How to use the ActionAI live sales dashboard

Use this section to frame who you are speaking with and which business problem the conversation is about.

• Company: enter the target company name so the generated summary is ready to copy after the call.
• Industry: choose the sector. This helps shape the pitch because a bank, hospital, manufacturer, and government agency all care about different risk language.
• Primary buyer: choose the person you are speaking with or trying to reach. CIOs care about architecture and security. COOs care about throughput. CFOs care about cost and control. Compliance cares about auditability.
• Workflow: choose the workflow that could become the first paid engagement. Keep it specific. “Invoice processing” is stronger than “AI transformation.”

The fit score helps decide whether this company deserves serious follow up.

• 0 to 24: weak fit. They may be curious about AI, but the business case is not strong yet.
• 25 to 34: nurture. There may be a future opportunity, but more pain, budget, or executive ownership is needed.
• 35 to 39: priority target. The account has enough signals to justify a real ActionAI conversation.
• 40 to 50: elite target. High volume, high risk, visible budget, and a clear reason to buy.
• Why it matters: enterprise sales wastes time when the account has interest but no urgent workflow. The score keeps focus on companies where one broken process is already expensive.

Check boxes only when the buyer confirms the signal or you have strong evidence.

• Visible AI budget: they are funding AI, automation, data, or transformation this year.
• High document or case volume: there are enough invoices, claims, files, checks, cases, or reviews to make automation valuable.
• Regulated or audit heavy: wrong decisions create compliance, audit, legal, or financial exposure.
• Manual review cost is obvious: people are spending measurable time checking documents, reconciling data, or handling exceptions.
• Existing AI pilots or automation team: they are already trying to solve this, which makes ActionAI a production reliability layer instead of a cold education sale.
• Clear executive owner: someone senior owns the budget or approval path.
• Complex enterprise systems: ERP, core banking, SharePoint, Oracle, SAP, ServiceNow, Salesforce, or other systems make reliability and integration more valuable.
• Failure risk is expensive: a bad output could cause payment errors, audit findings, legal exposure, customer harm, or operational disruption.
• Recurring monitoring need: the workflow needs ongoing model monitoring, exception tuning, reporting, and expansion support.
• Can justify large contract: the value is big enough to support six or seven figure enterprise spend.

Use rough numbers to make the business case concrete. Do not pretend it is a final proposal. It is a value anchor.

• Monthly volume: how many items flow through the process each month. Examples: invoices, claims, applications, trade finance docs, tickets, or audit files.
• Minutes per review: average human time required per item.
• Loaded hourly cost: all-in cost of the people doing the work, including salary, benefits, overhead, and management cost.
• Automation impact: realistic percentage of work that could be automated, accelerated, or redirected. Start conservative.
• Why it matters: enterprise buyers approve spend when the pain has numbers. The ROI section turns “AI sounds useful” into “this workflow costs us real money every month.”

Ask for ranges, not perfect numbers. Buyers often do not know exact figures in the first call, but they can usually estimate volume, time, team size, and exception rates.

• Open gently: “To see if this is even worth a deeper technical session, can we rough out the workflow economics?”
• Make it safe: “Ballpark is fine. I’m not treating this as a final business case. I just want to know whether the value is big enough to justify more work.”
• Use ranges: “Is this closer to 1,000 items a month, 10,000, or 100,000?” Ranges are easier to answer than exact numbers.
• Ask operational people first: managers who run the workflow often know volume and time better than executives.
• Validate later: if the number looks promising, the next meeting can confirm it with finance, operations, or process data.

Monthly volume is the number of units moving through the workflow each month.

• Ask it this way: “Roughly how many of these items does your team process per month?”
• If they do not know: ask for daily or weekly volume. “How many do you see on a normal day?” Then multiply by business days.
• Alternate question: “How many people touch this queue, and how full is the queue usually?”
• Where to find it: ERP reports, ticketing systems, AP systems, claims platforms, CRM queues, workflow tools, email inbox counts, SharePoint folders, audit logs, or monthly management reports.
• Examples: invoices per month, claims per month, applications per month, customer tickets per month, bank reconciliation items per month, trade finance packets per month.
• What to enter: use the monthly number. If they say 500 per day, enter about 10,000 per month for a 20 business day process.

This is the average human time spent reviewing or processing each item.

• Ask it this way: “When one item comes in, how long does it usually take a person to review, validate, route, or complete it?”
• Ask by case type: “What does a clean case take? What does an exception take?” Then use a blended average.
• If they do not know: ask, “How many items can one reviewer handle in a day?” If one person handles 40 per day, that is roughly 12 minutes per item in an 8 hour day.
• Include hidden time: searching systems, checking policy, emailing for missing documents, waiting on approvals, updating ERP, and preparing audit evidence.
• Do not include: waiting time where no human is working unless the delay itself creates measurable business cost. Keep the first calculation simple.
• What to enter: use the average active human work time per item. If clean cases take 5 minutes and exceptions take 30, enter a realistic blended number like 8 to 12.

