPR issues, we had to merge some of the 10x priority areas, ending up with four 10x sectors:  • Agritech;  • Health and life sciences; • Advanced manufacturing (including low carbon); and  • Software and screen.  Approximately 11.4 percent of the total sample is classified as being 10x. Here is a summary of our findings. Growth in sales and gross value added (GVA) As shown in Table 1, the 10x sectors of the NI economy were relatively resilient from 2014–2020 as total Gross Value Added (GVA) increased over the period, though agritech was negatively impacted by COVID-19.  Performance of the non-10x sectors improved over the period 2014–2019, as evidenced by increased total GVA (except traditional manufacturing, which declined by 20.35%). Most non-10x sectors were adversely impacted by COVID-19, however, except manufacturing and ‘other’ production.  Productivity Productivity is measured by the ratio sales per employment and GVA per employment. As illustrated in Figure 1, for 2014–2020, the wholesale and retail sector had the highest sales per employment, followed by agritech and other production. Other production has the highest GVA per employment, followed by construction, health and life sciences and software and screen. Agritech has the second lowest GVA per employment. External sales behaviour A country’s wealth is influenced by its ability to attract funds from external markets. To determine how NI is doing, we investigated the trade behaviour of NI enterprises using four ratios, which reflect the percentage of overall sales each business undertakes with Britain, ROI, the rest of the European Union (REU) and the rest of the World (ROW). The average percentage for each year (2014–2020) for the whole sample is provided in Table 2.  The most important external market is Britain, accounting for on average 11.75 percent of sales, followed by ROI (6.18%), ROW (2.69%) and REU (1.74%). Generally, the percentage of total sales to these external markets increased steadily over the period 2014–2019 and declined in 2020, coinciding with COVID-19. Patterns in the percentage of total sales to the four markets are further analysed by sector over the period 2014–2020 in Figures 2 to 5. Sectoral differences are evident. Generally, non-10x enterprises (the six to the left-hand side of each figure) are less engaged with external markets relative to 10x enterprises (the four to the right-hand side of each figure).   Differences in the relative importance of markets is also observed across sectors. For example, the ROI market is most important to the agritech sector (Figure 3), and the ROW market is most important to the health and life sciences sector (Figure 5), probably indicative of sales to the US. This sector is also very active in markets in the REU (Figure 4).  Note: When interpreting these results, be aware that the data is based on the largest enterprises in NI and the authors had to design their own 10x categories based on Standard Industrial Classification codes.   Will it work? The number of enterprises in NI that can be classed as ‘10x’ increased over the period from 619 in 2014 to 723 in 2020. They are contributing GVA to the economy and, importantly, most of their turnover is to external markets, which is beneficial for a small regional economy where local demand is limited.  These enterprises seem to be resilient, with little change in behaviour observed in the period after Brexit, and, with the exception of agritech, they continued to grow despite COVID-19 (though the data was only available for 2020).  In theory, the DfE’s ambitions are laudable. Cluster approaches have proven successful in other countries, including ROI, where foreign-owned high-tech enterprises pay higher wages, invest in R&D for future growth and have high exports.  Moreover, the vision of sustainable growth and prosperity for all (levelling up) aligns with more holistic concepts of economic growth that account for social and environmental concerns alongside economic prosperity.  There are concerns, however. This is an ambitious undertaking that will take time to implement. The 2030 target set by the DfE is tight, the support structures to fuel 10x growth are not yet fully established, ‘10x’ is not yet fully defined, ‘place’ is not yet fully defined and hence the data are not (yet) available to enable 10x to be identified and analysed by place.  This will hinder the ability to foster clusters and build networks, which are important for innovation. Also, change will be difficult due to existing established structures.  For example, most policy and government action is managed through Local Government Department (LGD) level structures. However, clusters of enterprises may cross LGD boundaries, complicating a joined-up approach.  In addition, economic and social development is not only managed by the DfE; many other bodies such as central government and local government departments, business networks and educational establishments, are involved. Role for accountants Accountants can play an important role in the success of the DfE’s policy and the future of the NI economy. Accountancy firms are present in most towns across the region. Accountants are part of local business networks and have first-hand knowledge of entrepreneurship and innovation within communities.  Moreover, accountants are well-equipped to facilitate the creation of priority clusters and expanding networks that enable local businesses to connect and grow both within and beyond their communities. This will be good for communities and for the accountancy profession.   *Note: The tables and diagrams in this article are from the authors’ full report, available on the Northern Ireland Statistics and Research Agency website. Professor Anne Marie Ward, FCA, is Professor of Accounting at Ulster University; Dr Esmond Birnie is Senior Economist at Ulster University; and Dr Stuart Henderson is a Lecturer in Financial Services at Ulster University.

