One screen that answers three questions for Strong's leadership: what needs a decision from us, is everything healthy, and what has it delivered. Try the queue below: approvals here are real clicks with a real audit trail.
AI prepared each of these and stopped, because Strong's policy says a person decides. Nothing moves money or reaches a customer until you act.
Recommendation: approve. Unit registered 14 months ago through a warehouse-club purchase, inside the 2-year heater coverage. Owner's photo and error code match a known heater batch; 11 similar claims this quarter.
Draft ready to send. A dealer asked whether a 50-amp, 240V circuit is enough for a customer's install. The answer quotes the current spec sheet and install guide; every claim in it was checked against approved product data.
Order read and keyed into the ERP draft, then held. The PDF purchase order and the retailer's portal disagree on the delivery address. Shipping to the wrong one risks a retailer chargeback.
We watch whether the business process works, not only whether a server is up.
Every AI action and every human decision is written to a hash-chained record. Each line seals the one before it, so a quiet edit later breaks the chain and shows.
| Time | What happened | Who | Seal |
|---|
Sample data. Agent names, volumes and results show the shape of the day-90 view; Strong's real numbers replace them once discovery sets the baseline.
Strong sells through more channels than almost anyone in the category. Every one of them ends in the same three back-office queues, and that is where AI returns money first.
A club or marketplace buyer has no dealer between them and Strong. Their warranty claim, their question and their review all land on the factory, and the speed of that answer shows up on the product listing. Public reviews across the category put service and warranty response first among complaints.
A first ranking from public information, to be confirmed in discovery with Strong's volumes. The first wave is back-office, measurable and low-risk, which is where independent research finds AI actually reaches the P&L.
| Opportunity | What AI does, and where a person stays in | Evidence | Wave |
|---|---|---|---|
| Warranty claim triageCustomer service, warranty | Reads the claim, photos and serial; checks registration and coverage; drafts the parts or technician decision. A person approves every credit. | US manufacturers paid 1.30% of product sales in warranty claims in 2025. Warranty Week, Apr 2026 | First |
| Order intake from every channelInside sales, e-commerce | Turns POs from email, PDF and retailer portals into ERP drafts and flags mismatches before they become chargebacks. | Manufacturer case studies report about 70% less processing time per order. Vendor case studies; validate on Strong's volume | First |
| Service and dealer knowledge assistantSupport, dealers, field techs | Answers spec, install and error-code questions from approved manuals, price book and warranty language only. Customer-facing replies are confirmed before sending. | Support staff with an AI assistant resolved 14% more issues per hour; newer staff 34% more. NBER working paper 31161 | First |
| Warranty-to-plant root causeQuality, manufacturing | Joins claim text to build data so leak and finish issues trace back to a line, a shift or a supplier lot in month one, not month nine. | Cheaper first step than cameras on the line: it tells you where cameras would pay. | Second |
| Supplier invoices and APFinance | Matches invoices to POs and receipts; routes exceptions. Payments stay with a person. | Best-in-class AP spends $2.78 per invoice against a $10.89 average. Ardent Partners, 2025 | Second |
| Demand and build planningSupply chain, two plants | Forecasts by channel and series to plan builds, shifts and control-pack purchases. | Needs two seasons of clean channel data; we size it in discovery. | Second |
| Dealer quoting and configuratorDealer sales | Builds accurate quotes from the current price book and options; any price exception needs approval. | Value depends on dealer quote volume; measured in discovery. | Later |
| Visual inspection on the lineQuality | Cameras at shell and plumbing stations to catch defects before shipping. | Start only once root-cause analysis shows which stations drive claims. | Later |
We don't lead with a big savings figure. We lead with a model Strong can check. The starting values are placeholders; change any of them and the result updates. Improvement rates are deliberately conservative versions of the published benchmarks.
Hours returned are capacity, not layoffs. Most clients spend them on callbacks, dealer support and peak season. Reputation on retail listings and fewer chargebacks are real value too, but we leave them out until we can measure them.
Most AI programs don't fail on the model. They fail because nobody owns the backlog, decisions go unrecorded and no one can say what the work delivered. This is the operating rhythm we already use on our own production AI platform.
An initiative moves on only when it meets its exit test. That is how pilots either reach the P&L or stop early and cheaply.
Name the process, the owner and the problem in Strong's words.
Measure today: volume, minutes, errors, cost.
Small increments, each one tested and shown.
Run beside the current process until results match.
Monitor, improve, report value monthly.
Leadership sees one line per initiative: where it is, who owns it, and whether it is earning its keep.
Sample portfolio. Discovery sets Strong's real list and owners.
We build with AI and check it with AI, and a person keeps every decision that matters. The cycle runs every working day.
Our engineers and AI build one tracked item end to end: written spec, tests first, the build, then automated checks.
Separate AI reviewers check every change for correctness and security. One runs on a different vendor's model so nobody grades their own work.
Findings arrive sorted by who may act on them. A person accepts, approves item by item, or decides. Nothing merges on its own.
A working demo, a one-page status, the decisions waiting on Strong, and value against baseline.
A dated, append-only decision log means a new team member, an auditor or Strong's CFO can see why the system works the way it does.
