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73% of artificial intelligence projects in companies fail before reaching production....
73% of artificial intelligence projects in companies fail before reaching production.
Según McKinsey (2025), el 78% de las organizaciones ya utilizan IA en al menos una función de negocio, frente al 55% del año anterior. — Fuente: McKinsey, The State of AI, 2025
Not for lack of technology. The technology works.
They fail for three brutally simple reasons: incompetent implementation, incorrect expectations, and consultants selling vaporware instead of solutions.
Every week, dozens of companies sign €30,000, €50,000, or €100,000 contracts with “AI experts” who promise to revolutionize their business. Six months later: they have spent the budget, have a prototype that doesn’t work in production, and zero measurable ROI. The problem wasn’t the AI. It was hiring someone who didn’t know how to implement it.
The numbers don’t lie: according to McKinsey analysis of 400+ corporate AI projects, only 27% reach production and generate measurable value. The rest die in pilot phase or are deactivated after 6-12 months with no real adoption. Gartner reports that enterprises will spend 65 billion euros on AI in 2023, but only 18 billion will generate positive return – 47 billion burned in failed projects.
The paradox: AI works extraordinarily well when implemented correctly. Companies that get it right report: 40-80% automation of repetitive tasks, 30-60% reduction in operating costs, 15-35% increase in sales conversion, and 3-8x ROI in 18-24 months. The difference between the 27% that succeed and the 73% that fail is not budget or industry. It’s knowing what to look for in an artificial intelligence company and how to avoid the obvious pitfalls.
This guide cuts through the noise. No buzzwords. No corporatism. No promises of AGI that “thinks like a human”. Just what works in real production, what it really costs (spoiler: between €5,000 and €150,000 depending on complexity), what results to expect with realistic timeline, and the 5 red flags that scream “run away from this consultancy”.
We’ll document 6 real cases with raw numbers: exact investment, timeline implementation, measured results, and what went wrong and what went right. We will reveal the 8 questions you MUST ask before signing a contract (if the vendor doesn’t clearly answer all 8, don’t contract). And we will provide the evaluation framework that serious companies use to decide if they are ready for AI or if they need to fix their basic processes first.
By the end you’ll know: whether your company needs AI today or is getting ahead of itself, what to look for in a serious vendor versus charlatan, how much to pay (and when you’re getting ripped off), and which applications generate real ROI versus expensive experiments that never scale.
We are not looking to please. We are not looking for you to burn €50,000 on an AI project that will die in the pilot phase.
The Brutal Reality: Why 73% of AI Projects Fail
Three reasons. Always the same.
Reason 1: Selling Science Fiction, Implementing Mediocrity
What they promise: “Our AI learns itself, adapts automatically, will revolutionize your business.”
What they deliver: Basic wrapper over misconfigured OpenAI API that responds generic without context of your business.
Reality: Current AI is NOT AGI. It does not “think. It is sophisticated statistical automation. It works remarkably well BUT it needs: clean data, defined process, specific training your use case, correct integration of existing systems.
Mediocre consulting firms sell fantasy because it’s easier than implementing well. Client signs contract excited about “AI revolution”. Six months later has chatbot that responds poorly 40% of the time and no one uses it.
Quick quack test: If in first meeting they talk for 30 minutes about “the transformative potential of AI” without asking ONE question about YOUR specific business, run away.
Reason 2: Your Company Is Not Ready (And No One Tells You So)
The 60% of companies looking to implement AI have a more basic problem: undocumented chaotic processes, data scattered across 40 spreadsheets, legacy systems with no APIs.
Inconvenient truth: AI doesn’t fix clutter. It automates it. If current process is chaos, AI will automate chaos faster.
Minimum viable IA requirements:
- Documented process (you can explain step by step what it does today)
- Structured data >1,000 historical records
- Basic digital systems up and running (CRM, ERP, not Excel everywhere)
- Calculable ROI: if you automate, you save X€ or generate Y€.
Serious companies tell you in discovery, “You’re not ready for AI. Fix this first. They save you €30,000 on a project that would have failed.
