Category: AI, Data Governance
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How to Accelerate AI’s Path to Business Value in Industrial SMBs
AI tools can boost individual productivity but require leaders to re-engineer workflows for ongoing business value. By fostering AI literacy among all employees and utilizing platforms, partners, and learning programs, organizations can empower staff to adapt and innovate. Successful AI adoption hinges on collaboration and employee engagement in dynamic workflows.
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5 Ways AI Governance Lowers the AI Hallucination Tax
We define the “hallucination tax,” as costs incurred when AI outputs are incorrect but presented with confidence. Key strategies for managing this include developing a unified AI registry, establishing guardrails, and monitoring data contexts to enhance AI reliability and reduce costs. A recap of our recent Coffee With Digital Trailblazers with special guest Felix Van…
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Twenty-Five Years Later: My 9/11 Story
In this reflective account, Isaac Sacolick recounts his experience during the 9/11 attacks, blending personal narrative with professional urgency. He details his journey to a merger meeting, interrupted by news of the World Trade Center collapse. Amidst confusion and panic, thoughts of colleagues and loved ones surface, revealing the day’s profound impact on his life.
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150+ AI Tools for Agile Product Managers
A research study revealed over 150 AI tools for product leaders.A I is reshaping the roles of agile product managers and owners, pushing them towards customer-centric tasks while leveraging AI tools for efficiency. Examples include enhanced prototyping with AI coding harnesses. For effective use, integrating AI with a clear operational purpose is crucial.
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10 Signs of the AI Bubble Bursting and 7 Ways CIOs Should Prepare For It
The potential for an AI bubble to burst raises critical questions for CIOs on their AI strategy. While some deliver AI business value, many struggle with ROI and governance. 7 recommendations for CIOs include addressing detractors, catching up on AI governance, and addressing AI debt.
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How Knowledge Management Drives AI’s Context Layer: 5 Industry Examples
Knowledge management is a key process for developing the AI’s context layer. The article explores opportunities and challenges in five industries: healthcare, financial services, construction, manufacturing, and higher ed. Common challenges include change management, executive alignment, data integration, and data governance. Opportunities include faster time-to-decision, improved CX, and enabling agentic AI operations.
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Building the AI Agent’s Brain: Knowledge Graphs vs. Semantic Layer vs. Context Layer
Confused by the jargon on the context layer surrounding the AI agent’s brain? What’ the diffeence between knowledge graphs, the semantic layer, and and the context later? What AI governance is required, and how can organizations develop the AI brain iteratively?
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How Citizen Developers Should Respond to Allegations They Are Shadow IT
The role of low/no-code development can be significant in advancing a digital transformation strategy. It highlights the need for governance and best practices to prevent the perception of citizen development as shadow IT. Key strategies include defining building disciplines, prioritizing integrations, implementing security standards, documenting data assets, and controlling AI experimentation.
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Technology Selections in the AI Era: 7 Criteria to Evaluate a Vendor’s Ecosystem
Organizations struggle with technology selection in the AI era, often facing integration issues or overanalyzing criteria. Misjudging value and risk can lead to AI debt. Evaluating technology must focus on value, risk, and ecosystem, considering factors like data portability, integration capabilities, community sentiment, and leadership accessibility to ensure long-term success.
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AI Is Only as Good as Your Talent: Five Ways People Amplify Its Value
As AI transforms the workforce, organizations must adapt talent strategies to leverage human skills that machines cannot replace, such as critical thinking and ethical judgment. Cooperation between talent and AI enhances innovation and drives business value. Companies must prioritize skilled talent nurturing to maximize AI effectiveness, ensuring ongoing collaboration and expertise.
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Should Citizen Developers Vibe Code — or Vibe No-Code?
Are vibe coding tools appropriate for citizen developer – or is what they really need a vibe no-code experience. The choice requires a review of governance models, company culture, and developer skills. Examples of effective implementations, like Quickbase, are showcased, advocating for a careful evaluation of vibe no-coding for empowering developers.
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3 Leadership Skills AI Rewrote — and 2 That Didn’t Exist Five Years Ago
The discussion at Coffee With Digital Trailblazers emphasized the need for mid-career leaders to reskill, focusing on leadership skills tailored for the AI era. Essential skills identified include change management, critical thinking, data governance, and AI agent orchestration, highlighting how AI reshapes leadership roles and enhances business value.