John Jalbrzykowski on AI Solutions at ABS Consulting

🗣️ Welcome to 𝐕𝐢𝐬𝐢𝐨𝐧𝐚𝐫𝐲 𝐕𝐨𝐢𝐜𝐞𝐬, where we spotlight the talented experts driving innovation and safety at ABS Consulting. Meet Patrick Murphy, Senior Consultant, AI Solutions at ABS Consulting. Patrick leads commercial AI services for the maritime industry across Asia Pacific, combining client engagement with technical scoping, solution development and prototyping. His work focuses on helping clients address complex challenges with practical AI applications that improve how critical information is organized, reviewed and used. As Patrick puts it, “AI can make a real difference by helping people find critical information more quickly and reliably, especially in environments where accuracy, traceability and safety matter most.” Learn more about Patrick’s 𝐯𝐢𝐬𝐢𝐨𝐧 for the future of AI and how he’s using his 𝐯𝐨𝐢𝐜𝐞 to help shape innovative solutions for the maritime industry. Swipe through to get to know Patrick! 🔗 Discover how ABS Consulting, powered by people and driven by safety, helps clients navigate risk and operate safely and efficiently: www.abs-group.com #visionaryvoices #absconsulting #poweredbypeopledrivenbysafety

Business Technology Series | Episode #03 The next competitive advantage won't come from automating everything. It will come from knowing what should never be automated. AI agents are changing how businesses think about work. Not because they replace people. But because they can execute repetitive tasks, coordinate routine processes, and help teams work more efficiently. Businesses are beginning to ask a different question. Not "Can AI help?" But "What decisions can AI safely support?" As AI becomes more capable, leadership becomes even more important. Who defines the rules? Who reviews the outcomes? Who remains accountable when AI makes a mistake? The organizations that gain the most from AI won't be the ones that automate the most. They'll be the ones that combine automation with governance, accountability, and human judgment. Technology improves efficiency. People provide judgment. Together, they create business value. AI should strengthen decision-making. It should never replace accountability. That's the principle we encourage at Crest StarOne™. Because automation creates opportunities. Accountability creates trust. As AI agents become more capable, what do you believe will matter more—automation or accountability? #BusinessTechnology #AIAgents #BusinessLeadership #CrestStarOne

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Many business leaders want a complete AI strategy before starting any automation initiative. The challenge is that it is difficult to define the right strategy, governance model, and priorities without first gaining practical experience. When organizations rely entirely on external advisors, they may receive a strong strategy on paper—but still lack the internal knowledge needed to evaluate whether it is realistic, relevant, or well designed. A better approach is to start with a low-risk, low-cost automation use case that delivers clear and measurable results. This gives leaders the opportunity to see how AI automation works in practice, understand the operational and governance requirements, identify potential risks, and build internal confidence. External partners can still provide valuable expertise, but the organization becomes better equipped to challenge recommendations, make informed decisions, and develop a strategy grounded in real experience. AI strategy should not always come before action. Sometimes, the best strategy is built through carefully controlled action. #AIAutomation #AIStrategy #DigitalTransformation #IntelligentAutomation #AIGovernance #OperationalExcellence #BusinessTransformation

𝗪𝗲’𝗿𝗲 𝗢𝘃𝗲𝗿𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗣𝗶𝗹𝗼𝘁𝘀 𝗮𝗻𝗱 𝗨𝗻𝗱𝗲𝗿𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 The biggest challenge in AI today isn’t innovation. It’s industrialisation. Organisations are producing more AI pilots than ever. Yet only a fraction ever reach enterprise scale. Why? Because pilots are built to prove possibility. Production systems are built to deliver value repeatedly, securely and reliably. The gap between those two realities is where most AI initiatives quietly stall. Scaling AI is not a modelling problem. It demands governance frameworks, integration into core workflows, change management, risk management, and clear accountability for outcomes. Technology teams celebrate a successful proof of concept. Executives should be asking a harder question. Can this become part of how we operate every single day? The future belongs to organisations that move beyond experimentation and start operationalising intelligence. Innovation creates momentum. Industrialisation creates impact. #ArtificialIntelligence #EnterpriseAI #AILeadership #DigitalTransformation #AIGovernance #Vision2030 #TechLeadership #Innovation #ResponsibleAI #FutureOfWork

𝗪𝗵𝗮𝘁 𝗶𝗳 𝗔𝗜 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝗯𝘂𝗿𝗲𝗮𝘂𝗰𝗿𝗮𝗰𝘆... 𝗯𝘂𝘁 𝗲𝘅𝗽𝗼𝘀𝗲𝘀 𝗶𝘁? One of the most interesting patterns I'm seeing in AI transformation discussions is that many organizations expect AI agents to automate complexity away. I think the opposite may happen. For decades, organizations have relied on talented people to navigate broken processes, work around disconnected systems, bypass unnecessary approvals, and compensate for organizational inefficiencies. People naturally know:  • Which approval can be accelerated  • Who to call when systems disagree  • Which process step exists only on paper  • Where institutional knowledge fills the gaps 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗱𝗼𝗻'𝘁. When an AI agent fails to execute an end-to-end workflow, the problem is often not the model. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. As organizations move toward Agentic AI, the real challenge may not be deploying more intelligent agents. It may be confronting how work actually gets done. Many leaders see AI as a technology transformation. I increasingly see it as an organizational transparency transformation. For the first time, organizations are being forced to document decision rights, clarify ownership, simplify workflows, and remove years of accumulated process debt. In that sense, AI may become the world's largest process audit. The winners of the Agentic AI era will not necessarily be the organizations with the most agents. They will be the organizations willing to redesign how they operate. 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗺𝗮𝘆 𝗻𝗼𝘁 𝗯𝗲 𝗮𝗯𝗼𝘂𝘁 𝗮𝗱𝗱𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. 𝗜𝘁 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗳𝗼𝗿 𝗮 𝘄𝗼𝗿𝗹𝗱 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝗶𝘀 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲. Curious to hear from government and enterprise leaders: 𝗛𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 𝘂𝗻𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗵𝗶𝗱𝗱𝗲𝗻 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝘄𝗲𝗿𝗲 𝗽𝗿𝗲𝘃𝗶𝗼𝘂𝘀𝗹𝘆 𝗺𝗮𝘀𝗸𝗲𝗱 𝗯𝘆 𝗵𝘂𝗺𝗮𝗻 𝘄𝗼𝗿𝗸𝗮𝗿𝗼𝘂𝗻𝗱𝘀? #AgenticAI #FutureOfWork #GovernmentTransformation #DigitalTransformation #AILeadership #Copilot #PublicSector #Innovation