Consultants in need of billing: “Of course you’re ready, let’s get started now” (charge 6 months, deliver nothing functional, blame “your data” when it fails).
Reason 3: Expectations Misaligned With Reality
Customer expectation: “AI will be up and running in 4 weeks and will automate everything immediately”.
Reality: Serious implementation takes 3-6 months. First 2-3 months development/testing. Month 4-6 deployment and adoption. Full ROI seen month 8-12.
Expectation: “AI will be perfect 100% of the time”.
Reality: Typical Accuracy well implemented: 85-92%. Sufficient to automate most cases, human review edge cases 10-15%.
Expectation: “Once implemented, it works alone forever”.
Reality: IA models require maintenance: re-training every 3-6 months when new data differs data training (model drift), continuous performance monitoring, updates when business changes.
Serious suppliers align expectations from day 1. Charlatans promise the impossible, get paid, disappear when it fails.
The Guaranteed Failure Formula
Mediocre consultant + unprepared company + wrong expectations = 50.000€ burned in 6 months with zero results.
The good news: it’s 100% avoidable if you know what to look for.
What to Look for in Serious IA Business (And Obvious Red Flags)
5 Characteristics of AI Companies That Work
1. Do Discovery Before Proposing
First 2 meetings are 80% of them asking about YOUR business: current processes, available data, specific problems, existing systems. 20% explaining general approach.
If first meeting is PowerPoint 40 slides on “the AI revolution” without a question about your situation, red flag giant.
2. Show Code and Technical Architecture
They can explain: what technical stack they use (Python + TensorFlow/PyTorch, fine-tuning specific models, RAG architecture for retrieval), which models (GPT-4, Claude, Llama, custom?), how they will integrate with your systems, where data is stored.
If answers are vague “advanced proprietary algorithms” with no technical details, they have no real expertise.
3. Portfolio Verifiable Production Cases
Minimum 3 similar projects in production (no dead prototypes). Case studies include: specific problem, detailed technical solution, measured results, client contactable for reference.
If they just show “we worked with [large company logo]” without verifiable details, they probably made PoC that never made it to production.
4. Transparent About What They Cannot Do
“For your specific case, AI can automate 60-70%, the remaining 30-40% requires human review” is serious answer.
“We can automate 100% of your process” is either a lie or incompetence.
Good companies tell you honestly if you are NOT ready or if AI is not the best solution for your specific problem.
5. Contracts with Clear Deliverables and Metrics
Specific proposal: concrete milestones with dates, deliverables each phase (not just “we will work on the system”), measurable success metrics, what happens if it doesn’t work (performance clauses), total cost breakdown.
Vague contracts “we will develop IA system to optimize operations” without specifying anything = run.
5 Red Flags That Scream “Run Away”.
🚩 They promise AGI or “AI that thinks like a human.”
It does not exist commercially. If they promise it, they are lying or incompetent.
🚩 They only talk buzzwords with no concrete cases.
“Machine learning, deep learning, neural networks” without EVER explaining what problem they solved for which customer with what result = they sell smoke.
🚩 The sales process is pitch, not discovery.
They sell you without understanding your business first = guaranteed poorly adapted generic solution.
🚩 Opaque pricing without breakdown
“From 10.000€ depends…” without clear criteria = never 10K, always 30K+ with surprises.
🚩 Team with no verifiable experience
They do not show who will work on your project, or CVs are generic with no specific IA production projects.
For serious implementation that works, look for companies with demonstrable experience in automation with artificial intelligence applied to real business cases.
AI Applications With Real ROI (No Expensive Experiments)
Forget humanoid robots. These applications generate measurable return today:
Automated Customer Service
What it does: Chatbots trained with YOUR FAQs, documentation, ticket history. They respond 24/7, escalate to human only when necessary.
Typical ROI:
- 65% tickets resolved without human
- Response time from 4h to 15min
- Cost/ticket -58%.
- Payback 8-14 months
Investment: €8,000-25,000 implementation
Who it works for: Volume >500 queries/month, clear documented FAQs, saturated support team.
