AI Implementation
AI that works starts in the back office, not on the shop floor.
Between 70% and 85% of AI projects fail to deliver on what they promised, and only 6% of businesses reach AI performance that translates to 5%+ EBIT impact (McKinsey State of AI 2025). The organisations that pull ahead are not running bigger programmes. Most of them started in the back office. That is where AI Fluent starts.
30 minutes. No prep needed. We figure out fit together.
Florin Cusmereanu · 12 years Tier-1 automotive · €70M die-cast portfolio · 3 years IT and AI deployment · every engagement delivered personally
Back-office first. Low risk. Measurable from day one.
BCG puts AI implementation success at 70% people and workflow changes, 20% technology, and 10% algorithms (industry benchmark). This is not surprising. It is a documented failure pattern: the technology gets deployed, the workflows do not change, and the team never genuinely adopts it. The solution exists. Nobody uses it.
The back office changes the risk profile. We begin with manual processes that consume significant time, produce no direct operational value in themselves, and sit well outside production, quality-critical systems, and customer-facing workflows. The cost of a mistake in this zone is low. The time saving shows up within weeks. The business case is straightforward to build.
In practice, that means processes like supplier performance reporting, shift handover documentation, email classification, and quality incident summaries. Things your team currently handles through copy-paste, spreadsheet formulas, and email threads. Give those same inputs to an automated system and the output comes back faster and more consistently.
Nothing in the first phase touches the production floor, the ERP core, or any quality-critical system. The automation runs alongside existing systems. IT involvement in the first phase is minimal. The scope stays narrow enough to deliver a measurable result within 12 weeks. If the first solution underperforms, the cost is contained.
Gartner projects that 40% of agentic AI initiatives will be abandoned by 2027 (industry benchmark). The ones that survive start somewhere they can prove value quickly. That is the only starting point we use.
8 solutions we build most frequently
These are the automations most commonly identified during scoping conversations with manufacturing, industrial, and SMB operations. Deliverable solutions at the scope described, not hypothetical use cases.
- Supplier scorecard generation
- OTD, PPM, and quality trend data pulled and compiled into consistent narrative reports with per-supplier commentary. Manual data aggregation eliminated.
- Shift handover reports
- Structured shift summaries generated from log inputs. Consistent format, fewer gaps in the handoff, less time spent on documentation at shift end.
- Email triage and routing
- Shared inboxes classified, tagged, and routed automatically. Supplier communications, customer service queues, and internal escalations sorted without a human reading every message first.
- RFQ processing
- Incoming RFQ documents parsed, key data extracted, summarized for the purchasing team. Outgoing RFQs structured from technical inputs.
- Quality incident summarization
- Quality alerts classified, summarized, and assigned. Where applicable, an 8D draft is initiated automatically so the response process starts without a manual handoff.
- Invoice extraction
- Structured data pulled from PDF and email invoices. Manual entry reduced or eliminated.
- AI knowledge assistant
- A custom assistant trained on internal documentation: SOPs, product specifications, supplier records, HR policies, safety guidelines. Answers employee questions accurately and consistently without human escalation.
- Meeting-to-action-items
- Transcribed or recorded meetings turned into extracted decisions, owners, and deadlines, then distributed without a manual step.
Supplier performance reporting at a die-cast operation
The situation
A purchasing team at a European die-cast Tier-2 supplier runs monthly supplier performance reviews. OTD, PPM, and incoming quality alert data live across 3 separate systems: the ERP, the quality management platform, and a shared folder of supplier communication records.
Each reporting cycle, 2 analysts spend 6–8 hours pulling data and cross-referencing it across those sources, calculating scores, writing individual per-supplier narrative commentary, and formatting the document for management review. The analysis quality varies depending on who writes it. Critical performance deviations sometimes go unnoticed for several days because the process only runs once a month.
What was built
The automation runs on scheduled data exports from each system. No live ERP integration is required in the first phase. It applies the team's existing scoring criteria, calculates a consistent rating for each supplier, and writes narrative commentary from the data. The purchasing manager reviews the formatted report and approves it before distribution.
A mid-period alert rule was added in the second week: when a supplier breaches an OTD or PPM threshold between reporting cycles, the system sends an alert to the relevant purchasing contact within the same business day.
