It’s 2025, and AI is everywhere. It’s writing our emails, summarizing our meetings, generating our presentations, and apparently now offering to optimize our Thanksgiving dinner planning with algorithmic precision.
“Just input your guest dietary restrictions, kitchen equipment, and desired timeline,” the latest AI tool chirps. “I’ll generate an optimal cooking schedule down to the minute!”
Cool. Except Aunt Margaret is going to show up an hour early with an unannounced casserole, your oven will inevitably be unreliable, and no algorithm can account for the interpersonal dynamics that make holiday gatherings equal parts joy and chaos.
This is the same energy I’m seeing in boardrooms across technology organizations. Executives who suddenly believe AI can optimize everything, solve every problem, and revolutionize every process. And as the person responsible for technology strategy and planning, I’m the one who has to turn AI hype into actual business value.
Spoiler alert: AI is a tool, not magic. And November, with its conflicting demands and limited capacity, is showing us exactly where AI helps and where it absolutely doesn’t.
The AI Hype Cycle Has Entered the Building (And It’s Exhausting)
Let me set the scene. It’s a Tuesday morning strategy meeting. You’re reviewing Q1 plans. Suddenly, someone (always someone from the business side who just read a LinkedIn post) asks: “Could we just use AI for this?”
“This” could be literally anything. Customer support. Code review. Strategic planning. Data analysis. Content creation. Process optimization. Doesn’t matter. The question is always the same: “Could AI do it?”
The answer is: Maybe? It depends? But probably not the way you’re imagining?
The AI suggestion categories I’m seeing:
The Reasonable: “Can we use AI to automate these repetitive data entry tasks?” Yes. Probably. Let’s explore that.
The Ambitious: “Can we use AI to improve our customer service response times?” Maybe. Depends on implementation, training data, and whether you’re willing to accept some AI-generated weirdness.
The Delusional: “Can we use AI to replace our entire engineering team?” No. Stop talking. We need to have a serious conversation about what AI actually does.
The Desperate: “Can we use AI to solve [complex organizational problem that’s actually about people and culture]?” AI cannot fix your dysfunctional team dynamics, I promise.
As the strategy leader, my job is to translate between these categories and figure out where AI actually creates value versus where it’s just expensive theater.
The Thanksgiving Problem: When Optimization Meets Reality
Here’s why the “AI-optimized Thanksgiving” is a perfect metaphor for technology strategy planning:
The algorithm assumes perfect conditions. Your AI-generated cooking schedule assumes:
- Your oven is calibrated correctly
- All ingredients are exactly as specified
- No one will interrupt you
- Nothing will go wrong
- Everyone will arrive on time
- You can precisely follow the timeline
Reality includes chaos. In real life:
- Your oven runs hot
- The turkey is a different size than you ordered
- Someone calls with a crisis mid-cooking
- You drop something
- Guests arrive early or late or not at all
- You have to improvise constantly
The same applies to AI in business strategy:
The algorithm assumes: Clean data. Clear parameters. Stable conditions. Predictable outcomes. Rational actors.
Reality includes: Messy data. Competing priorities. Constant change. Unexpected events. Humans being human.
AI can help. It’s not going to replace judgment, experience, and the ability to adapt when things go sideways.
What AI Actually Does Well (And What It Doesn’t)
Let’s get specific. After implementing AI tools across various technology initiatives, here’s what I’ve learned:
AI excels at:
Pattern recognition in large datasets. If you have massive amounts of data and need to spot trends, AI is genuinely useful.
Automating repetitive tasks. Data entry. Basic categorization. Routine responses to common questions. Let AI handle the boring stuff.
Generating first drafts. Content, code, analysis. AI is great at giving you something to start with. Just don’t expect it to be finished quality.
Augmenting human decision-making. AI can surface insights or options you might miss. It’s a good thinking partner, not a replacement for thinking.
Scaling things that were previously manual. If something works but requires too much human time, AI might help you do it faster.
AI struggles with:
Nuance and context. AI doesn’t understand organizational politics, cultural dynamics, or unspoken contexts. It reads the words, not the room.
Novel problems. If your challenge is truly new, AI trained on historical data won’t have relevant patterns to work from.
Judgment calls. AI can give you probabilities. It can’t tell you what you should do given your specific values, constraints, and goals.
Creativity that breaks patterns. AI remixes existing patterns well. True innovation that challenges assumptions? That’s still human territory.
