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As Organizations Ask If They “Should” Launch AI Projects, A Risk Management System Becomes Essential

Organizations considering AI projects must prioritize risk management frameworks before deployment rather than reacting to problems after they arise. Industry leaders emphasize that responsible AI adoption requires robust safeguards, governance structures, and ethical guardrails embedded from the outset. The shift from asking 'can we' to 'should we' launch AI initiatives signals a maturing approach to enterprise AI strategy.

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By Mark Beccue · Ai EthicsAi IntegrationData PrivacyData Transparency
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Key takeaways

01

Risk management systems for AI must be established before deployment, not retrofitted after issues emerge.

02

The key governance question for organizations has evolved from technical feasibility to ethical and strategic appropriateness.

03

Responsible AI adoption requires structured frameworks that address accountability, transparency, and potential harm.

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Four major US AI firms, including Anthropic, Google, Microsoft, and OpenAI, have launched the Frontier Model Forum to address advanced AI risks and establish industry standards. This collaborative initiative emphasizes safety, security, and trust, with companies committing to risk management system tools like watermarking systems to differentiate AI-generated from human-produced content.

Companies know AI is not merely about integrating the latest algorithms or using vast data sets. At its core, it’s about making critical decisions that affect stakeholders, users, and the general public. That’s why, as AI weaves its way deeper into our daily operations, the real question companies should grapple with isn’t, “Can I implement this AI?”, but rather, “Should I?”

The real question companies should grapple with isn’t, “Can I implement this AI?”, but rather, “Should I?”

As such, before diving headfirst into AI development, organizations need to assess the ethical dimensions, from bias and accuracy to transparency and data privacy. That’s where a risk management system comes into play. Expert Mark Beccue, Research Director of AI at The Futurum Group, weighs in with further analysis on just how a risk management system can help a business set ethical standards for their AI practices.

Mark’s Thoughts:

“An organization shouldn’t be asking, ‘Can I do this AI?’ It really should be asking, rather, ‘Should I do this AI?’ And you look at that through the lens of: Does it make sense for our organization? What risks are we going to take?

Hi. I’m Mark Beccue. I’m the Research Director of AI at the Futurum Group.

Thinking about this question, it is interesting that some of the leaders in the hyperscalers space have come forward to volunteer some best practices. And I think the first thing we have to think about when you talk about recommendations is that any organization that’s looking at AI or working on AI right now should build their own AI risk management structure for their organization. And what I mean by that is you’re going to look for governance tools, AI governance tools. It starts with risk management, risk assessments. But it really should include an oversight team and really life cycle management.

It’s really kind of soup to nuts how you do AI really comes down to working on understanding the risks for your company. And, you know, that’s really aside from things that are happening with standards and stuff like that. Because there’s lots of risks involved and you need to understand them.

When you look at AI risk management, and another best practice is to think about the core areas of focus, which really evolve around, AI ethics. And those are accuracy, which has to do with bias, transparency of AI, security, which also includes data privacy, and then fairness. So those are the core pieces that are in an AI risk management structure.

And there’s really another piece that you have to think about when you’re looking at AI going forward and how you’re going to look at standards, what best practices are. And it really comes down to a very simple question. An organization shouldn’t be asking, ‘Can I do this AI?’ It really should be asking, rather, ‘Should I do this AI?’ And you look at that through the lens of: Does it make sense for our organization? What risks are we going to take?

An organization shouldn’t be asking, ‘Can I do this AI?’ It really should be asking, rather, ‘Should I do this AI?’

So, if you take that approach, the standards are going to come. It’s super early. There’s not a lot out there right yet that are very specific to AI. They will come. The laws and the standards will come.

There are some protections for organizations to kind of think about this a little more within GDPR. So that’s data privacy, which will cover a lot of AI things. And then there’s maybe some security issues. Not so much in standards, but what organizations do to vet technology or applications they use and think about that from their own perspective of what they face with security.

