I realize that different Al engines are constantly learning and refining, but basic things like sports teams and schedules you would expect to be old hat.
But that isn’t the case apparently.
If you look in these images you see how Google thoroughly messed up three questions I asked the other day.
Three strikes and you’re out.
Which, incidentally, if the Oilers don’t get their act together, they will be playing baseball very soon: (mocking artwork courtesy of Microsoft Copilot)
This bizarre advice highlights a fundamental flaw in AI training approaches, where the inability to discern sincerity from satire leads to absurd and potentially dangerous outcomes.
In AI’s defense, apparently glue does help keep cheese on the pizza.
I often have discussions about the role of AI in organizations and innovation processes.
At its core, the inquiry is straightforward: Where does AI fit in?
While not an exhaustive list, here’s some proposed AI use cases for different business units, as well as AI-related roles:
Design: – AI tools for ideating, prototyping, and designing novel product/service experiences – AI systems to analyze user data/feedback for deriving insights – AI for optimizing service flows, usability, accessibility in designs
Engineering/R&D: – Generative tools utilizing AI for optimized product designs, materials, simulations – AI-powered ideation and brainstorming assistants for generating new product/feature ideas – AI systems to analyze patent databases, research papers for innovation insights – Computer-aided engineering with AI for modeling, analysis, testing
Marketing: – AI copywriting tools for content creation, ad copy, email campaigns – AI image/video generation and editing tools – AI voice assistants/chatbots for marketing interactions – AI analytics for buyer behavior modeling and campaign optimization
Sales: – AI-driven digital sales assistants for customer conversations – AI recommendation engines for cross-sell/upsell opportunities – AI contract review and negotiation tools
Operations: – Predictive maintenance and asset optimization using AI – Intelligent process automation with AI decision support – AI for planning, scheduling, supply chain optimization
Finance: – AI for financial forecasting, budgeting, spend analysis – AI-driven anomaly detection for auditing and fraud prevention – AI tools for contract/document review and analysis – AI assistants for automating financial reporting and insights
Executive/strategic level: – AI for scenario planning, simulations of strategies, investments – AI analyzing data for insights – AI-powered dashboards and natural language reporting – AI-enabled risk analysis/mitigation, opportunity identification
AI should also be integrated into the management structure.
Some possibilities: – Chief AI Officer – Business Unit AI leads – An AI Program Management Office (AI PMO)
Of these, the AI PMO is the hub, the locus, a centralized team responsible for overseeing the company’s AI strategic implementation and project portfolio.
This centralized-yet-embedded capability can identify high-value use cases, deploy the appropriate AI tools, provide training, and ensure adherence to ethical principles – all while aligning AI efforts with core business objectives.
Ultimately, the implementation of AI needs to be done with empathy and with feedback from the individuals who will be using these tools. The seeds of AI can be planted from above, but adoption and effective implementation can only occur when the people using the tools believe in them and embrace them.
The possibilities that can be realized with AI are boundless, constrained only by the limits of one’s imagination. By thoughtfully integrating AI capabilities across vital functions, an organization can position itself for success in the AI-driven future that lies ahead.
If love to hear your thoughts on how AI gets integrated into organizational cultures . What would you do? Are certain business units more critical than others for integrating AI?
Both humans and AI default to predictable, unimaginative ideas when they first start brainstorming.
What I like to do to maximize AI’s value is look for what’s missing, not just what’s generated. Find gaps in AI’s generated ideas and explore directions it doesn’t take.
Using AI effectively requires us to engage our critical thinking and ask thoughtful questions. AI is a tool, and intelligently used tools are always more effective.
A hammer can put a hole in a wall as effectively as it can drive a nail.