Two dogs. Two leashes. You would think that would be a relatively simple system. Then Dusty crosses behind me, Tanner circles a tree, and somehow my left leg becomes part of the infrastructure. Everybody is moving. We have created a knot.
That is what I picture when a company tells me it has a CRM, marketing automation, sales tools, customer-success software, product analytics, and now six AI agents. My first question is not, “What else should you add?” It is: How does information move between them, what decision does it support, and what happens next?
This is the second episode in Orchestrate, the O in my G-R-O-W-T-H Framework. Gauge identified the gaps. Reframe selected the customers, outcomes, positioning, growth motions, capabilities, and technology results that deserve investment. In the first Orchestrate episode, we mapped the complete go-to-market route. Now we need to design the information, systems, decisions, responsibilities, and actions that make that route possible.
A connected GTM system is not defined by the number of platforms the company owns. It is defined by whether the right context reaches the right person or workflow in time to improve the customer outcome.
Every Team Can Have Data While the Customer Still Waits
Consider a hypothetical B2B software company trying to help more qualified trial customers reach a useful result and become paying customers. A prospect starts a trial because their operations team is overwhelmed by alerts. The prospect attended a technical session the previous week and asked whether the product connects to their monitoring environment. Now the prospect is stuck during setup.
Marketing sees a trial conversion. Sales sees a new contact. Support has not received a request. The product shows that no data source is connected. Each team has part of the story, and the customer is still waiting to get somewhere.
This is not a shortage of data. It is a failure to turn distributed information into coordinated action. The company needs to connect four things:
🐕 Context: What do we need to know?
🐕 Decision: What should that information help us decide?
🐕 Connection: How does it reach the right team or system?
🐕 Action: What happens next, who owns it, and when?
These four questions create the foundation for useful automation and AI.
Subscribe for more resources on this topic here
Question 1: What Do We Need to Know?
Start with the growth outcome, not the technology stack. In this example, the outcome is helping more qualified trial customers reach a useful result and become paying customers. What information is necessary to support that outcome? For this customer, the company may need to know:
🐕 Business problem: Why did the customer start the trial?
🐕 Technical environment: Which monitoring system, cloud environment, or related technology do they use?
🐕 Buying context: Which stakeholders have engaged, and what have they asked?
🐕 Trial activity: What has the customer completed, skipped, or attempted?
🐕 Product status: Has a data source been connected? Has the customer reached the first useful result?
🐕 Commitments: What did marketing, sales, a partner, or another team already promise?
🐕 Customer response: Has the customer asked for help, ignored outreach, or indicated a blocker?
Some of that information may live in structured CRM fields. Some may live in webinar questions, email threads, call notes, support conversations, or partner updates, and some may live inside the product. The exercise is not to collect every possible data point. It is to identify the minimum reliable context required to make the next decision.
Treat Missing Data as a Question, Not a Blank Cell
After identifying what matters, check whether the information is current, reliable, matched to the right account, and available to the people who need it. If the company does not know which monitoring system the customer uses, that empty CRM field is not merely a data-quality problem. It is a question someone or something needs to answer.
The response might be to ask the customer during signup or onboarding, enrich the account from an approved data source, extract the answer from an earlier conversation, or assign a person to confirm it. It might also be to mark it as unknown and prevent the workflow from making an unsupported assumption. The goal is not to make the database look complete. The goal is to obtain the information required to help the customer move forward.
Question 2: What Decision Should the Information Support?
Data becomes operationally useful when the team agrees on the decision it should inform. A trial customer who has not connected a data source may need setup assistance. A customer who has connected the system and reached the first useful result may need guidance on applying the solution more broadly.
A customer who has attempted setup repeatedly without success may require technical escalation, while a customer who signed up but has taken no meaningful action may need a different intervention entirely. Those are not interchangeable situations, and the team needs to agree on the signals that distinguish them. For example:
🐕 No data source connected within the expected period: Identify the likely setup obstacle and offer targeted assistance.
🐕 Data source connected but no useful result reached: Provide guidance tied to the customer’s use case.
🐕 First useful result reached: Reinforce the value, recommend the next use case, and prepare the next conversation.
🐕 Repeated failed attempts: Escalate to a specialist with the full activity history.
🐕 No response after relevant outreach: Change the contact strategy or determine whether the trial should remain active.
This turns raw signals into shared operating definitions.
Decide Where AI Can Help
Only after the information and decision are clear should the team decide where AI belongs. AI might summarize the customer’s questions, technical environment, commitments, and recent activity for the onboarding specialist. It might detect a pattern that suggests a likely setup obstacle, or recommend the appropriate next-best action based on the customer’s stage and behavior. It might also draft a customer-specific explanation or create an internal task with the relevant context.