The key is to deploy AI agents trained specifically with your data, not generic chatbots that respond poorly.
2. Marketing & Sales Automation
What it does: Predictive lead scoring (which leads convert), automatic content personalization, optimization of sending timing, generation of content drafts.
Typical ROI:
- 22% increase in conversion
- CAC 18% reduction
- Team time savings 35%.
- Payback 10-16 months
Investment: 10,000-40,000
For whom: Lead volume >200/month, documented sales process, CRM with historical data.
3. Intelligent Document Processing
What it does: OCR + NLP extracts data from invoices, contracts, CVs, forms. Automates data entry, classifies documents, routes approvals.
Typical ROI:
- Processing time -70% -70
- Error rate of 12% to 2%.
- Savings 15h/week
- Payback 6-10 months (faster ROI)
Investment: €8,000-30,000
For whom: You process >500 documents/month manually, relatively standard format.
Automating operational processes with AI works best in repetitive processes with high volume.
4. Business Predictive Analytics
Role: Models predict demand, churn, cross-sell opportunities, operational anomalies based on historical data.
Typical ROI:
- Forecasting accuracy +40%.
- 35% reduction in stockouts
- Churn prevention 25% Churn prevention 25% Churn prevention 25% Churn prevention 25% Churn prevention 25% Churn prevention 25% Churn prevention
- Direct Impact P&L
Investment: 15,000-60,000
For whom: Historical data >2 years, significant transaction volume, decisions based on forecast.
What TODAY Is Experimental (Not ROI Of Course)
- Fully autonomous creative generation
- AI making strategic decisions without humans
- Physical robots in retail/hospitality
- AGI replacing workers completely
Rule: If you can’t calculate specific savings BEFORE you implement, you have no clear ROI. Don’t do AI for AI’s sake.
Real Budgets: How Much It Costs and What to Expect
No makeup. No “it depends”. Real numbers for the Spanish market 2024:
Tier 1: Pilot Project / PoC (5.000-15.000€)
Includes: feasibility analysis, limited functional prototype, validation with your data
Timeline: 4-8 weeks
Result: You know whether it is technically feasible, you decide whether to scale up
Brutal reality: 40% PoCs show that you do NOT need AI. That’s success – saves you 50K€.
Tier 2: Production Implementation (15.000-50.000€)
Includes: Full development, systems integration, training team, support 3-6 months
Timeline: 3-6 months
Result: System running production, automation 40-60% process
ROI: Positive month 8-14 typically
Tier 3: Enterprise Transformation (50,000-150,000€+)
Inclusions: Multiple AI systems, complete data infrastructure, change management
Timeline: 6-12 months
Outcome: Complete operational transformation, 60-80% automation
For whom: Enterprise, multiple use cases, C-level engagement
Breakdown Typical Project 25,000€.
- Discovery & analysis: 15% (3.750€)
- Model development: 40% (€10,000)
- Integration: 25% (€6,250)
- Testing: 10% (2.500€)
- Training & docs: 10% (2.500€)
Red Flags Pricing
- <5.000€ full implementation (impossible to do well)
- ❌ No breakdown by phase (opacity = problem).
- ❌ “Consultant day” without deliverables (infinite billing).
- ❌ “From X€ it depends…” no criteria (it will never be X)
Ask before signing, “What specific metrics will improve and by how much?” If vague answer, they don’t know what they are selling.
6 Case Studies: Investment and ROI Without Makeup
Case 1: Ecommerce Fashion – Chatbot Customer Service
Investment: €18,500 implementation
Timeline: 4 months development + deployment
Results month 12:
- 68% tickets resolved without human
- Response time 4.2h → 12min
- Cost/ticket 7.80€ → 2.40€.
- Annual savings: €32,400
- ROI: 1.75x year 1, 4.2x cumulative 36 months
Case 2: B2B SaaS – Predictive Lead Scoring
Investment: €28,000
Timeline: 5 months
Results month 18:
- Conversion leads 14% → 19.8%.