Accountability. When AI makes a mistake (and it will), someone human has to own it. The algorithm isn’t going to take responsibility.
The strategy question: Where in your organization does AI’s strengths align with actual business problems? Start there, not with “let’s AI everything.”
The November Reality: AI Meets Reduced Human Capacity
Here’s where it gets interesting. November is showing us exactly when AI helps and when it doesn’t.
Where AI is helping this month:
Automated scheduling and coordination. When everyone’s calendars are chaos with PTO and holidays, AI-powered scheduling tools actually save time.
Drafting routine communications. End-of-year updates, standard responses, process documentation. AI can generate drafts that humans polish.
Data analysis for year-end reporting. Crunching numbers, spotting trends, generating initial insights. This is legitimately useful.
Filtering and prioritization. When you’re drowning in requests and emails, AI can help sort what needs attention now versus later.
Where AI is not helping:
Complex decision-making. Should we pursue this strategic initiative? How do we handle this organizational challenge? These need human judgment, especially when stakes are high.
Relationship management. AI can’t substitute for human connection, especially when people are stressed and need genuine support.
Creative problem-solving. When standard approaches aren’t working (which is most of November), you need human creativity and adaptation.
Change management. AI can’t convince resistant stakeholders, navigate politics, or build trust. That’s human work.
The lesson: AI is useful for freeing up human capacity to focus on the things that actually require human skills. Use it that way.
The Strategy Framework: AI as Tool, Not Strategy
Stop thinking about “AI strategy.” Start thinking about business strategy that might include AI tools where appropriate.
The right question is not: “How can we use AI?”
The right question is: “What problems are we trying to solve, and might AI be useful for some of them?”
My framework for evaluating AI opportunities:
Step 1: Define the actual problem Not “we need to be doing more with AI.” What specific business challenge or opportunity are you addressing? Be concrete.
Step 2: Assess whether AI is a good fit
- Is there enough quality data?
- Is the task repetitive or pattern-based enough?
- Is the cost justified by the value?
- Do we have the expertise to implement well?
- What’s the risk if AI gets it wrong?
Step 3: Consider alternatives Before jumping to AI, ask: Could we solve this with better processes? Training? Different tools? Sometimes the answer isn’t AI at all.
Step 4: Start small and prove value Pilot before scaling. Measure actual impact. Be honest about results. Kill things that aren’t working.
Step 5: Plan for the human element How will people use this? What training do they need? How do we handle errors? Who’s accountable?
The litmus test: If you can’t clearly articulate the business problem and how AI specifically helps solve it, you’re not ready to implement.
The Thanksgiving Parallel: Optimizing the Right Things
Let’s go back to Thanksgiving. An AI might optimize your cooking schedule. But that’s not actually the constraint.
What AI might optimize:
- Cooking timeline
- Shopping list efficiency
- Optimal oven temperature
- Ingredient substitutions
What actually determines Thanksgiving success:
- Whether people feel welcomed
- If the food tastes good (not just done on time)
- Quality of conversation and connection
- Managing family dynamics gracefully
- Creating memories worth having
The same applies to business:
What AI might optimize:
- Process efficiency
- Data analysis speed
- Content generation
- Routine decisions
What actually determines business success:
- Strategy and vision
- Relationship and trust
- Innovation and adaptation
- Culture and values
- Execution quality
Focus your AI investments on optimizing things that actually constrain business value. Don’t optimize for optimization’s sake.
The November AI Scenarios: Real Examples
Let me share actual situations I’m navigating right now:
Scenario 1: The year-end reporting request
The ask: “Can AI generate our annual technology report?”
The reality: AI can compile data, generate initial analysis, and create draft sections. But the narrative, strategic insights, and what-it-means-for-next-year requires human judgment. AI is a great assistant here, not a replacement.
The approach: Use AI to crunch numbers and generate draft sections. Humans provide context, insights, and strategic framing. Result: Report done faster with better analysis.
Scenario 2: The customer support backlog
The ask: “Can AI handle our support tickets during November when people are out?”
The reality: AI can handle routine questions and categorize complex ones for humans. But nuanced customer issues, especially upset customers, need human empathy and judgment.
The approach: AI first-pass on tickets. Routes simple ones, escalates complex ones. Humans focus on high-value, high-emotion interactions. Result: Better customer experience with reduced load.
Scenario 3: The strategic planning crisis
The ask: “Can AI help us figure out our Q1 priorities?”
