What is Executive AI in Stok.ly?
Executive AI is the tier that briefs leadership.
It performs the work a senior analyst does when asked “what should I be worried about” — reading across orders, fulfilment status, customer lifetime value and margin data, distinguishing a systemic pattern from an isolated incident, putting a number on it, and saying who owns it.
It runs in minutes rather than an afternoon, on live data rather than last week’s export and it does not stop at the document.
The same session that produces the analysis produces the operational change set.
“Which customer’s orders are at risk of not being fulfilled in the next 14 days because we do not have the stock on site? Rank by customers who spend the most with us.”
Not only will the AI research across orders, inventory, pre-allocations and Purchase Orders to establish fulfilment capability, the AI will recommend changes to pre-allocations and action those changes to help ensure your most important customers get what they were promised on time.
The Problem: Risk Hides Until Someone Goes Looking
Which accounts are quietly buying less than they used to?
Which fulfilment failures are actually a systemic pattern rather than a series of isolated incidents?
The Executive AI is designed to deliver what your board actually needs to hear this week.
“The problem with reports, is that we need to know what we are looking for. The executive AI helps us dig into the data and unearth the answers in a fraction of the time it would take to trawl through spreadsheets.”
If you rely on reports, it means that someone senior has to go looking, cross-reference systems and build the narrative by hand — which usually happens after the damage is already done or after the customer has called.
Executive AI enables you to be proactive and look ahead.
Reports show you what has already happened – often when it’s too late.
“Is this a one off or is there a pattern here?”
The CEO Risk Review
We asked for a CEO-level risk review on our best customers.
The AI read across the full operational record and found 19 partially fulfilled or delayed orders for a single customer spanning 6 months.
It characterised this as a systemic pattern rather than a service incident, quantified £32,272.60 of revenue at risk, flagged revenue concentration risk and identified the customer as a top 1% of revenue generators for the business.
It assessed margin impact.
Its recommendation was to treat the situation as a named-account recovery programme rather than a normal order-clearing exercise.
Without being asked, the AI proposed and produced a board-style RAG summary covering four risk domains: revenue risk, margin risk, customer confidence risk and operational control risk.
Each carried a rating and a plain-English board implication translating the operational finding into strategic consequence.
The root cause table attached a confidence level — high or medium — to each finding based on the quality of the underlying evidence, which is what allows a board to distinguish what to act on now from what needs further investigation.
It then offered to produce a one-page board pack slide.
The Allocation Strategy – The Action
Asked for an allocation strategy, the AI named the customer as the one to protect.
It had independently assessed which incoming stock could be relied on and had flagged a purchase order as unreliable because its expected date was already overdue.
Seven SKUs, with channel conflict identified per SKU, protected quantities for the priority account and recommendations to restrict the ecommerce channel on the affected products while releasing surplus elsewhere.
The action list
The AI produced a complete, risk-sequenced, execution-ready stock pre-allocation change list, ending in one line: reply “approve” if you want me to proceed.
The thread took nine minutes.
One continuous thread of reasoning.
Live data throughout.
No export, no spreadsheet, no separate report to reconcile.
The Result
Stock was re-allocated to ensure delivery commitments were met for this high spending customer.
Trust your stock. Transfer with confidence. Control orders. Fulfill every day like clockwork.
G2 Customer Reviews
What do you like best about Stok.ly – Inventory-Centric Cloud ERP?
“we have been a user for the last 4 years and we cannot think of life without it for our retail business. it makes listing to shopify and other marketplaces easy, keeps inventory accurate online and the POS is easy to use. accurate inventory across all sales channels is the big win for us.”
Order management and inventory ERP software for growing retail, wholesale and distribution businesses.
Why Continuity and Context Are Critical
The interesting behaviour outlined in the above example is that the AI Assistant retained context across a 10 minute conversation and investigation.
The analysis of the customer in the first minute was still being called on 10 minutes later and still governing the final action recommendations.
That is the difference between a tool that answers questions and one that holds a line of reasoning.
It is also what makes the sequence end in an action rather than a document — because by the time the analysis is complete, the system already knows what needs protecting and why.
Read the full methodology on the Continuous Advisory Session page.
Board and Investor Reporting
The board output in the above example was not a dashboard screenshot.
It was a structured document: RAG status per risk domain, a strategic implication paragraph per section, a confidence-calibrated root cause table and a one-page slide format with headline, RAG dial, top risks and next-seven-days actions.
Two things make this document instantly presentable to the board.
First, the confidence calibration — a board that cannot tell high-confidence findings from provisional ones cannot prioritise.
Second, the data currency: the document is generated from the live operational record, so there is no reconciliation step between what the board is told and what the system holds.
It Finds Growth, Not Just Risk
The same intelligence layer runs in the other direction.
For every B2B account, Stok.ly’s AI compares what a customer currently buys against the highest-margin products in your range, calculates the untapped revenue if they bought those too, and ranks every account by opportunity — producing a prioritised list with a specific product recommendation per account, formatted for a sales team to act on rather than a report to interpret.
This capability is live in the platform.
Ask the AI Assistant to review customers with a similar profile to your highest spenders and make recommendations on what products to offer them.
Example Questions the Executive AI Answers
- What should I be worried about this week that I do not already know about?
- Which high-value customers have delayed or partially fulfilled orders, and how much revenue is at risk?
- Is this a run of isolated incidents or one systemic pattern?
- Which of my accounts have quietly reduced their spend, and when did that change?
- Who owns this problem?