Loaded hourly cost is what the company really pays for one hour of work, not just the employee’s base wage.

• Ask it this way: “For a rough model, what loaded hourly cost should we use for the team doing this work?”
• If they do not know: ask for role level. “Is this handled by clerks, analysts, finance managers, underwriters, lawyers, or compliance reviewers?”
• Safe defaults: clerical processing: $30 to $45/hour. Finance or operations analyst: $45 to $75/hour. Compliance, legal, underwriting, audit: $75 to $150+/hour.
• What loaded means: salary plus benefits, taxes, management overhead, software, office cost, and operational overhead.
• Do not overfight this number: if they push back, use a conservative number. A conservative model is more credible in enterprise sales.
• What to enter: use one blended hourly cost for the people doing the work. You can refine by role later.

Automation impact is the percentage of work that could be automated, accelerated, or redirected after ActionAI is deployed.

• Ask it this way: “If the system handled the clean cases and routed only uncertain ones to humans, what percent of this work could realistically be reduced or sped up?”
• Use exception rate: “What percent of cases are clean versus exception-heavy?” Clean cases are usually the first automation target.
• Start conservative: for early discovery, use 25% to 50%. Only use 70%+ if the buyer confirms that most cases are repetitive and rules based.
• Frame it correctly: impact does not always mean layoffs. It can mean faster close, fewer errors, fewer backlogs, higher throughput, better audit prep, or redirecting staff to higher value work.
• Use ActionAI language: “touchless for clean cases, human in the loop for exceptions.” That is safer than promising full automation.
• What to enter: use the conservative percentage of effort likely reduced or redirected. If unsure, enter 30% and let the business case prove itself.

The calculator only estimates labor value. Real enterprise value can be much bigger.

• Error prevention: duplicate payments, wrong GL coding, off-contract pricing, bad claims decisions, missing documents, or compliance misses.
• Cycle time: faster invoice approval, faster reconciliation, faster month-end close, faster underwriting, faster case processing.
• Audit and compliance: lower audit prep time, fewer findings, faster regulator response, better evidence trails.
• Working capital: faster visibility into payables, receivables, reconciliation, and financial state.
• Risk reduction: fewer silent AI failures, fewer manual misses, stronger explainability, and cleaner governance.
• How to mention it: “This calculator only shows labor. If we include error prevention, cycle time, audit effort, and risk reduction, the business case may be materially larger.”

Use the tabs during the call so the conversation stays structured.

• Discovery: use this first. Find what is funded, what is stuck, who must approve it, what failure means, and what systems are involved.
• Pitch: use after pain is confirmed. Position ActionAI as custom engineered AI workflows with reliability built in, not a generic chatbot or model wrapper.
• Architecture: use with CIO, CTO, engineering, security, or procurement. It explains source systems, platform layer, reliability layer, deployment, and security.
• Objections: use when they say they already have Microsoft, Salesforce, ServiceNow, OpenAI, or an internal team. The answer is that ActionAI adds production reliability around the stack.
• Next Step: use at the end. The goal is a 30 minute production readiness session around one specific workflow with the right stakeholders.

Capture the human context that numbers alone do not show.

• Pain and production blocker: write what is actually stopping the workflow from being automated or approved. Examples: hallucination risk, audit trail missing, bad data, no confidence score, legal approval, integration complexity.
• Stakeholders and next step: write who owns budget, who owns approval, who owns the workflow, and who must attend the next call.
• Autosave: notes save in the browser on that device. They are not shared with other people unless copied, exported, or printed.

This is the follow up tool.

• What it does: turns the account data, fit score, ROI math, qualification signals, notes, and next step into a clean summary.
• Copy Summary: use this after a call to paste into CRM, email, notes, Slack, or a message to ActionAI.
• Export JSON: saves the dashboard data as a structured file for later review.
• Print: creates a call record that can be saved as PDF.

• Before the call: enter company, industry, likely buyer, and suspected workflow.
• First 10 minutes: use Discovery. Do not pitch yet. Find the production blocker.
• Middle of call: check qualification signals and estimate ROI.
• After pain is clear: use Pitch and Architecture to explain why ActionAI fits.
• Final 5 minutes: use Next Step to book a production readiness session with the workflow owner, AI/IT owner, and compliance or finance owner.
• After the call: copy the generated summary and send it to ActionAI or save it in your pipeline.