The reality: AI can analyze data about past performance, market trends, resource capacity. It cannot make judgment calls about strategic tradeoffs, political feasibility, or organizational readiness.
The approach: Use AI to surface data and options. Humans make decisions based on that information plus all the context AI doesn’t have. Result: Better-informed decisions, not outsourced judgment.
The pattern: AI augments human work. It doesn’t replace human judgment, especially for high-stakes decisions.
The Implementation Reality: AI is Still Just Software
Here’s what nobody tells you about implementing AI: It’s harder than traditional software.
Why AI implementations are complicated:
Data quality matters more. Garbage in, garbage out, but with AI, the garbage is often statistically plausible-sounding garbage. This is worse.
Training and tuning are ongoing. You don’t just “install” AI. You configure, train, monitor, adjust. Continuously.
Errors are subtle. Traditional software breaks obviously. AI makes confident mistakes that look plausible. You need humans checking output.
Change management is harder. People are more skeptical of AI than traditional tools. You need extra effort to build trust and adoption.
Costs are often higher than expected. API calls, compute resources, storage for training data, ongoing tuning. Budget accordingly.
Expertise is scarce and expensive. Everyone wants AI talent right now. Competition is fierce. Salaries reflect this.
The planning implication: AI projects take longer and cost more than you think. Plan conservatively. Start small. Prove value before scaling.
The Budget Battle: Justifying AI Investment in November
It’s budget season. Everyone wants AI budget. Your job is to figure out which requests are legitimate and which are just hype-chasing.
Red flags in AI budget requests:
“We need to invest in AI to stay competitive.” (Translation: I don’t have a specific use case, just FOMO.)
“AI will pay for itself through efficiency gains.” (Maybe, but show me the math with realistic assumptions.)
“Everyone else is doing AI.” (Everyone else is also wondering if their AI investments are working.)
“This AI tool can handle anything we throw at it.” (No it can’t. No AI is that flexible.)
Green flags in AI budget requests:
Clear business problem with measurable impact. Specific use case with defined success metrics. Realistic cost-benefit analysis.
Pilot results showing actual value. Team with capacity and expertise to implement well. Plan for ongoing maintenance and tuning.
My budget evaluation criteria:
- Is there a specific, measurable business problem?
- Is AI actually a good solution (vs. other approaches)?
- Do we have the data quality and volume needed?
- Do we have the expertise to implement successfully?
- What’s the realistic ROI timeline?
- What’s the risk if this doesn’t work?
- Can we start small and scale based on results?
If the answer to most of these is “no” or “I don’t know,” the request needs more work before getting budget.
The Human Element: AI’s Biggest Blindspot
Here’s where AI falls apart completely: Understanding humans.
AI doesn’t understand that your developer is going through a divorce and needs flexible hours. That your PM is a new parent operating on no sleep. That your team has been through three reorganizations and is change-fatigued.
AI doesn’t know that the CFO and CTO don’t get along, so you have to navigate political dynamics carefully. That the cultural norm in your organization is to say yes in meetings and express doubts later. That timing matters as much as content when proposing strategic changes.
AI can’t tell you:
Whether your team has capacity for another initiative. Whether this is the right time politically to propose that change. How to frame a difficult message for your specific executive audience. Which stakeholders need extra attention and support. What unspoken concerns are blocking progress.
This is where strategy leaders earn their pay: Navigating the human elements that AI can’t see or account for.
The November example: AI might analyze your project portfolio and recommend optimal priorities based on data. But it can’t tell you that launching a major change during Thanksgiving week is a terrible idea regardless of what the optimization algorithm says. That requires human judgment about human limitations.
The Ethics Question: AI in November Raises New Concerns
When people are stressed, capacity is low, and oversight is reduced (because people are on PTO), AI risks increase.
November-specific AI concerns:
Over-reliance when humans are stretched thin. When your team is overwhelmed, there’s temptation to just “let AI handle it” without proper oversight. This is when mistakes happen.
Reduced human review. Quality control suffers when people are rushed. AI output gets approved without proper vetting.
Privacy and security lapses. Stressed people make mistakes like feeding sensitive data into AI tools without checking privacy policies.
Algorithmic decisions affecting stressed humans. Using AI to make decisions about performance, workload, or schedules during high-stress periods requires extra care.
Bias amplification. When humans are tired, they’re less likely to catch AI bias or challenge problematic outputs.
The strategy responsibility: Build in extra safeguards during high-stress periods. More human review, not less. Clear guidelines about what AI can and can’t decide without human oversight.