Last thing I’ll leave is there’s a really good resource for any organization that’s looking into this step, and that is it’s called aiethicist.org. And that has lots of frameworks, lots of resources from multiple organizations that are free that you can look at to start to think about how you’re going to set up your organization.”

Video TranscriptExpand ↓

An organization should be asking. It shouldn't be asking, can I do this AI? It really should be asking rather, should I do this and you look at that through the lens of does it make sense for our organization? What risks are we going to take? Hi. I'm Mark Becky. I'm a research director for AI at the futurum group. Thinking about this question. It is interesting that some of the leaders in the hyperscalers space have come forward to volunteer some best practices. And I think the first thing we have to think about when you talk about recommendations is that any organization that's looking at AI are working on AI right now should build their own AI risk management structure for their organization. And what I mean by that is you're you're gonna look for governance tools, AI governance tools. It starts with risk management, risk assessment, But it really should include an oversight team and really life cycle management. It's really kind of soup the nuts how you do AI really comes down to working on understanding the risks for your company. And, you know, that's really aside from things that are happening with, standards and stuff like that. Because there's lots of risk involved and you need to understand them. When you look at AI risk management and another best practice is to think about the core areas of focus, which really evolve around, AI ethics. And those are accuracy, which has to do with bias transparency of AI security, which also includes data privacy, and then fairness. So those are the core pieces that are in an AI risk management, structure. And there's really another piece that you have to think about when you're looking at AI going forward and how you're gonna look at standards, what best practices are. And it really comes down to a very simple question. An organization should be asking it shouldn't be asking, can I do this AI? It really should be asking rather, should I do this AI? And you look at that through the lens of does it make sense for our organization? What risks are we going to take? So if you take that approach the standards standards are gonna come. It's super early. There's not a lot out there right yet that are very specific to AI. They will calm the laws and the standards will come. There are some protections for organizations to kind of think about this a little more with with within GDPR. So that's day privacy, which will cover a lot of AI things. And then there's maybe some security issues, not not so much in standards, but what organizations do to vet technology or applications they use and think about that from their own perspective of what they face with, with security. Last thing I'll leave is there's a really good resource for any organization that's looking into this step, and that is it's called ai ethicist dot org. And that has lots of frameworks, lots resources from multiple organizations for, that are free that you can look at to start to think about how you're gonna set up your organization.

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About the author

Mark Beccue
Mark BeccueResearch Director

Mark Beccue is a veteran market research analyst with more than 25 years of experience in market research and business strategy. Mark is one of a handful of pioneering analysts who began to focus on AI market research in 2015. Today with Futurum Group and previously as a principal analyst within the AI practice area at Omdia and Tractica, he has advised clients and provided them with syndicated and custom qualitative AI research services. His expertise in AI use cases, applications and software, natural language AI and broader trends surrounding AI market adoption have made him a well-known and sought after speaker, panel moderator, conference chair and media resource within the AI business community. He has served in those roles for events including the AI Summits in London, Singapore, New York and Silicon Valley, IOT World, Smart Home Summit, UX Next and Telco AI Europe. Prior to joining Tractica, Mark was an independent market research analyst focused on emerging technologies. Before going independent, Mark served as in house market intelligence analyst for Syniverse, where he helped guide overall business and product line strategies. For 4 years Mark worked as a Senior Analyst for ABI Research, a global technology research firm, focusing on mobile consumer services. Prior to ABI, Mark worked for 10 years for Syniverse in product management, greenhouse innovation and marketing. Specialties: AI B2B and B2C market intelligence, analysis and insights. Natural Language AI. Operationalizing AI in the Enterprise. AI market adoption trends and issues. Strengths - AI and other technical market analysis designed for business readers, writing, thought leadership, forecasting, market sizing

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About the Expert

Mark Beccue
Mark Beccue

Principal, Emerging Technology at EY

Mark Beccue is a principal focused on emerging technology and AI strategy at EY (Ernst & Young). He advises organizations on the responsible adoption of artificial intelligence, including governance frameworks and risk management. His work spans multiple industries where AI deployment decisions carry significant operational and reputational stakes.

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