Category: AI
-
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…
-
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.
-
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.
-
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.
-
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?
-
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.
-
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.
-
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.
-
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.
-
AI Companies Bet on Subscriptions: Many Will Repeat SaaS’s Worst Mistakes
SaaS and AI companies increasingly adopt subscription models, often incorporating usage-based pricing. While these models can be beneficial, success relies on delivering real value and aligning pricing with actual usage. Continuous customer support and reducing friction are crucial for retaining customers and driving ongoing engagement in subscription-based businesses.
-
Scoring AI ROI in Customer Experience: What CIOs Need to Know
CIOs are missing significant ROI opportunities in customer experience, particularly in contact centers, which require attention beyond just infrastructure. Engaging with call center agents and analyzing KPIs can uncover areas for improvement. AI innovations from Verint have demonstrated substantial financial benefits, enhancing efficiency and customer satisfaction. Emphasizing AI in contact centers presents a valuable opportunity…
-
5 Essential Questions for CIOs on Planning IT Careers in the AI Era
The rise of AI in IT roles is causing concern among employees about job security and their future IT careers. While CIOs are improving employee skills and career development, a structured career planning approach is lacking. AI is transforming roles, necessitating a focus on new skills and leadership capabilities for future growth.