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The AI revolution is well underway, driving unparalleled progress in business and finance. Microsoft Ireland CFO Áine Nolan shares her experiences and insights Artificial intelligence (AI) represents a valuable opportunity for Ireland to enhance our productivity and solidify our digital leadership in Europe.  This is according to Áine Nolan, FCA and Chief Financial Officer with Microsoft Ireland, who spoke at the recent Chartered Accountants Ireland Technology Conference, about how AI is revolutionising the finance function and driving unprecedented efficiencies.  “We have a thriving tech scene in Ireland, a highly educated workforce and really smart government policies. We can really become a hub for AI advancements,” Nolan said. The potential is significant, driven by the rapid emergence of AI as a commercial proposition and its popularity with users in both their lives and work. “The rate of AI adoption currently is somewhat unprecedented,” said Nolan. “Generative AI is capturing, distributing and democratising intelligence for everyone and that is a powerful concept.”  AI uptake in Ireland Microsoft Ireland recently partnered with Trinity College Dublin Business School to conduct research into the uptake of generative AI in the Irish market. Published in March 2024, the Generative AI in Ireland 2024 report found that 49 percent of respondents were already using the technology in some form in their organisation. “This research is less than a year old and already out of date, which just shows you how fast the rate of uptake is. We are due to release new research in March this year, which shows that adoption rates have since risen to about 70 percent,” Nolan said. Despite its proliferation in Irish workplaces, not all employers are, as yet, fully equipped to manage the implications of the AI age. “Through our research with Trinity, we have found that employees are bringing their own AI to work, with or without their employer’s consent. This ‘shadow’ gen AI culture creates risks for employers who really need to have guardrails in place,” Nolan said. “There is also sometimes a view that AI is just an add-on productivity tool you can slot into your existing workflows, but this fundamentally underestimates the magnitude of behavioural change and organisational transformation needed to unlock its value.” It will take time and a great deal of change management to integrate AI successfully as a new dimension of work, Nolan said. “People often make the mistake of simply asking how they can apply AI to their existing processes, but, fundamentally, they should be asking what they need AI to do and how it can make their processes more efficient or facilitate innovation—even creating a new service for our customers, for example.” The dawn of the AI agent Although many people currently use AI as a kind of “virtual assistant”, helping with everyday tasks, such as organising their work calendar or automating note-taking during meetings, the technology is set to assume a far more prominent and proactive role. “In the future, AI will operate on your behalf—as an agent—allowing you to eliminate tasks from your plate altogether,” Nolan explained. “This might mean making autonomous decisions for your IT helpdesk and, eventually, managing your full device refresh, from examining your POs right through to ordering new devices, checking your budgets and getting the necessary human approvals at the end of the process.  “A more complex example might involve AI looking after lead generation for your business by sourcing and emailing potential customers or acting as a customer support agent in a much more complex way than a chatbot, where it is actually making decisions on behalf of your organisation.” Microsoft and AI in finance Already, Nolan and her finance team at Microsoft Ireland are reaping the benefits of the organisation-wide implementation of the software giant’s AI technology. “Our global CFO Amy Hood consistently challenges our finance team to use our own technology to improve our processes. Her mantra is really clear—by adopting innovative technologies, finance will strengthen its business leadership through compliance, accuracy and efficiency.” And, as CFOs across all organisations assume an increasingly strategic business role, AI will become even more fundamental to their work day-to-day. “The role of the CFO is changing rapidly and, as finance leaders, we need to play a lead role in developing a clear AI strategy, ensuring our organisations have the necessary capability, technology and stakeholder buy-in. The rate of AI adoption is unprecedented and we need to be ready,” Nolan said. “In the last 12 months alone, I have seen big changes in how our own AI at Microsoft has been able to generate intelligent comments for us, as we work through our balance sheet and P&L variance analysis,” Nolan said. “We have had a big win in the efficiency of our contract review process, where we once had a full revenue recognition team analysing all of our contracts to account for them. “Now, we have AI reading 10,000 contracts a year and sorting them into low-, medium- and high-risk categories for us.” This means the revenue recognition team is only required to review high-risk contracts manually.  “We’ve had other big wins in journal entry anomaly detection, which has helped reduce risk on our financial statement—and our AI is now able to produce the first draft of the statement, reducing time spent on this work by about 15 percent.” Microsoft’s generative AI is creating models that recognise patterns in the financial planning and analysis data used to predict outcomes. “We’ve moved from bottom-up to top-down budgeting, reducing time spent on budgeting analysis from six months to six weeks,” Nolan said.  “This means we have much more time to think strategically and analytically—and to have a seat at the table in terms of our influence in the wider organisation. For us, AI is well and truly here.”

