AI isn’t a side experiment anymore. It’s quietly reshaping how buyers discover you, how teams think, and how work actually gets done. This is a practical look at what we’ve learned over the last year, designed to be straightforward, actionable, and usable as you head into 2026 planning.
1. Use AI as Part of Your Go-To-Market Strategy
If AI can’t find you, buyers won’t either
People aren’t just Googling anymore. They’re asking AI things like:
“I’m looking for X. Who should I talk to?”
“What’s the best option for my situation?”
Your 2026 goal is simple: make it easy for LLMs to understand, trust, and recommend you.
Step 1: See what AI sees
- Open an AI tool in incognito mode
- Ask: “I’m looking for [what you sell]. What companies should I consider?”
- Ask: “Tell me about [your company].”
- Is the response accurate? Clear? Missing entirely?
- Do this monthly and track how the answers change
If you don’t like the response, that’s not an AI problem. It’s a content and structure problem.
Step 2: Build AI-readable infrastructure
- Create an LLM.txt page. A simple, plain-text page on your site that explains who you are, what you do, who you’re for, and how you’re positioned.
- Create a glossary or thought leadership page. Define the core concepts, KPIs, and language in your industry. For example: “Top SaaS KPIs every operator should track.” This works because you’re teaching AI how to think about your space, and who to trust in it.
- Standardize your case studies. Use the same format every time: problem, solution, value delivered. Machines love clarity. Humans do too.
2. AI in Chat: Your Brainstorm Partner
The real unlock is thinking with AI
“I just need to talk this through” used to mean staring at a wall or pacing the house. Now it’s a dialogue.
When we planned our 2026 goals for Full Send, everyone used AI ahead of time to draft, refine, and pressure-test ideas. The takeaway was simple: AI does not regress to the mean if you use it well.
A solid prompt format for goal-setting:
- Share current metrics and constraints
- Include last year’s goals and progress, articulate what goals were missed and why (tell it your mistakes, it won’t judge 🙂)
- Ask it to pressure-test tradeoffs, risks, and competitive dynamics
- Have it output goals in OKR format
- Add a final check: “What would keep us ahead of the AI learning curve in 2026?”
More prompt ideas:
Pricing and packaging
- Evaluate our pricing from the perspective of value, simplicity, competitive landscape, and margin opportunity. Here’s what we sell and how it’s priced: [details]. What would you simplify or change?
CFO dashboard architecture:
- I want to design a cash flow dashboard using Shopify and QuickBooks data. Help me define:
- The key questions this dashboard should answer
- The core cash flow KPIs and charts that actually matter
- The data required from Shopify and QuickBooks
- How to pull and model the data (using Fivetran - like what tables and fields)
- Critical calculations and pitfalls like timing, refunds, and accrual vs cash
Sales strategist
- Based on this prospect, research their website, product, and founders. Then give me:
- The core metrics they likely care about and why
- Five smart discovery questions that surface real priorities and buying triggers
- What should I be pitching to them in our discussion?
3. Custom GPTs and Context Engineering
A custom GPT is like the chat feature but with specific context & instructions so it thinks more like someone on your team and less like a generic chatbot.
The key is context. That means access to client folders, Notion pages, documentation, and historical decisions.
Get ruthless about good document hygiene. Take inventory of your folder structure, naming conventions, and version control. Retire “final_v2_REALFINAL”.
Example: Growth Operator Check-In
Role: You are a pragmatic growth operator helping a founder decide where to spend attention this week.
Context: You have access to the company’s recent performance metrics (e.g., sales from Shopify / subscriptions from Stripe, web traffic & conversion, cancellations with reasons), plus basic info on business model and goals.
Responsibilities
- Identify the one or two growth levers that moved and make a hypothesis on the operational cause (based on our company actions that you know of)
- Recommend 1-3 focused actions for the upcoming week
- Flag anything that looks “fine” but when you dig down a layer, may be trending the wrong way
Operating principles
- Bias toward action over insight (like what can I do with this information)
- Try to separate/remove short-term noise
- Assume limited time, budget, and team capacity
Output format
- What changed this week (bullets, straight forward)
- Why it likely happened
- What to do next
- What not to worry about yet
If key inputs are missing, don’t make assumptions, ask questions or for more information.
Important reminder: This isn’t about automating your brain or replacing your work, it’s about leveling you up. It helps you see more, think wider, and uncover insights you wouldn’t get on your own. Pretty please don’t copy paste the output… treat it like a thought partner.
4. AI Inside the Workflow
Automation does steps. AI does reasoning.
Traditional automation handles deterministic, rule-based work like moving data & triggering actions. AI inside the workflow adds reasoning. It interprets context, weighs tradeoffs, and decides what should happen next.
A LOT of the time, you do not need a full agent. You just need an LLM inside a workflow. For example, you would not say:
“AI, go find late invoices.”
That’s a rules problem, not a thinking problem.
Example: Accounts Receivable Workflow
First, use automation to gather clean context. Then use AI to decide what to do with it.
Step 1: Deterministic automation
- Query QuickBooks to identify late invoices
- Pull invoice details and payment history
- Search Gmail for recent client communication
- Pull client notes and relationship context from Notion or a CRM
These steps are predictable, auditable, and repeatable. Structure this information into a consistent summary.
Step 2: Reasoning
Now bring in AI with a clear goal:
“Based on this full context, should we reach out to this client? If yes, draft the message the way our team would.”
This is where AI adds leverage by weighing timing, nuance, and relationship context. It decides whether to act, and how.
5. AI Agents: Doing Things
An AI agent is a system that can understand a goal, can reason, and has access to tools (think, Gmail, Slack, Excel, Hubspot) to take action. If a classic “prompt” tells you which city you should visit based on your list of preferences, an AI agent books the accommodations.
If agents feel a little sci-fi right now, or like they haven’t lived up to the hype, that’s not a failure of the technology. Most SMBs (including us!) are still catching up on trust, workflow design, and adoption.
Start small. Embed AI into everyday workflows, then let more agentic systems emerge as teams and processes mature.
Example: Client Intel Agent (Conceptual)
Purpose: Create a single, coherent monthly narrative of everything that happened with a client.
Inputs
- Meeting transcripts
- Slack threads and DMs
- Emails
- Prior month summaries
- External publicly available updates about the client
Outputs
- Key themes and decisions
- Risks and tension signals
- Scope creep or expectation drift
- Duplicate work or role confusion (did two employees ask the same thing from the client?)
- Emotional context and relationship signals
This could directly improve client relationships, your product/service, and internal efficiency.
Final Take: The Real Divide Isn’t Skill. It’s Curiosity.
The gap between AI super-users and skeptics is widening fast. Some people experiment relentlessly. Others wait to be convinced.
2026 belongs to the curious.
Be curious. Be intentional. And send it.
Reach out to our team today for a free AI strategy session!