The Practical Playbook: Using AI Strategically in November
Enough theory. Here’s what to actually do:
For year-end reporting: Use AI to aggregate data and generate initial analysis. Have humans add context, insights, and strategic framing. Don’t publish AI-generated content without human review and editing.
For capacity planning: Use AI to analyze historical patterns and project workload. Have humans adjust for November realities (PTO, holidays, reduced capacity). Don’t let algorithms dictate unrealistic timelines.
For decision support: Use AI to surface options and analyze tradeoffs. Have humans make final decisions incorporating context AI can’t see. Don’t abdicate judgment to algorithms.
For communication: Use AI to draft routine updates and documentation. Have humans personalize, add empathy, and ensure tone is appropriate. Don’t send AI-generated messages to stressed humans without editing.
For problem-solving: Use AI to generate potential approaches and identify patterns. Have humans apply creativity, experience, and contextual knowledge. Don’t assume AI’s first suggestion is best.
The pattern: AI generates. Humans judge. Always.
The Future Gazing: AI Strategy Beyond November
November is a test case. How we use AI during constrained, high-stress periods reveals what’s actually valuable versus what’s just convenient when times are good.
What I’m learning:
AI works best for well-defined, repetitive tasks. The more unique or contextual the problem, the more human judgment you need.
AI augmentation beats AI replacement. Tools that make humans more effective work better than tools trying to eliminate humans.
AI needs human oversight. Not just initially, but ongoing. The “set it and forget it” approach fails.
Context is everything. AI that works great in one situation might fail in another due to differences AI can’t detect.
Change management is the real challenge. Technology is rarely the constraint. Getting people to adopt and use it effectively is the hard part.
What this means for 2026 strategy:
Focus AI investments on augmentation, not replacement. Build robust human oversight into all AI implementations. Start with clear problems, not cool technology. Measure actual business impact, not just “AI adoption.” Plan for ongoing tuning and maintenance, not one-time implementation.
The controversial take: Most organizations would get more value from improving basic processes and data quality than from implementing AI. Fix your fundamentals first. Then add AI where it actually helps.
The Thanksgiving Wisdom: Optimize What Matters
Back to Thanksgiving. You could spend hours optimizing your cooking schedule with AI. Or you could:
- Focus on recipes you know work
- Ask guests to bring dishes (delegate!)
- Accept that timing won’t be perfect
- Prioritize conversation over perfection
- Enjoy the process instead of stressing over optimization
The meal won’t be algorithmically optimal. It will probably be better because you focused on what actually matters: people, connection, and food that tastes good.
The business parallel:
You could AI-optimize every process and decision. Or you could:
- Focus on solving real business problems
- Empower your team with good tools and judgment
- Accept that not everything needs optimization
- Prioritize outcomes over process perfection
- Build systems that support humans instead of replacing them
Your organization won’t be algorithmically optimal. It will probably be more successful because you focused on what actually matters: Strategy. Execution. People. Value creation.
The Reality Check: AI is a Tool, You’re the Strategist
AI is not going to do your job. It’s not going to replace strategy and planning leaders. It’s not going to make hard decisions for you.
What AI will do: Give you more data. Automate routine tasks. Surface patterns you might miss. Generate options to consider. Free up time for higher-value work.
What you still have to do: Set direction. Make judgment calls. Navigate politics. Build relationships. Create vision. Execute strategy. Lead people. Adapt to change. Take responsibility.
The November test: Can AI help you get through this chaotic month more effectively? Sometimes yes. Can it replace your judgment about what matters and how to proceed? Absolutely not.
Use AI where it helps. Trust your judgment for everything else.
The Final Word: Technology Serves Humans, Not Vice Versa
As we rush to implement AI everywhere, it’s worth remembering why we do technology strategy at all: To create business value. To support people doing meaningful work. To solve real problems.
AI is means, not end. If an AI implementation makes processes more efficient but makes humans miserable, that’s not success. If it optimizes one metric while destroying culture, that’s not strategy. If it removes human judgment from decisions that need human judgment, that’s not progress.
The question is never: “Should we use AI?”
The question is: “What problem are we solving, for whom, and is AI the right tool?”
Sometimes yes. Often no. Always worth asking.
Now go forth and plan your Q1 strategy. Use AI where it helps. Trust your judgment. Navigate the politics. Build something valuable.
And maybe let AI optimize your Thanksgiving cooking schedule. Just have a backup plan for when the algorithm meets reality.