What changed
The team recovered 6–8 hours per reporting cycle. Analysis quality became consistent across all suppliers regardless of who ran it. At-risk supplier alerts now surface within hours of a threshold breach, not days later when the monthly report runs. The purchasing team redirected the recovered time to supplier development conversations. That is the work automation cannot do.
The solution ran on structured exports for the first 6 months before any integration into live systems was discussed. IT was not involved in Phase 1.
How an engagement works.
Discovery call
A 30-minute conversation. You describe the operational problem; we determine whether and how AI can address it. No commitment, no pitch deck.
Strategic workshop
A half-day session, typically 4 hours. We map the workflows, define the scope, and agree on the benchmark we'll measure against.
Scoped proposal
A written proposal with defined deliverables, a realistic timeline of about 12 weeks from kickoff, and a conservative ROI projection in two tiers: direct savings and additional upside.
Delivery
Florin works directly on the implementation. No handoff to a junior team. The solution is documented, handed over, and yours when we finish.
What to expect, honestly
Two categories. Never merged into a single figure.
Conservative estimate · ~70% confidence · what we commit to
These are the savings we calculate before the engagement begins, grounded in your actual headcount, time costs, and error rates. They are the number we stand behind.
Typical inputs: hours per week spent on the process × loaded cost per hour × team size. Presented per annum.
Labelled upside · not guaranteed
These are the downstream effects that are real but harder to attribute directly: reduced error rate in dependent processes, faster cycle time where the automation feeds into production decisions, freed capacity that leadership can direct elsewhere.
We document these separately. They are not added to the headline ROI figure.
Direct Savings and Additional Impact are never merged into a single number. If you see a combined figure, it is wrong.
Estimate your ROI before we speak
ORBIT (Output, Return & Business Impact Tool) is a free calculator built for operations teams evaluating back-office automation. Enter the process, the team size, and the time cost. The tool outputs a conservative two-tier estimate you can use in an internal business case.
The ORBIT estimate is a starting point, not a proposal. The scoped ROI projection in a written proposal uses your actual data from the strategic workshop.
The questions worth asking before you commit
“We've tried AI before and it didn't work.”
Most AI implementations fail. The failure rate sits between 70 and 85 percent across industries (McKinsey State of AI 2025). They fail because the technology gets deployed but the workflows and the people don't change. We start where that failure risk is lowest: back-office processes, not production. The scope is narrow enough to prove it works before anything bigger is on the table.
“This will need IT approval and that process takes months.”
Most implementations at this scope do not require IT involvement in the first phase. The starting point is structured data exports from systems you already use. The automation processes those exports. Live systems stay untouched, and nothing in Phase 1 requires access to infrastructure or live databases. When IT does need to be involved, it is because the scope has grown to a level that makes it necessary. We include that in the scoping conversation.
“The budget timing is not right this quarter.”
A 12-week delivery means the implementation window can be planned well in advance. The scoping work, including the written ROI projection, happens before the budget decision, not after. Most clients find it easier to bring a documented business case to a planning conversation than to start that conversation empty-handed. If there is a specific quarter in mind, tell us and we plan around it.
Ready to find the first process worth automating?
According to Microsoft Work Trend Index 2026, 9 in 10 manufacturing leaders expect AI agents to function as additional operational capacity within the next 12–18 months. The organisations currently ahead started narrow. They proved the return before expanding.
The discovery call is 30 minutes. You describe the process. We tell you whether it is a good candidate for automation and what the realistic return looks like.
Florin Cusmereanu
Founder, AI Fluent
Florin Cușmereanu spent 12 years in Tier-1 automotive operations managing a EUR 70M annual die-cast portfolio across Europe, running supplier relationships and carrying quality accountability through OEM audit cycles across three continents. He moved to AI because the problems he had lived were exactly the ones most consultants couldn't diagnose from the outside. Every AI Fluent engagement is designed and delivered by Florin personally.
- experience
- 12 years Tier-1 automotive
- portfolio
- EUR 70M annual die-cast
- delivery
- founder-led, every engagement
Ready to start?
30 minutes. No prep needed. We figure out fit together.