From governance and data quality to workforce transformation and economic competitiveness, Sync NI brings together technology professionals from across Northern Ireland to explore how artificial intelligence is reshaping organisations.
Sync NI convened a roundtable of technology leaders, AI practitioners and innovation specialists from across Northern Ireland to explore the realities of artificial intelligence adoption and the foundations organisations need before deploying it at scale.
Held at URBAN HQ in Belfast, the discussion brought together industry experts David Crozier Director AICC; Carla McGlynn Managing Director Citi; Jonny Bradley Director AI Services Aflac; Hugh Kirk Cyber Security Director & Leader Allstate; Andy McClernon Director of Engineering QUB Momentum One Zero; Niall Haslam Senior Manager Engineering Apex Fintech Solutions; and Ruth McGuinness CEO Actonomy, a Kainos AI company. Together, they examined the opportunities, challenges and practical considerations shaping AI adoption across industry.
While artificial intelligence has become one of the most talked-about technologies of the decade, participants agreed that many organisations are still struggling to distinguish hype from reality. AI features prominently in boardroom strategies, software product roadmaps and economic forecasts, yet uncertainty remains about what AI actually is, where it can deliver value and how businesses should approach adoption responsibly.
Rather than focusing on speculation, the conversation centred on implementation, governance, workforce readiness and how organisations can turn AI from an interesting technology into a sustainable business capability.
One of the first observations raised was the tendency to equate AI solely with generative AI. Tools such as ChatGPT, Copilot, Claude and Gemini have dominated public attention because they provide highly visible examples of AI-generated content. However, participants stressed that generative AI represents only one part of a much broader field.
Artificial intelligence has been embedded within business systems for years through technologies such as predictive analytics, recommendation engines, fraud detection, forecasting models, computer vision and natural language processing. For many around the table, the recent surge in interest reflects increased public awareness rather than the arrival of an entirely new technology.
A recurring theme throughout the discussion was the importance of understanding AI's true capabilities. While modern systems can generate impressively human-like responses, participants argued that organisations should view AI as a sophisticated pattern-recognition system rather than a thinking machine. Treating it as the latter often leads to unrealistic expectations and poor decisions.
The group agreed that AI should be approached as a powerful tool whose effectiveness depends on the quality of the data, the expertise of its users and the governance structures surrounding it. One of the most persistent misconceptions, they argued, is the belief that AI can be deployed as a standalone solution to solve organisational challenges.
Instead, AI tends to amplify existing strengths and weaknesses. Organisations with strong processes, skilled teams and high-quality data can see significant benefits. Conversely, poor processes and weak data can be magnified just as quickly. In many cases, AI exposes underlying organisational issues rather than resolving them.
Despite ongoing debate about future possibilities, many organisations are already seeing practical results from AI deployment. Participants described growing adoption across software development, business operations and customer-facing functions.
Within engineering teams, AI tools are helping generate code, accelerate testing, support technical research and identify vulnerabilities. Across wider business functions, AI is being used to process documents, summarise information, identify trends and support decision-making. Customer-facing teams are using AI to prepare for meetings, consolidate information from multiple systems and improve responsiveness, while automation is increasingly streamlining back-office activities.
Several contributors noted that AI adoption often began before formal policies or programmes were introduced. Employees had independently started using publicly available AI tools in their day-to-day work, creating what is commonly referred to as "shadow AI"
This mirrors earlier technology shifts, when staff adopted cloud-based services or collaboration platforms faster than organisations could establish governance frameworks. As a result, the challenge for many leaders is no longer deciding whether to adopt AI. Adoption is already underway. The priority is ensuring these tools are used securely, responsibly and in a way that aligns with organisational goals.
Some participants compared the current AI landscape to the early years of cloud computing. Initially viewed as a major transformation initiative, cloud eventually became standard business infrastructure. Many around the table believe AI is following a similar path and will ultimately become a routine part of everyday operations.
Productivity emerged as one of the most debated topics during the discussion. While there was little disagreement that AI can improve efficiency, participants highlighted the difficulty of measuring the benefits.
Software development provided some of the clearest examples. AI-assisted workflows are reducing project timelines by helping teams create requirements documents, generate code, automate testing and identify issues more quickly than traditional approaches. Tasks that previously required weeks can increasingly be completed in days, and sometimes hours.
However, quantifying these gains remains challenging. While an employee may save time drafting reports, summarising meetings or managing administrative tasks, translating those improvements into traditional return-on-investment metrics can be difficult.
Several participants distinguished between efficiency and productivity. Efficiency gains can often be measured through reduced costs, shorter delivery cycles or lower resource requirements. Productivity gains, meanwhile, may manifest through improved quality, innovation, employee experience or customer satisfaction, outcomes that are valuable but harder to quantify.
As a result, many organisations are shifting their focus from individual AI tools to end-to-end business processes. Rather than asking how AI can improve a single task, they are examining entire workflows to identify where automation and intelligence can reduce friction, accelerate delivery and create greater value. According to participants, this process-focused approach is producing some of the strongest business cases for AI investment.
Governance was another major theme. Although often overshadowed by discussions about innovation, many attendees argued that governance has become one of the most important factorsdetermining whether AI initiatives succeed.
Organisations making meaningful progress consistently highlighted the importance of clear policies, secure environments, approval processes and risk management frameworks. Several described creating internal AI sandboxes where employees can safely experiment with new technologies while protecting sensitive corporate information.