- Commercial/deal time -35% -35
- Incremental revenue: 180K€/year
- ROI: 6.4x
Case 3: Legal Firm – Contract Processing
Investment: 22.000€.
Timeline: 3.5 months
Results month 10:
- Contract review time 2.5h → 0.4h
- Savings 18h/week junior lawyers
- Equivalent cost saved: 42K€/year
- ROI: 1.9x year 1, 5.1x at 36 months
Case 4: Retail – Demand Forecasting
Investment: €45,000
Timeline: 7 months
Results month 14:
- Accuracy forecast +38%.
- Stockouts -42%.
- Overstock -31%.
- Marginal improvement: 285K€/year
- ROI: 6.3x
Case 5: Fintech – Fraud Detection
Investment: €65,000
Timeline: 8 months
Results month 12:
- Fraud detection real-time 94% accuracy
- 73% reduction in fraud losses
- False positives from 18% to 4%.
- Direct savings: 420K€/year
- ROI: 6.5x
Case 6: Manufacturing – Predictive Maintenance (FAILURE)
Investment: €55,000
Timeline: Cancelled month 5
Why it failed:
- Insufficient data sensors (only 4 months historical, needed 24+)
- Legacy systems without APIs, technically impossible integration
- Resistance team maintenance (feared layoffs)
Lesson: Inadequate discovery. Serious company would have identified problems week 1 discovery, not month 5.
Pattern cases success: Clear process, sufficient data, C-level sponsor, realistic expectations. Failure: the opposite.
El mercado global de inteligencia artificial alcanzó los 390.910 millones de dólares en 2025, con una proyección de crecimiento a 3,5 billones para 2033 (Grand View Research, 2025). — Fuente: Grand View Research, 2025
AI Implementation Working with CRONUTS.DIGITAL
We do not sell smoke. We implement systems that work in production.
Our Approach
Discovery Brutal Honest (Week 1-2):
We analyze if you’re really ready. If you’re not, we tell you what to fix first. We’d rather lose a project than charge you for something that will fail.
Proof of Concept Functional (4-6 weeks):
Prototype working with YOUR real data. No demos. No slides. Working system proving technical feasibility.
Implementation Production (3-6 months):
Complete development, integration with your systems, training team, controlled deployment, post-launch support.
Areas Where We Generate Real ROI
- Marketing Automation: Lead scoring, personalization, content generation with AI applied to marketing
- Operational processes: Documents, workflows, data analysis with operational automation
- Conversational agents: Chatbots, virtual assistants with custom trained agents.
- IA Strategy: Roadmap, feasibility, vendor selection with strategic consulting
Transparent Pricing
PoC/Pilot: 5.000-12.000€ (4-6 weeks)
Implementation: €15,000-45,000 (3-6 months) depending on complexity
Enterprise: Custom (50K-150K€) complete transformation
Always included: Code ownership (you own it), full documentation, technical training team, 3-6 months post-launch support.
What we do NOT do
- ❌ Selling AI to those who don’t need it.
- ❌ Promise AGI or “thinking AI”.
- ❌ Projects without clear success metrics.
- ❌ Vendor lock-in owner
Simple Guarantee
If after PoC we conclude that IA is not the best solution for your problem, we propose a better alternative. We prefer your success to billing a project that will fail.
Preguntas frecuentes
Lo que CMOs y directores nos preguntan.
8 dudas concretas con respuesta accionable en ≤ 80 palabras · formato óptimo para AI Overviews.
What exactly does an artificial intelligence company do and when do you really need it?
What exactly does an artificial intelligence company do and when do you really need it?
- Clear and repetitive processes consuming 20+ hours/week of your equipment
- Sufficient data volume (minimum 1,000 historical records to train models)
- Clear ROI: if you automate, you save X€ or generate additional Y€.
- Realistic budget: €5,000 minimum pilot projects, €15,000-50,000 serious implementations
- Your processes are undocumented chaos - AI doesn't fix clutter, it automates it.
- You are looking for "innovation" with no measurable business objective - that's technology tourism.
