Episode 4
The Regulation Dilemma: Balancing AI Innovation with European Complexity
Guest post by Adam Spearing, VP of AI GTM, EMEA at ServiceNow Organisations in Europe are under pressure to adopt AI at speed, all the while operating in a highly complex and ever-changing regulatory and risk environment. Even now, the EU's AI Act is s...
Irish Tech News Audio Articles · Ronan Leonard
March 26, 20267m 43s
Audio is streamed directly from the publisher (cdn.beyondwords.io) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
Guest post by Adam Spearing, VP of AI GTM, EMEA at ServiceNow
Organisations in Europe are under pressure to adopt AI at speed, all the while operating in a highly complex and ever-changing regulatory and risk environment. Even now, the EU's AI Act is shifting the landscape for organisations operating in the region, with Article 72 of the AI Act coming into force and creating new regulations on the monitoring of high-risk AI systems. For these organisations, scaling AI safely in this complex regulatory environment is a constant challenge. They face the risk of fragmenting systems, as well as losing visibility of how AI is used or introducing unmanaged risk.
The real opportunity is not to be found in buying new yet disconnected AI tools, but when AI is embedded directly into secure, governed workflows from the very start. For those facing regulation headaches, integrating governance tools into a workflow alongside data is becoming increasingly vital. Forward-thinking companies are even adopting a predictive approach when it comes to AI, anticipating risks and regulatory changes and mitigating them before they occur.
Building AI that lasts
Taking a more thoughtful, measured approach pays off when it comes to AI. Companies who hold back from 'rushing in' to AI and adopt a more thoughtful approach can actually accelerate faster over the medium to long term. Imagine it as something like an 'AI factory' – business leaders need to qualify which processes will benefit, assess the 'right' level of AI, then deploy that within governed workflows. An important part of that is 'qualifying out' which processes are not suitable due to AI due to factors such as risk. Having a data model and AI integrated deeply into governed workflows means that governance can scale without fragmenting. In today's world, this is crucial.
The EU AI act has made it urgent to find a path between embracing AI too rapidly and feeling paralysed by fears around risk and compliance. Over-cautious companies are set to fall behind, but rushing blindly into AI carries the risk of creating governance debt: organisations without adequate governance cannot demonstrate compliance, and will not be able to scale and reap the benefits of AI.
Many organisations are still stuck in this gulf between responsiveness and responsibility: business leaders need responsiveness from generative AI, delivering insights rapidly, but also must keep regulators happy by behaving responsibly. Having workflow-native AI means that requirements such as transparency and oversight in the EU AI Act become architectural, with governance built in, rather than bolted on, after the fact, to disparate AI tools.
Combatting future risk
As I see it: reactive AI governance is a hindrance; proactive AI governance is an accelerator to business value. The next challenge is operationalising this through what I call 'governed acceleration'. With new AI regulations emerging in different regions and adherence reaching across borders, forward-thinking organisations are turning to AI tools themselves to be prepared for this evolving governance landscape. Chosen correctly, the right AI technology can help organisations stay ahead of the systems that govern AI, by enforcing compliance, for example. This means that the right technology choices deliver a self-reinforcing, circular advantage. Governance is growing in importance, with the role of the CIO now encompassing issues such as model training, algorithmic bias and organisational culture.
As a result, a clear governance structure is key. There should be a single, well-defined owner of AI governance, be that the legal department, the chief data officer, the CIO or a chief AI officer. This person or team takes responsibility to implement consistent frameworks around third-party AI tools, assessing and managing risks and regulatory compliance. This enables organisations to innovate quickly and confidently, while maintaining control.
When governance becomes invisible
W...
Organisations in Europe are under pressure to adopt AI at speed, all the while operating in a highly complex and ever-changing regulatory and risk environment. Even now, the EU's AI Act is shifting the landscape for organisations operating in the region, with Article 72 of the AI Act coming into force and creating new regulations on the monitoring of high-risk AI systems. For these organisations, scaling AI safely in this complex regulatory environment is a constant challenge. They face the risk of fragmenting systems, as well as losing visibility of how AI is used or introducing unmanaged risk.
The real opportunity is not to be found in buying new yet disconnected AI tools, but when AI is embedded directly into secure, governed workflows from the very start. For those facing regulation headaches, integrating governance tools into a workflow alongside data is becoming increasingly vital. Forward-thinking companies are even adopting a predictive approach when it comes to AI, anticipating risks and regulatory changes and mitigating them before they occur.
Building AI that lasts
Taking a more thoughtful, measured approach pays off when it comes to AI. Companies who hold back from 'rushing in' to AI and adopt a more thoughtful approach can actually accelerate faster over the medium to long term. Imagine it as something like an 'AI factory' – business leaders need to qualify which processes will benefit, assess the 'right' level of AI, then deploy that within governed workflows. An important part of that is 'qualifying out' which processes are not suitable due to AI due to factors such as risk. Having a data model and AI integrated deeply into governed workflows means that governance can scale without fragmenting. In today's world, this is crucial.
The EU AI act has made it urgent to find a path between embracing AI too rapidly and feeling paralysed by fears around risk and compliance. Over-cautious companies are set to fall behind, but rushing blindly into AI carries the risk of creating governance debt: organisations without adequate governance cannot demonstrate compliance, and will not be able to scale and reap the benefits of AI.
Many organisations are still stuck in this gulf between responsiveness and responsibility: business leaders need responsiveness from generative AI, delivering insights rapidly, but also must keep regulators happy by behaving responsibly. Having workflow-native AI means that requirements such as transparency and oversight in the EU AI Act become architectural, with governance built in, rather than bolted on, after the fact, to disparate AI tools.
Combatting future risk
As I see it: reactive AI governance is a hindrance; proactive AI governance is an accelerator to business value. The next challenge is operationalising this through what I call 'governed acceleration'. With new AI regulations emerging in different regions and adherence reaching across borders, forward-thinking organisations are turning to AI tools themselves to be prepared for this evolving governance landscape. Chosen correctly, the right AI technology can help organisations stay ahead of the systems that govern AI, by enforcing compliance, for example. This means that the right technology choices deliver a self-reinforcing, circular advantage. Governance is growing in importance, with the role of the CIO now encompassing issues such as model training, algorithmic bias and organisational culture.
As a result, a clear governance structure is key. There should be a single, well-defined owner of AI governance, be that the legal department, the chief data officer, the CIO or a chief AI officer. This person or team takes responsibility to implement consistent frameworks around third-party AI tools, assessing and managing risks and regulatory compliance. This enables organisations to innovate quickly and confidently, while maintaining control.
When governance becomes invisible
W...