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The 10 Best AI Stocks to Own in 2026
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GM to the Top 1% ☕
Sunday recap. Three numbers from this week, read together instead of separately, because they are describing the same problem from three different angles.
41% more pipeline from blended human-plus-AI pods over autopilot. A 63-point gap between orgs that "use AI" and orgs running it agentically. Only 7% of orgs hitting 90%+ forecast accuracy. Pull the thread on all three and one habit sits underneath every gap.
💡 THE HABIT UNDER ALL THREE NUMBERS
Every number this week traces back to the same behavior: teams reaching for a bigger or newer tool before fixing what the tool is fed or supervised by.
The pods that beat autopilot by 41% did not win because their AI was smarter. They won because a human was still making the judgment calls the agent should not make alone. The orgs stuck at 24% agentic adoption are not behind on model access. They are behind on CRM hygiene, the unglamorous data discipline nobody wants to own. The 93% of orgs missing 90% forecast accuracy are not missing a forecasting algorithm. They are missing accurate close dates and honest stage reasons entered by humans who treated CRM updates as optional.
None of these are tooling problems. All three are discipline problems wearing an AI story. The fix is the same in every case: put a human in charge of the judgment and the data quality the automation depends on, and the automation starts producing the numbers the vendor demo promised.
If you take one thing from this week into next, take this: before you evaluate another tool, audit whether you are giving your current tools clean inputs and real supervision. That single habit is worth more than any single feature upgrade you will be pitched this quarter.
🔧 THE ONE-HABIT AUDIT
Four checks that apply the same discipline across all three numbers from this week.
1. Name your judgment calls: List the decisions in your process no agent should make unsupervised. If that list is short or fuzzy, that is where the pod advantage leaks out of your own workflow.
2. Grade your own data hygiene weekly: Pick one CRM field and check its accuracy every Friday. That habit alone moves you toward the 24% doing this for real instead of the 87% claiming they do.
3. Attach a reason to every stage change: A stage without a reason is a guess. A reason is what turns 70% median accuracy into something closer to 90%.
4. Revisit before you replace: Before swapping a tool for a newer one, check whether the current tool is being fed clean data and real human oversight. Most "the tool doesn't work" problems are actually "we never gave it a fair input."
🎯 THIS WEEK'S HOMEWORK
Pick one number from this week (41%, 63 points, or 7%) that most describes a gap on your own team. Write the single habit change that would move your team toward the better side of that number, and start it Monday.
❓ QUESTION OF THE DAY
Which of this week's three numbers hit closest to home for your own pipeline?
Reply with the number. I read every one and it shapes next week's issues.
See you tomorrow.
Edward
Founder, Morning Sales
P.S. If this week convinced you the fix is discipline, not another tool, the 500 AI Sales Prompts manual is built for exactly that: prompts that force clean inputs and real judgment instead of vague automation. 27 dollars: https://store.edwardgorbis.com