The explosive growth of AI has transformative potential but also raises critical privacy concerns that must be addressed, writes Pat Moran The world of artificial intelligence (AI) took a massive leap forward with the emergence of ChatGPT in November 2022. Since then, there has been a surge in the design and implementation of AI use cases across industries such as healthcare, retail, financial services, manufacturing and others. While the emergence of AI is transformative, this powerful tool is not without its challenges, particularly the profound privacy concerns it raises. As organisations eagerly harness the potential of AI, it is vital to know the associated privacy risks, such as: Data collection and breaches – As AI models evolve, their training datasets will likely grow, increasing the risk of personal and special category data being included. These datasets must be stored and processed securely while training AI systems. Algorithmic bias and discrimination – Biased algorithms may inadvertently perpetuate biases and lead to decisions that could negatively impact certain groups of people without the organisation’s intention to discriminate. Data subject requests – Once the AI systems are trained and deployed, responding to certain data subject requests becomes increasingly difficult. Transparency – As AI systems become commonplace in organisations, users will increasingly unknowingly interact with these systems, including instances where users are affected by automated decision-making. Regulatory requirements and industry standards – Even though AI is considered a novel technology, there are existing and upcoming regulations and standards that define and guide its usage. Organisations must demonstrate compliance with these regulations and standards to maintain customer trust and meet procurement standards in the market. Misuse of personal data in AI-enabled cyberattacks – Malicious actors have begun leveraging personal data such as audio clips and deep-fake content for advanced phishing attempts and other scams. Inaccurate responses – It is common for generative AI programs to respond based on probabilities identified within the data sets used to train the AI instead of actual, accurate data points. This can result in inaccurate responses and may cause issues if users do not verify the authenticity of the system’s responses. Organisational changes for AI To successfully traverse the concerns listed above while developing and integrating AI systems, organisations should consider the following best practices: AI governance: The teams involved in developing AI governance should be interdisciplinary, including teams in AI development, legal, privacy, information security, customer success and others. Privacy by design: The foundation of responsible AI lies in the concept of ‘privacy by design’, which states that data protection and privacy considerations must be implemented throughout the development lifecycle for any AI system. This includes incorporating privacy-enhancing technologies, ensuring appropriate security, compliance with regulatory requirements and other privacy-specific principles. Some AI systems have a ‘black box’-like nature, which makes it harder to detect and fix ethical, privacy and regulatory issues once deployed, increasing the need for privacy by design. Further, there might be other processes that pose too high a risk to move towards automation through AI and will require controls such as “a human in the loop”. Transparency: Users must be provided with clear and transparent communication in the form of privacy notices and other means including: confirmation that AI systems are used to process their data (including details of automated decision-making, if present); how their data is collected and processed; how long it will be stored; an outline of their rights, etc. The information helps users provide informed consent and builds trust in AI systems as well as the organisation. Fairness: An important step is to perform regular audits of AI systems to test their performance and ensure no bias or discrimination against users. The review should include the automated decision-making algorithm, and the process by which the algorithm makes decisions should be transparent and explainable. Data management: Ensure data ingested by the AI system during training is lawfully obtained, high-quality, and rigorous vetting and anonymisation have been performed. Technologies such as pseudonymisation or data aggregation should be implemented to ensure compliance with data minimisation and retention privacy principles. Up-to-date records of processing activities should also be maintained to ensure data is managed effectively throughout its lifecycle. Remember, organisations cannot use publicly available data to train AI systems without a valid lawful basis. Risk management, compliance and information security: A risk-based approach, including a data protection impact assessment, should be implemented to assess the level of risk involved before AI systems are deployed. The organisation should also sign off on the risk levels, controls and mitigations. AI compliance monitoring should be incorporated into the organisational, regulatory compliance programme or privacy programme. The wider organisational information security programme should include AI systems and their underlying data to prevent data breaches and malicious attacks. Technical and organisational measures such as encryption, data masking, password management, access controls and network security should be implemented. Employee training: As AI is a new technology, employees must be trained periodically on responsible AI usage. Training should include the privacy impact of AI systems, compliance with data protection regulations while using AI, misuse of personal data in AI-enabled cyberattacks and how to guard against it, and data protection best practices. Conclusion The advent of AI may be compared to the invention of the combustion engine. While organisations can move faster, they will also require stronger brakes. These brakes may address these multifaceted concerns, which necessitates a holistic approach, combining technological innovation, ethical practices, user empowerment and regulatory adherence. Organisations’ responsibility will be to innovate and ensure that innovation aligns with the values of privacy, ethics and user trust. Pat Moran is the Leader of Cybersecurity Practice at PwC.

In the complex landscape of corporate decision-making, understanding the differences between ESG and sustainability is crucial, writes Dan Byrne Corporate decision-making today involves a lot of talk about the environment, social and governance (ESG) and sustainability – precisely, how your company will fit into both movements. No one wants to discover they don’t know the difference between the two in the middle of a board meeting. While the two ideas share a lot of overlapping principles, they are different. It is essential to understand these difference because, once you sit down with colleagues to oversee core strategic decisions, you must have robust knowledge about the relevant topics. The difference between ESG and sustainability Sustainability is a principle dictating that, while we must look after the needs of our current society, it cannot be to the detriment of future generations. The concept of sustainability is so broad that it inevitably means different things in different boardrooms. The common thread in most organisations is that sustainability principles guide stakeholder expectations and, as a result, company strategy. ESG isn’t a principle; it’s a framework for measuring specific impacts and risks. It is a tool that can help investors and stakeholders to understand where their money is going. Why the confusion? There is a lot of overlap between ESG and sustainability, so organisations often file them under the same heading. In practice, companies embracing ESG will often commit to not harming the planet (environment), its people (social) or themselves (governance). While this should always be approached with the understanding that ESG is an investment metric and tool for analysing risk, it can be easy to generalise to the point that ESG is instead viewed as a sustainability metric or simply another name for sustainability itself. This is particularly true when companies focus on the “E” part of ESG. It’s popular across multiple industries and wins the backing of key stakeholder groups. An organisation’s focus on the environment creates a natural overlap with sustainability activities. Avoiding confusion in the future If you are in a board meeting and find yourself hovering around both topics, be sure not to hint that they’re the same with these tips: Remember that ESG is a collection of metrics; sustainability is a principle; If you’re talking about ESG, you will likely end up talking about numbers, quantities, reporting and investment opportunities. If you’re talking about sustainability, it’s expected more in the context of organisational goals, culture and policies; and Sustainability, in many respects, is the end goal. ESG is a pathway and a framework that will allow you to get there. Dan Byrne is a writer with the Corporate Governance Institute