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Use an AI agent to automate boring front-end work by listening to Slack for bug reports and automatically creating first-draft pull requests to fix them. This saves a tremendous amount of engineering time and keeps engineers focused on higher-leverage work.
Prevent reps from entering deal values before meeting the prospect by enforcing a stage gate: only allow deal value entry after a certain stage is reached. This prevents inflated pipeline coverage and misleading leadership.
Slop (automated spray-and-pray outreach) sometimes converts, but it creates messy pipeline with unqualified deals that may not be ICP, lowering conversion rates and creating noise that looks like real opportunities. It can burn through your total addressable market (TAM) quickly and damage email deliverability and credibility.
Start building AI workflows yourself instead of waiting for engineering. Block a few hours to create a simple Clay signaling or pipeline workflow. A sales leader can build a sophisticated intent-signaling Clay table in about six hours without needing an engineer.
Build an ICP score and use it to tier territories (tier one through three), then build separate playbooks for each tier based on conversion behavior. When lower-tier accounts (tier three) convert at a high rate, rebuild the ICP score and create a separate playbook for those accounts (e.g., quick land/beachhead deals vs. large platform deals).
Replace rigid activity benchmarks (emails sent, calls made) with quality-based outbound metrics that measure personalized outreach, events, social connections, and solutions-based engagement. Learn from top reps and rebuild the outbound playbook.
Build a basic ICP score using Clay to identify target accounts and create a targeted list for outreach. Build a Clay table to set up basic intent signals (e.g., prospect visited your website, hired someone with a relevant title) and fire alerts into Slack for real-time notifications. Build a Slack canvas to create an ongoing briefing of focus areas from the signaling system.
Shaun Crimmins recommends using a forward deployed engineer to win over technical stakeholders, because engineers respect other engineers. Use forward deployed engineers (FDEs) to build technical champions by embedding them with the customer to build and ship solutions that improve the customer's daily workflow. Arm the forward deployed engineer with context about what the customer is trying to accomplish and what matters to them before the engagement. Have the FDE ask the customer engineer about their daily workflow, challenges, and world to uncover pain points before building a solution. Have the FDE return to the customer engineer within a week with a built solution that addresses the uncovered workflow challenges.
Shaun Crimmins recommends aligning on success criteria up front before starting a pilot.
Shaun Crimmins recommends mapping each value touch throughout the pilot in advance, planning exactly when you deliver value and what your ask will be. In week one, improve a metric the customer didn't know they had (e.g., knowledge base coverage from 30% to 90%) and hand them the improved asset at the end of the pilot regardless of outcome. Use the improved metric from week one as leverage to ask for access to additional tools or integrations in week two.
Shaun Crimmins recommends including three slides before dropping into a demo: the biggest problems learned in discovery, how you solve those problems, and what success looks like today. Set up your demo so it could work without you in the room.
Before launching an AI agent, ensure it works exceptionally well, not just 'good enough', to avoid high churn. Many AI agents are average or require babysitting and manual work, leading to high churn rates of 20-40% per month. If you overpromise on an AI agent's capabilities, customers will cancel within a month or two. Do not build an AI agent that is solidly mediocre or only does 80-90% of the job; it's not enough to replace a human. Tune and optimize the AI to be exceptional. Do not ignore the cost of the AI agent; it could hit your profit.
Rob Walling provides techniques to overcome fear of social interaction: hire a therapist or coach to join calls initially, use an LLM to draft outreach emails and prepare call scripts, practice sales conversations out loud (record yourself, use a mirror, practice while driving), and take small progressive steps. He emphasizes that repeated practice trains the nervous system not to go into fight/flight/freeze mode, and reminds that social interaction is not a life-or-death threat.
Derrick Reimer recommends using an LLM (e.g., Claude Code) to help a new developer understand the codebase by asking questions about major subsystems and reading Git history. He also suggests pair programming for 1-2 hours a day for the first couple of weeks to infuse team ethos and build rapport.
confident
Erik Allebest advises not assuming a growth spike from a macro event (like COVID or The Queen's Gambit) is permanent; wait to see if the baseline holds. He describes the 'white-knuckle' scalability issues across product and tech during rapid growth, and notes that growth became more predictable after building the right team.
Hodak recommends pairing a long-term research project (e.g., bio-hybrid neural interfaces) with a near-term commercial product (e.g., retinal prosthesis) to sustain the business, and emphasizes that having a real business is critical for a moonshot.
Choose exercises where the arm finishes back behind the body, putting the long head in its most shortened position.
Choose exercises that put the shoulder in flexion and the elbow up overhead to stretch the long head of the triceps.
Give the AI agent file tools to read, search, and list files across the repository, so it can follow changes beyond the diff into pipeline definitions and access grants. Restrict the AI agent to repository tools only — it cannot query the live catalog, lineage tool, or production state; live facts stay layer one's job.
Configure the agent's rules by change type in the PR, knowing what layer one already covers, targeting query anti-patterns, sensitive data asks, and consumers outside lineage. For schema changes, have the agent walk the pipeline definitions and access grants in the repository to find consumers that were not registered in lineage.
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