The figures below come from our own governed AI platform, built and operated over the last six months for a regulated, multi-site operator.
changes shipped in six months by a 15-person team
tracked work items, each traced to the changes that delivered it
decisions recorded in the log for one workstream alone
engineer-hours of review work done overnight across 16 nights
The last figure is our own estimate, using a stated model: half an hour per change reviewed, three hours per blocking defect caught before release.
Looking up a spec and issuing a credit are different risks, so they get different controls. Every AI action at Strong falls into one of four lanes, and each lane decides who has to be involved.
Application monitoring measures how the system runs. Governance measures how the AI decides.
Airiam AI Monitoring Standard
Quality score at 0.85 or above; errors under 0.5%.
Close to a limit; Airiam acts before users notice.
Slow responses, or approvals waiting over two hours.
Unavailable or audit not writing. Escalated within 15 minutes.
A scored test conversation runs against each agent every six hours, so a drop in answer quality pages us like an outage does.
Strong doesn't need an enterprise governance suite. It needs a short, real policy pack that holds up when a retailer, an insurer or a European distributor asks.
| Framework | What it means for Strong | What we deliver |
|---|---|---|
| NIST AI Risk Management Framework | The free US standard: govern, map, measure, manage. | Use-case register, risk tier per agent, owner per agent |
| ISO/IEC 42001 | The certifiable AI management system. Align now, certify only if a big retailer asks. | Controls mapped to its clauses, so certification is a step, not a project |
| EU AI Act | Strong sells in Europe. Customer-facing assistants must say they are AI, and staff using AI need basic AI literacy. | Disclosure on customer-facing agents; a one-hour AI literacy session with a record of who attended |
| Shadow AI | Staff already paste specs, prices and customer emails into personal AI accounts. | Inventory of AI tools in use, an acceptable-use policy, and a move to company accounts |
Everything in this Command Center already exists in a system we built and operate. We are offering Strong a pattern we've proven, not a promise.
AI agents that do money-moving back-office work: filing insurance claims, reading payer portals, matching payments to bank deposits, turning paper statements into structured records. Every action is governed and audited, under healthcare privacy rules.
Live demo available with sanitized data.
| Insurance claims prepared by AI, filed after checks | Warranty claims prepared by AI, credited after approval |
| Payer portal robots that log in and read status | Retailer portal agents that read POs and messages |
| Paper statements read into structured records, then checked by arithmetic | PDF purchase orders and supplier invoices read into the ERP, then checked |
| Payments matched to bank deposits, read-only | Orders and invoices reconciled with accounting, read-only first |
| A live risk score that tightens what agents may do | Unusual claims or orders automatically move to approval |
| Strict isolation so one site never sees another's data | Dealer, retailer and plant data kept apart by role |
We built an integration that posted payments straight into the client's accounting system. Review showed it could double-count money already recorded elsewhere. We retired it before go-live and shipped a read-only reconciliation instead. When an AI write is risky, we don't ship it, even after we've built it.
For six days a health check reported green while every real AI request was being refused. The fix was a monitor that holds a real, signed-in AI conversation around the clock. That is why the Command Center asks whether the business process works, not only whether the server is up.
For a specialty-materials manufacturer, we automated the path from shop-floor reactor data to the quality management system.
The same plant-floor-to-quality-system path is where warranty root-cause analysis at Strong starts.
Nearly every mid-market company is using AI. Very few can show what it earned. The research agrees on why.
of agentic AI projects will be canceled by the end of 2027, over cost, unclear value or weak risk controls.
Gartner, June 2025of companies are scaling AI agents anywhere. Only 39% see any profit impact from AI at all.
McKinsey State of AI, Nov 2025AI built with a specialist partner succeeded about twice as often as AI built alone, 67% against 33%.
MIT NANDA, 2025of mid-market firms now use generative AI, and lack of in-house expertise is a top barrier.
RSM Middle Market AI Survey, 2025of AI projects fail, twice the rate of other IT. The top cause is misreading the business problem.
RAND, 2024The platforms are good tools, and we use them where they fit. A tool doesn't pick the use case, connect your ERP and retailer portals, or answer for the result.
| Buy an AI platformAgent 365, Copilot Studio, ServiceNow | Hire a large consultancy | Airiam Command Center | |
|---|---|---|---|
| Picks and ranks Strong's use cases | You do | Yes | Yes, against a measured baseline |
| Connects your ERP, helpdesk and retailer portals | Connectors, your team configures | Yes, by a separate build team | Yes, the same team |
| Runs the agents after launch | You do | Usually hands over | Yes: monitoring, fixes, monthly value report |
| Human approval on money and customer actions | Possible, you design it | Designed, then handed over | Built in from day one |
| Proof it works before you commit | Trial licenses | References | A running production system you can see |
| Sized for a 200-person manufacturer | Per-user and per-action pricing | Enterprise programs | Fixed-fee stages, cancel on notice |
If Strong runs Microsoft 365, the Command Center works alongside it. Agents can be built in Copilot Studio where that fits, and governed through the same lanes and audit trail.
Three stages, each with its own fixed fee and its own decision. Strong can stop after any of them and keep what was delivered.
We'll show the live platform with sanitized data at the first session, then walk the warranty queue with your team.
Sample data throughout the Command Center views is illustrative and Strong-specific in shape only; discovery replaces it with validated baselines. External statistics are cited at source. Production-platform figures come from Airiam's own engineering records. © Airiam