Defining the Intelligent Enterprise MSIF formally defines an Intelligent Enterprise as: An organization intentionally designed to sense, interpret, decide, execute, measure, and continuously learn by coordinating human intelligence, organizational intelligence, digital intelligence, artificial intelligence, decision intelligence, and executive intelligence. Several elements of this definition are important. First, intelligence is intentional. It does not emerge automatically from technology. Second, intelligence is coordinated. It requires collaboration across people, processes, technology, governance, and leadership. Third, intelligence is continuous. An Intelligent Enterprise never stops learning. #ArtificialIntelligence #AI #GenerativeAI #EnterpriseAI #BusinessAI #AppliedAI #AIInnovation #AITransformation #AIAutomation #AIAgents #AgenticAI #IntelligentSystems #MachineLearning #DeepLearning #NeuralNetworks #AIForBusiness #AILeadership #ResponsibleAI #IntelligentEnterprise #EnterpriseTransformation #DigitalEnterprise #FutureEnterprise #BusinessTransformation #EnterpriseInnovation #EnterpriseAutomation #EnterpriseTechnology #SmartBusiness

Better decisions create better businesses. Better information creates better decisions. That's why AI should begin with business insight—not technology. At CIO Dynamics, we work with organizations to identify where better information can improve operational performance. Before recommending AI solutions, we help clients understand: ✔ Which decisions have the greatest business impact ✔ Where information is delayed or fragmented ✔ Which processes limit visibility ✔ How AI can support faster, more confident decision-making AI is most valuable when it helps leaders reduce uncertainty—not increase complexity. Organizations that combine strong leadership with trusted information are better equipped to improve cost, schedule, risk, and overall business performance. If your organization is ready to improve decision quality through practical AI adoption, let's start the conversation. Veronica Cortes Norman #CIODynamics

𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗯𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗼𝗻𝗲 𝘄𝗲𝗹𝗹-𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. As AI adoption continues to grow in manufacturing, one pattern stands out: organizations that achieve meaningful results rarely begin with broad transformation goals. Instead, they start by addressing a specific operational challenge where success can be measured. A focused approach helps reduce risk while building confidence across the organization. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗮 𝗳𝗲𝘄 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: 𝟭. 𝗧𝗮𝗿𝗴𝗲𝘁 𝗮 𝗵𝗶𝗴𝗵-𝗶𝗺𝗽𝗮𝗰𝘁 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲. Whether it's reducing unplanned downtime, improving quality inspections, or optimizing production scheduling, selecting a clearly defined use case makes it easier to evaluate outcomes. 𝟮. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. Establishing performance metrics before implementation helps determine whether the initiative is delivering operational value and where refinements are needed. 𝟯. 𝗦𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗽𝘂𝗿𝗽𝗼𝘀𝗲. Once a solution demonstrates measurable improvements, organizations are better positioned to expand AI into other areas using the knowledge and experience they've gained. AI initiatives tend to deliver stronger long-term results when they are guided by business objectives, supported by quality data, and aligned with operational priorities rather than technology trends alone. 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗲𝗱 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗵𝗼𝘄 𝗼𝘁𝗵𝗲𝗿𝘀 𝗮𝗿𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗶𝘀: What business challenge would your team solve first with AI? #Manufacturing #ArtificialIntelligence #ManufacturingTechnology #OperationalExcellence #DigitalTransformation #Innovation #InfoMatrix

𝗠𝗼𝘀𝘁 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗱𝗼𝗻'𝘁 𝗵𝗮𝘃𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗮 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. As conversations around AI accelerate, it's tempting to focus on models, copilots, and autonomous agents. In my view, the organizations that will create the greatest value won't simply be those that adopt AI first— They'll be those that experiment early, learn deliberately, and continuously strengthen the operational foundations, trusted data, governance, analytics, and human-AI collaboration needed to scale AI with confidence. Agentic AI isn't something organizations should wait for until they're "ready." It's something they should begin exploring today through focused, well-governed use cases that build capability, create organizational learning, and establish the foundations for broader adoption tomorrow. The framework below summarizes how I believe industrial organizations can navigate that journey. I'm interested in your perspective. 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗺𝗼𝘀𝘁 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘁𝗼𝗱𝗮𝘆? - Building trusted data and operational visibility? - Strengthening analytics and governance? - Experimenting with agentic AI pilots? - Or already scaling AI across core operations? I'd love to hear where you're seeing the biggest opportunities—and the biggest barriers. #IndustrialTransformation #Manufacturing #Operations #OperationalExcellence #DecisionIntelligence #AgenticAI #ArtificialIntelligence #DigitalTransformation #Industry40 #Leadership Robin Sturgis Philip Topham Sharon Beemer, NACD.DC, MBA Stefanie Pluschkell, PhD, MBA Ritesh Patel