- Produce a board RAG summary of operational risk, with confidence levels against the evidence.
- Which operational changes would have the highest commercial impact?
- Which B2B accounts have the most untapped revenue potential, and what should the sales team lead with?
Governance: What Stops This Going Wrong
An AI that can commit operational actions and brief a board needs to be interrogable. Four controls apply.
- Approval gates. Nothing is committed to the operational record without an explicit confirmation, and the AI presents its full workings before asking.
- Confidence calibration. Findings carry a confidence level tied to evidence quality, and forecasts carry the number of days of history behind them. The system distinguishes what it knows from what it infers.
- Self-assessed data quality. In the session above, the AI independently identified a purchase order whose expected date was already overdue and excluded it from reliable supply.
- Traceability. Recommendations resolve back to the source records and the pre-computed fields they were derived from, so a finding can be checked rather than trusted.
The honest test for any ERP AI is not whether it produces a confident answer.
It is whether it tells you when it should not be confident.
Why This Doesn't Exist Anywhere Else
Positions below reflect vendor-documented capability as at 27 July 2026.
Cin7’s Ask Me Anything answers plain-English questions about live data, returns graphs and source links, and maintains conversational context within a session — the strongest conversational offering of the three competitors. It is documented as an insight, guidance and troubleshooting tool.
Brightpearl’s Sage Copilot surfaces prioritised, page-scoped insights inside Inventory Planner and links to the screen where action is required; it is a Premium-only beta.
Orderwise’s AI programme, MyForterro AI, is invitation-only and proof-of-concept, with human validation explicitly required before any AI-generated output is committed externally.
None of them documents board-format executive intelligence, cross-domain risk synthesis with named owners and quantified exposure, or a continuous advisory thread that carries a strategic finding from one query into an operational recommendation in the next.
Frequently Asked Questions
Can Stok.ly identify high-value customer risk automatically, without me asking a specific question?
Yes. A CEO-level review reads across the full operational record — orders, fulfilment status, customer lifetime values and margin data — and surfaces concentrated risk without being pointed at it. In a real session it identified 19 partially fulfilled or delayed orders for one customer across nine months, quantified £32,272.60 of revenue at risk, characterised the pattern as systemic rather than incidental, flagged concentration risk, and named the account owner without being told who that was.
Can the AI produce board-ready documents?
Yes. It produces a RAG-format summary covering revenue risk, margin risk, customer confidence risk and operational control risk, each with a plain-English strategic implication, plus a root cause table with confidence levels attached to the evidence and a one-page board slide format with headline, RAG dial, top risks and next-seven-days actions. It is generated from the same live data as the review that preceded it, so there is no reconciliation step.
Does the AI remember what it found earlier in the same session?
Yes, and this is the capability that matters most. A finding from one query carries into the next without being restated. In the demonstrated session, a customer identified as the critical risk at 14:29 was named as the customer to protect in an allocation strategy at 14:36, in a different query, unprompted. That continuity is what allows analysis to become an approved action within a single sitting rather than a document someone acts on later.
How long does a CEO risk review take?
In the demonstrated session, the full sequence — CEO risk review, board RAG summary, allocation strategy and execution-ready action list — ran in nine minutes on live production data. The individual risk review was the first output, at the start of that window.
Does the AI propose its own next step, or only answer what it is asked?
It proposes. After the CEO risk review it offered a board RAG summary without being asked, and after producing that it offered a one-page board pack slide. The behaviour is deliberate: an advisory sequence that requires the user to know what to ask next is still leaving the hard part to the user.
How do I know the AI’s findings are reliable?
Findings carry a confidence level tied to evidence quality, and recommendations resolve back to the source records and pre-computed fields they were derived from. The AI also assesses its own inputs: in the demonstrated session it independently flagged a purchase order whose expected date was already overdue and excluded it from reliable supply, and it declined to fully quantify margin impact because the underlying weighted average cost migration was incomplete. It states what it does not yet know.
Can Stok.ly identify which B2B customers have the most growth potential?
Yes. It compares each account’s actual purchases against the highest-margin products in your range, calculates the untapped revenue if they bought those too, and ranks accounts by opportunity with a specific product recommendation each, formatted for a sales team to act on. This capability is live in the platform, though a formal demonstration output has not yet been produced in a deal context.
Can the AI identify who in my team owns a problem?
Yes. In the demonstrated session it identified the account owner responsible for the at-risk customer without being told who that was, by reading the relationship from the operational record. A recommendation with no named owner is a recommendation that does not get actioned.
Do Cin7, Brightpearl or Orderwise offer anything equivalent?
Not at this level, on the public evidence as at 27 July 2026. Cin7’s Ask Me Anything is the strongest conversational tool of the three and returns data, graphs and source links, but is documented as an insight and guidance tool. Brightpearl’s Sage Copilot surfaces prioritised page-scoped insights inside Inventory Planner and is a Premium-only beta. Orderwise’s MyForterro AI programme is invitation-only and proof-of-concept. None documents board-format executive intelligence, cross-domain risk synthesis with quantified exposure and named owners, or a continuous advisory thread that carries a finding into a later operational recommendation.
Is anything committed to my system without approval?
No. Every action that changes the operational record passes through a single explicit approval gate, and the AI presents its full workings before asking. The demonstrated session ended with the AI preparing an action list and waiting on the word approve.
Contact Information
All our sales, support and development team are located in Hereford and Cheltenham in the U.K. Please submit the contact form and we will contact you within the same business day.