But the team must specify the boundaries. What can AI summarize? What can it classify or flag? What can it recommend? What can it execute automatically? When must a person review the recommendation? And what happens when the information is incomplete or conflicting?
AI should not be asked to disguise missing context with confident output. When important information is unknown, the correct action may be to ask a question, route an exception, or wait for human review.
Question 3: How Does the Information Reach the Right Team?
This is where the company maps the actual connections underneath the GTM roadmap. Can product activity be matched to the correct contact and account? Can the onboarding specialist see what the customer asked during the technical session? Will sales know that setup is incomplete and that assistance is underway?
Can customer success see the milestone the customer reached before the next conversation? If a partner brought the customer into the trial, can the partner’s role and commitments be seen by the appropriate team? And if the connection breaks, who owns the repair? These requirements belong on the GTM roadmap with an owner, budget, priority, and sequence.
Budget for Data Unification Before Adding More AI
My Orchestrate stage includes budgeting for data unification and integration before adding more AI applications. Otherwise, the company asks a new tool to work around the same missing or disconnected information. That does not mean the organization needs a massive data transformation before it can begin. It means the first use case must have a reliable path for the context it actually needs. For each workflow, document:
🐕 Source: Where does the information originate?
🐕 Identity: How is it matched to the correct person, account, trial, opportunity, or customer?
🐕 Destination: Where must the context appear so someone can act?
🐕 Timing: How quickly must it arrive to remain useful?
🐕 Owner: Who is accountable for the connection and its data quality?
🐕 Failure response: What happens when the data is missing, delayed, duplicated, or assigned incorrectly?
This is the plumbing underneath the customer experience. It is not glamorous, but it determines whether the strategy works.
Question 4: What Happens Next?
The connected system needs to produce a defined action. In the trial example, the next step might be that the onboarding specialist receives a task identifying the likely setup issue, the monitoring environment, the customer’s stated problem, the relevant activity, and any previous commitments.
Sales can see that assistance is underway and does not send a disconnected message. When the customer connects the data source and reaches the agreed milestone, the next conversation reflects that progress. The workflow should specify:
🐕 Action: What exactly should happen?
🐕 Owner: Who is accountable for doing it?
🐕 Timing: How quickly must the action occur?
🐕 Context: What information should accompany the task?
🐕 Completion signal: How will the system know the action occurred?
🐕 Customer milestone: What result shows that the customer moved forward?
Without those definitions, a notification is just another signal entering an already crowded system.
Pressure-Test the Design Before You Automate It
The happy path is not enough. Pressure-test the design with realistic failures and exceptions. What happens if the contact cannot be matched to the correct account? What happens if the onboarding specialist is unavailable, or if the product event arrives late?
What happens if sales notes contradict the CRM field, or if the AI recommendation is based on incomplete activity? What happens if the customer never responds, or if a task is created but nobody completes it?
For each gap, define the failure condition, the fallback action, the responsible owner, the escalation timing, and the information required to resolve it. Put your own name beside a task too. Then send a recap with the decisions, owners, and dates. That is how we bring the dog home.
Use the Same Exercise Across the Revenue Journey
The four-question exercise is not limited to trials.
Account prioritization
What information indicates that an account deserves attention? What decision should that signal support? How does it reach the seller? What action happens within what time?
Stalled opportunities
What evidence indicates the deal is stuck? Which decision or missing stakeholder explains the delay? Who receives the context? What intervention follows?
Renewals
What product usage, support history, value evidence, stakeholder engagement, and commitments should inform the renewal plan? Who needs the combined story, and when?
Expansion
What behavior suggests that another team, use case, or business unit could benefit? Who validates the opportunity? What customer conversation should follow?
The pattern remains the same: Choose the outcome. Trace the information. Define the decision. Route the context. Agree on the response.
What Orchestrate Produces
Orchestrate gives the company the design and resource commitments required to build a connected growth system. For each priority workflow, leadership should leave with:
🐕 A clearly stated growth or customer outcome
🐕 The minimum context required
🐕 Agreed signals and decision rules
🐕 Defined roles for AI and human judgment
🐕 Mapped system and team connections
🐕 Named actions, owners, and timing
🐕 Failure paths and escalation rules
🐕 Integration, data, capacity, and budget requirements
Work the Plan is where the company builds and runs the design.
Dusty and Tanner are ready to pick up speed. I am untangling the leashes first. We will get further when every step stops tightening the knot.
Watch Episode 18 of Walking the Dogs, “Untangle the Leashes: Connecting Data, Decisions, and AI,” and choose one customer outcome to trace from signal through decision and action.
