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Managing AI trustworthiness and danger is important to realizing enterprise worth from AI. When requested what organizations should do to seize AI’s advantages whereas minimizing its downsides, Sibelco Group CIO Pedro Martinez Puig emphasised self-discipline and strategic focus.
“Capturing AI’s worth whereas minimizing danger begins with self-discipline,” Puig mentioned. “CIOs and their organizations want a transparent technique that ties AI initiatives to enterprise outcomes, not simply know-how experiments. This implies defining success standards upfront, setting guardrails for ethics and compliance, and avoiding the entice of countless pilots with no plan for scale.”
For Puig, the work begins by creating robust use instances and rigorous foundations. “CIOs should deal with use instances which can be stable sufficient to ship measurable impression. In mining and supplies, this contains guaranteeing knowledge integrity from the plant flooring to enterprise methods, embedding cybersecurity into AI workflows, and monitoring for dangers like bias or mannequin drift.”
Puig provides that belief is simply as essential as know-how. “Transparency, governance, and coaching assist folks perceive how AI choices are made and the place human judgment nonetheless issues. The purpose is not to chase each shiny use case; it is to create a framework the place AI delivers worth safely and sustainably.”
Nicole Coughlin, CIO of the City of Cary, N.C., echoes this view. “It takes governance, collaboration, and inclusion,” she mentioned. “The organizations that thrive at AI would be the ones that carry folks collectively — coverage, authorized, communications, operations, and IT — to co-create the guardrails. Minimizing danger is not about slowing innovation. It is about alignment and shared function.”
Key dangers for AI
Based on the authors of “Rewired: The McKinsey Information to Outcompeting within the Age of Digital and AI,” danger and belief have all the time been a part of AI, however right this moment’s panorama raises the stakes. They write that “AI transformations floor a complete new and sophisticated set of interconnected dangers. … AI improvements are happening in an setting of elevated regulatory scrutiny, the place customers, regulators, and enterprise leaders are more and more involved about vulnerabilities throughout cybersecurity, knowledge privateness, and AI methods.”
Given this context, they recommend organizations should prioritize “digital belief.” This entails:
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Defending shopper knowledge and sustaining robust cybersecurity.
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Delivering dependable AI-powered services and products.
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Making certain transparency round how knowledge and AI fashions are used.
Constructing this belief requires triaging dangers, operationalizing danger insurance policies throughout the group, and elevating consciousness so workers perceive their position in accountable AI.
In Dresner Advisory Service’s 2025 analysis, we examined the extra dangers distinctive to generative and agentic AI. These dangers — which vary from use case definition to safety and privateness — have undoubtedly hindered the manufacturing rollout of GenAI options; most of the identical considerations additionally apply to agentic AI, which is constructed on comparable foundational applied sciences.
Knowledge safety and privateness emerge as important points, cited by 42% of respondents within the analysis. Whereas different considerations — akin to response high quality and accuracy, implementation prices, expertise shortages, and regulatory compliance — rank decrease individually, they collectively signify substantial obstacles.
When aggregated, points associated to knowledge safety, privateness, authorized and regulatory compliance, ethics, and bias kind a formidable cluster of danger elements — clearly indicating that belief and governance are prime priorities for scaling AI adoption.
AI governance to generate belief
At its core, governance ensures that knowledge is protected for decision-making and autonomous brokers. In “Competing within the Age of AI,” authors Marco Iansiti and Karim Lakhani clarify that AI permits organizations to rethink the normal agency by powering up an “AI manufacturing unit” — a scalable decision-making engine that replaces guide processes with data-driven algorithms. Nonetheless, to attain an AI manufacturing unit, organizations want an efficient knowledge pipeline that gathers, cleans, integrates, and safeguards knowledge in a scientific, sustainable and scalable means.
A proxy for measuring this sort of industrialization of knowledge is the success of BI implementations. In Dresner’s 2025 analysis, 32% of organizations surveyed mentioned that they had been fully profitable with their BI implementations. In a dialogue with Stephanie Woerner of MIT-CISR, she instructed their newest analysis numbers had been comparable. Mixed, these findings recommend {that a} important majority of companies — roughly 68% — have but to ascertain actually efficient knowledge pipelines.
To bridge this hole, organizations should provoke and personal a knowledge governance program — one thing that traditionally CIOs have loathed however should clearly change within the AI period. Fundamentals embody:
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Knowledge integrity and high quality: Making certain the supply of reality is correct.
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Clear possession: Defining who’s chargeable for particular datasets.
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Equity: Actively monitoring for and lowering bias, together with guaranteeing that knowledge will not be uncovered and used just for professional functions.
Chris Baby, VP of product and knowledge engineering at Snowflake, places it this manner: “Effectivity with out governance will value companies within the long-term.” Agentic AI provides complexity, Baby says, as a result of these autonomous methods act on knowledge immediately. “The trail ahead is to unify knowledge, AI, and governance in a single safe structure,” he mentioned.
In the meantime, College of Porto Professor Pedro Amorim, recommends a “venture-style” method: “Fund many small, time-boxed bets, be taught shortly, and double down on the winners with a transparent path to industrialization.”
AI governance to make sure knowledge safety
Governance of danger focuses on defending entry to knowledge. Bob Seiner — a number one knowledge governance thought chief — notes that it’s important to formalize accountability and educate folks on obtain ruled knowledge habits. Efficient safety means stopping unauthorized entry, lack of integrity and theft whereas guaranteeing the professional processing of non-public info.
Iansiti and Lakhani argue that reliable AI requires “centralized methods for cautious knowledge safety and governance, defining acceptable checks and balances on entry and utilization, inventorying the property rigorously, and offering all stakeholders with essential safety.” As a result of LLMs depend on massive volumes of knowledge — together with PII — knowledge have to be secured in opposition to the distinctive methods LLMs retailer and retrieve info.
Amorim suggests establishing these guardrails in place early:
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Knowledge classification, privateness/IP guidelines.
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Human-in-the-loop for delicate choices.
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Specific no-go standards and analysis benchmarks.
He additionally recommends guaranteeing there’s finances on the entrance of the funnel, so you are not compelled into one or two huge bets.
Jared Coyle, chief AI officer at SAP, recommends a governance framework primarily based on three pillars:
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Related: AI must be designed to work inside a particular enterprise course of, not in a standalone “AI for AI’s sake” means.
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Dependable: The system ought to adhere to a constant and data-accurate output.
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Accountable: The method must be licensed, comply with strict moral pointers and carry ahead current safety infrastructure.
Parting Phrases
Reaching worth with AI requires industrialized knowledge and processes and robust governance.
The start line is straightforward: CIOs should guarantee their AI initiatives tie on to enterprise outcomes, set up clear success standards, and embed ethics and compliance guardrails early to keep away from the entice of countless pilots that by no means scale.
Equally essential is enterprise belief in AI. CIOs want clear AI workflows, robust knowledge foundations, cross-functional collaboration, and coaching that helps workers perceive how AI choices are made — and the place people stay in management.
Threat stays the largest barrier to GenAI and agentic AI. Knowledge safety and privateness prime the record, adopted by accuracy, regulatory compliance, bias and ethics — a cluster of interconnected dangers that gradual manufacturing rollout.
Efficient governance is the one option to ship the industrialized knowledge pipelines essential for belief. This requires formalizing accountability, centralizing knowledge platforms, imposing entry controls, and establishing early guardrails — akin to knowledge classification, privateness protections, and human-in-the-loop oversight — to make sure AI is related, dependable and accountable.
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