Liam Cotter charts the road ahead and critical importance of data for Irish organisations preparing for the AI revolution Right now, many organisations are experiencing caution, confusion—or both—in relation to artificial intelligence (AI). They are unsure about generative AI (GenAI), how it differs from previous AI iterations, and whether it can add value for them. With the first milestones of the European Union’s AI Act due to come into force in February 2025, focused on prohibiting AI systems posing unacceptable risk, organisations are concerned about falling foul of regulation. They are keen to ensure that any AI model introduced to help their business, undergoes rigorous testing to ensure it is fair and doesn’t have bias baked in. There are also more generalised fears regarding the cost of moving too quickly and developing the wrong solutions, however, as well as the “opportunity cost” of moving too slowly and thus failing to capture the benefits of the right opportunities. Data-based decisions Regardless of what stage an organisation has reached in its adoption of AI and GenAI, one thing holds true: the key to success is data. The only way to ensure quality AI outputs is to provide quality inputs. The way we manage and store data for the AI age differs from how we have done so in the past. Thus, even though the same fundamental rules apply, your data capture and entry systems may not be robust enough to handle AI demands and this could put you at a competitive disadvantage. Part of the problem with readying your data for AI transformation is the sheer amount of hard work involved, which may not appear not to offer a lot of value. This is because this work involves run-of-the-mill data generated from day-to-day operations. The key to the successful adoption of AI tomorrow is ensuring everybody in your organisation is aware of data management today. It is about ensuring everyone is measuring the quality of their data right across the organisation so they can stand over what it presents. For organisations that previously placed little value on the data they generate, this shift will require a culture change. It may also require different parts of the organisation to pool data—such as combining sales and stock databases rather than keeping them siloed, for example. In companies involved in mergers and acquisitions, it means ensuring you fully understand your data's lineage. The time to act is now The past 12 months have seen a growing realisation among organisations of the potential importance of AI as a lever for competitiveness. It is increasingly viewed as a valuable tool to drive digital transformation, enabling them to become more flexible, be faster to market, provide a better customer experience and more. Most of what AI will do has yet to be “dreamed up”. To put its scale in context, somewhere in the world, a data centre—the building block that powers the AI revolution—opens every two days. Organisations need to act to keep up. The first step is understanding the regulations and timeframes that are being rolled out under the EU AI Act. Next, identify use cases and develop them. Experiment—and if you are going to fail, fail fast. Get involved and discover the value in AI. People-powered data Understand the behavioural risks, too.  A lot of the work involved isn’t about technology at all. It’s about people. You can introduce the best technology in the world, but it's useless if staff don’t collect, curate and manage their data correctly. Everyone in your organisation must be able to stand by the accuracy of their data, which means good data practices must be applied to all business processes. In many organisations, this means investing in data capabilities, including staff training, and appointing a Chief Data Officer responsible for driving data literacy and good data management practices throughout the organisation, from the bottom to the top. To succeed, data management must be seen as a core, valuable component of what everyone does, regardless of their role. Break down the barriers Barriers to achieving effective AI readiness include an organisational culture that hasn’t yet caught up with the importance of data, allied to poor systems and processes that ensure people don’t understand the implications of getting it wrong. The real barrier is, however, that all of this takes work. Readying your data systems for AI is a pain, and sometimes, people can see no value in it. Once you can stand over your data, knowing it is of good quality and understanding its lineage, your organisation will likely be in pretty good shape because you can then move on and digitise your key business processes with confidence. The AI revolution starts and ends with data. Don’t underestimate the effort required to get good quality, well-managed data. It is the foundational work that cannot be avoided. Equally, don’t underestimate the impact. Once you have good data systems in place, you can confidently move forward and capture the full breadth of AI benefits that await.  Liam Cotter is Technology Practice Lead at KPMG