These environments allow organisations to encourage innovation without exposing themselves to unnecessary risks. Participants agreed that without proper governance, AI projects often remain isolated experiments that never deliver significant value. Effective governance provides the confidence needed to move promising initiatives into production.
The conversation also explored procurement and technology strategy. Given the speed at which AI technologies continue to evolve, many businesses are cautious about becoming dependent on a single platform or vendor. Maintaining flexibility, ensuring portability across models and avoiding long-term lock-in are becoming increasingly important considerations.
When discussing how businesses should begin their AI journey, participants repeatedly came back to one principle: start with the problem, not the technology.
Too often, organisations begin by asking what AI can do rather than identifying the business challenge they want to solve. Participants argued that successful projects typically emerge from clearly defined operational issues, inefficiencies or customer needs. Once those are understood, organisations can determine whether AI is the most appropriate solution.
At the same time, contributors cautioned against waiting for perfect conditions before experimenting. While governance and oversight are essential, some organisations risk paralysis by attempting to create comprehensive frameworks before any practical testing takes place.
There was strong support for controlled experimentation. Secure environments, limited pilot projects and structured learning initiatives allow businesses to explore AI responsibly while governance frameworks mature alongside implementation.
Several participants described seeing scepticism disappear once leaders and employees witnessed AI solving real-world problems. In many cases, understanding followed experience. Employees who initially struggled to see AI's relevance often identified valuable use cases after gaining access to appropriate tools and training.
Data quality emerged as one of the most significant barriers to successful AI deployment. While advanced AI models are now widely accessible through commercial and open-source platforms, participants argued that the quality of organisational data remains a far greater challenge than access to the technology itself.
The phrase "garbage in, garbage out" surfaced repeatedly. Regardless of how advanced a model becomes, inaccurate, incomplete or poorly structured data will inevitably lead to unreliable outcomes.
Bias was another major consideration. Historical datasets often contain patterns and assumptions that reflect previous decisions and processes. Without careful evaluation, organisations risk reproducing those biases through AI systems at scale.
Participants also highlighted the growing importance of explainability, particularly in regulated sectors. Businesses increasingly need to understand and justify how AI-assisted decisions are made, demonstrating fairness, consistency and transparency when required.
The discussion challenged traditional assumptions about what constitutes valuable business data. While organisations often focus on structured information such as databases and spreadsheets, significant opportunities also exist within unstructured content including documents, video, communications, sensor data and operational records. Unlocking value frequently requires businesses to take a broader view of the information they already own.
The conversation eventually turned to skills and workforce development. A strong consensus emerged that AI literacy will be critical to both organisational and regional competitiveness.
Participants stressed that AI literacy extends well beyond prompt engineering. It includes understanding the strengths and limitations of AI systems, recognising bias, critically evaluating outputs and identifying appropriate use cases.
Importantly, AI literacy cannot be confined to technology teams. Business leaders, project managers, analysts, finance professionals, legal teams and operational staff all require sufficient understanding to engage effectively with emerging technologies.
Many organisations represented at the roundtable are therefore investing in learning communities, mentoring programmes, AI champions and formal training initiatives. Participants argued that widespread understanding is essential if businesses want to scale adoption successfully. Without it, organisations risk creating a small group of specialists while the broader workforce struggles to adapt.
Interestingly, the discussion suggested that AI may increase the value of specifically human skills. As routine work becomes more automated, capabilities such as critical thinking, judgement and domain expertise become even more important.
While AI can analyse information quickly, understanding context, interpreting nuance and making informed decisions remain fundamentally human responsibilities. Communication and collaboration were also identified as critical capabilities, particularly as AI projects increasingly require multidisciplinary teams that bring together technical, operational and business expertise.
Adaptability was another recurring theme. With technology evolving rapidly, employees will need to continuously learn and embrace new ways of working. Participants also highlighted systems thinking as an increasingly valuable skill, enabling people to understand how technology, processes, data and people interact across organisations.
The human dimension of AI adoption surfaced repeatedly throughout the discussion. Many organisations have encountered concern among employees worried about automation and job displacement. Participants agreed that communication plays a crucial role in addressing these fears.
Employees are far more likely to engage positively with AI when organisations explain how roles will evolve, what new opportunities will emerge and what support will be available. Several contributors shared examples where resistance diminished significantly once clear pathways for training and career development were introduced.
The general view around the table was that AI is more likely to reshape jobs than eliminate them. Certain tasks will become automated and workflows will evolve, but human oversight, judgement and collaboration will remain essential.
Many compared AI to previous technological shifts such as cloud computing, agile delivery and modern project management practices. While these developments transformed the way organisations operate, they did not remove the need for talented people. Participants expect AI to follow a similar trajectory.
As the roundtable concluded, discussion turned to Northern Ireland's position within the global AI landscape. Participants expressed cautious optimism, highlighting the region's strengths in software engineering, cybersecurity, data science, financial services and advanced manufacturing.
Combined with a growing ecosystem of universities, research institutions, technology companies and start-ups, Northern Ireland possesses many of the ingredients needed to benefit from the next phase of AI-driven innovation. However, attendees emphasised that success will require continued investment in skills, governance, data readiness and organisational transformation.
The clearest message to emerge from the discussion was that businesses must move beyond simplistic narratives of either optimism or alarm. AI's future is unlikely to be defined by either utopia or catastrophe.
For Northern Ireland's technology community, the debate over whether AI matters is already settled. The challenge now is understanding how to deploy it effectively, responsibly and at scale to create lasting value for organisations, employees and the wider economy.
Read the Autumn 2026 edition free online →
Stay connected with NI's tech community:
