Today's Agenda
Hello fellow humans! Today, there is big news around agents, and for most people, it may not be clear what that means, but the AI powerhouse companies are trying to reshape our entire information ecosystem, and the pace of change is breathtaking. OpenAI and Nvidia have both declared that the 2025 is the year of the agent.
News
Google AI Studio
Google quietly launched the Google AI Studio product that lets developers integrate any of Google’s generative AI tools into their vibe-coded software projects. You can now easily incorporate Nano Banana, Veo, and natural speech into your software. I have a hard time imagining how Nano Banana or Veo play strong supporting roles in a project, but easy access to speech and Natural Language Processing tools sounds powerful to me.
80% of Orgs Struggle with Agentic Implementations
A new Cloudera survey of 1,484 enterprise IT leaders across 14 countries in February 2025, found that 96% plan to expand AI agent use in the next 12 months, with 83% believing such investment is essential for competitive advantage. Top applications include customer support (78%), process automation (71%), and predictive analytics (57%). However, 53% cite data privacy concerns as their top barrier, followed by integration challenges (40%) and high costs (39%).
Here are four actionable insights from this report:
Start small with high ROI projects: Begin with clearly defined, low-complexity use cases like internal IT support agents that deliver fast value, then scale gradually.
Establish accountability before deployment: Assign clear ownership for agent performance—whether to developers, business owners, or operations teams—before building governance frameworks.
Strengthen data foundations first: Invest in modern data architecture and unified platforms that provide consistent security controls to address the top barriers of privacy, integration, and data quality.
Understand where AI excels and where it stumbles: It is a powerful tool for predictive analysis in the hands of someone who already has those skills. It can scale your existing strengths, but it can’t provide strengths that your human staff doesn’t already possess; that kind of transformation requires human skills.
These will look familiar to anyone with a product management background. As more firms drive AI initiatives, more teams will find that AI agents are products, and they need to think like product managers.
You can also find an analysis of this report at AIthority.
AI is a Force Multiplier for Human Skills - World Economic Forum
The World Economic Forum argues that AI's greatest business value lies in building long-term resilience by amplifying human capabilities rather than pursuing short-term efficiency gains. When deployed thoughtfully, AI acts as a force multiplier across sectors—from legal case analysis to manufacturing defect detection—enhancing economic competitiveness through technologically adaptable workforce. AI can also preserve institutional knowledge, acting as living archives that retain expertise, trial histories, and methodologies when employees leave.
Here are three actionable insights:
Handle transitions with candor: Be honest about displacement, manage it through retraining and redeployment where possible, and provide fair exits when necessary to maintain trust.
Start with experiments, not finished products: Treat early AI deployments as tests, start small, communicate clearly about what you're testing, and involve employees in shaping how tools work.
Develop hybrid skills: Invest in training programs that develop employees who can interpret AI outputs, provide missing context, and make judgment calls when data alone is insufficient.
These should look familiar to anyone who has been involved in product management or change management. Because that’s where AI lands us today! We’re transitioning from humans providing services crafted by product people to being tasked with creating our own tools (hopefully using product thinking) as we navigate this massive change point.
Radical Candor
This is the definition of intelligence: the ability to acquire and apply knowledge and skills.
Every benchmark out there tests the ability of a model to apply knowledge; that part's covered.
But none of them focus on the agent's ability to acquire that knowledge. And accordingly, current frontier models almost entirely lack the ability to continually learn. Yes, you can train them once, but after that, their ability to acquire new knowledge is severely limited, and that's a big problem.