Data use is skyrocketing, raising ethical concerns beyond regulatory compliance. Colm McDonnell explores how embedding digital ethics ensures fairness, transparency, and accountability in organisations Data has become an integral part of modern life, and its usage is growing exponentially. From businesses to governments, organisations are collecting, storing, and analysing vast amounts of data to gain insights, make decisions, and develop new products/services. However, with great power comes great responsibility. The more data organisations process, the bigger the spotlight on them, not only to ensure regulatory compliance but also to focus on the significant ethical concerns resulting from data collection and its use. Furthermore, the growing use of technology, including artificial intelligence (AI) and robotics, stems concerns about the extensive use of data and the potential for misuse. What is digital ethics? Doing the right thing, regardless of legislation, takes you into the field of ethics. Organisations usually focus on various regulatory obligations that they must comply with, but organisations also have a responsibility to their stakeholders, including their employees, customers, vendors, and investors. This goes beyond regulatory compliance. Accountability can be complex to define and demonstrate, often leading organisations to set out some principles they should adhere to such as privacy, fairness, non-discrimination, transparency, and more, while processing data. Embedding digital ethics into an organisation involves promoting the moral values of the organisation through the alignment of data processing practices and processes with those values. Digital ethics refers to a set of principles and moral values that guide the responsible and ethical use of data. The following eight guiding principles define an approach to AI and digital ethics. Code of digital ethics All organisations should establish a code of digital ethics that sets out their commitments to ethical data practices. Digital ethics by design should be considered right from the outset of any product development, product enhancement or any proposed processing of data. Periodic training and awareness programmes should be rolled out to promote awareness of ethical data processing practices. This will eventually build a culture of trust, transparency, and safety within the organisation. Human oversight and determination Organisations must make sure AI systems do not take the place of human accountability and responsibility. There needs to be human oversight and safeguards in place to prevent misuse of data. There should be cross-functional stakeholder collaboration and effective governance. Proportionality, do no harm, safety and security AI systems should only be used as much as is required to accomplish a valid goal. Risk assessment should be utilised to prevent any potential harm from these types of applications. Fair and transparent algorithms Organisations must ensure that their decision-making and algorithms are fair and impartial. This can be achieved through ongoing monitoring and periodic testing. Transparency and explainability Data should be collected and used with transparency, so individuals understand how their data will be utilised thereby allowing them to make informed decisions about whether to share their data. Further, where deemed necessary, before collecting data, organisations should seek consent from individuals. This consent should always be freely given and be fully informed. Inclusion Unconscious or conscious bias can affect inclusivity in an organisation. Organisations should take the necessary steps to ensure the processing of data does not result in or hide discrimination or bias. Vulnerable data subjects who are the most susceptible to negative consequences of processing require additional consideration. Autonomy, freedom, respect, privacy, and dignity Individuals must be able to make their own decisions, take their own actions, and make their own choices. Processing of data should not constrain human beings in how they want to live their lives. Autonomy for individuals to control how their data is processed should be ensured. The processing of the data should be respectful of human values. Specifically, when the processing is carried out through AI, the outcome should not dehumanise individuals. Sustainability AI innovations should be evaluated to consider their effects on the environment and their ability to sustain through periods of time. These innovations should align with the organisation’s sustainability goals. Colm McDonnell is Partner of Risk Advisory at Deloitte