- You expect AI to solve strategic problems that are your responsibility
- Budget <5.000€ or expect results in 2 weeks
- Automated customer support: Chatbots trained with your real FAQs, answer 60-80% of queries without human. Clear ROI: reduce support costs 40-60%.
- Predictive sales analysis: Models identify which leads convert, prioritize sales reps. Typical conversion increase 15-30%.
- Marketing automation: Automatic segmentation, personalized content, optimal email timing. Lift 20-40% engagement.
- Document processing: OCR + NLP extract data from invoices, contracts, CVs. Saving 10-20h/week manual tasks.
How much does it really cost to hire an AI company and what results can I expect?
How much does it really cost to hire an AI company and what results can I expect?
- What it includes: Technical feasibility analysis, limited functional prototype, concept validation with your actual data
- Timeline: 4-8 weeks
- Results: You know if it is technically feasible, you see demo working, you decide whether to scale up.
- For whom: First time implementing AI, want to try without compromising big budget
- Reality: 40% PoCs show that problem does NOT need AI or insufficient data. That's success, not failure - saves you €50K on entire project that would have failed.
- What's included: Full model development, integration of existing systems, training team, support 3-6 months
- Timeline: 3-6 months
- Expected results: 40-60% automation of target tasks, positive ROI month 6-12
- Examples: Complete customer service chatbot, product recommendation system, document analysis automation.
- For whom: validated PoC, business critical process, sufficient volume justifies investment
- What it includes: Multiple integrated IA systems, complete data infrastructure, change management, IA governance
- Timeline: 6-12 months
- Results: Complete department/functional operational transformation, 50-80% process automation
- For whom: Digitally mature enterprise, multiple use cases, C-level engagement
- Discovery & data analysis: 15% (3.750€)
- AI model development: 40% (€10,000)
- Systems integration: 25% (6.250€)
- Testing & QA: 10% (2.500€)
- Training & documentation: 10% (2.500€)
- Month 1-3: Investment without return (development, testing)
- Month 4-6: Deployment, first savings (20-40% potential)
- Month 7-12: Optimization, full benefits (60-80% potential)
- Year 2+: ROI compounding, maintenance <20% annual upfront cost
- Customer service: 65% of tickets resolved without human intervention, response time from 4h to 15min, cost/ticket -58%.
- Lead qualification: Increased conversion 28%, reduced commercial time 40%, closed deals +22%.
- Document processing: 18h/week saved, error rate from 12% to 2%, processing cost -71%.
- ❌ Promise full implementation <5.000€ (impossible to get it right).
- ❌ No breakdown of costs by phase (total opacity).
- ❌ They charge per "consultant day" without concrete deliverables (infinite hourly billing).
- Pricing "from X€ it depends..." without clear criteria (never X, always 3X)
- ❌ Do not include maintenance on proposal (post-launch surprises).
How do I identify if an AI company is serious or is selling me smoke?
How do I identify if an AI company is serious or is selling me smoke?
- "Show me 3 similar AI projects that you implemented in production." If they can't, they have no real experience. Case studies should include: client (albeit anonymized), specific problem, technical solution, measured results.
- "Who on your team will work on my project and what is their background?" You need to see CVs/LinkedIn. Looking for: real AI implementation experience (not just research), industry knowledge of your industry (fintech, ecommerce, etc), verifiable technical skills.
- "What data of mine do you need and what if I don't have enough?" Honest answer: "We need at least 1,000 clean records, if you don't have them, we start collecting data 3-6 months". Bad answer: "Don't worry, we work with any data".
- "What is the success rate of your IA projects reaching production?" If they say 100%, they are lying. Realistic rate good companies: 60-75% PoCs are converted to production. 25-40% are cancelled due to technical/economic feasibility (that's GOOD, they saved you money).
- "Give me the technical breakdown: what models, what architecture, what infrastructure?" They must be able to explain in technical terms (even if they then translate it into business language). If they only speak business without technical substance, they have no real ability.
- "What specific metrics will improve and by how much?" Don't accept "we will improve efficiency". Demand: "We will reduce processing time 40-60%, errors from 15% to 3%, cost per unit -50%". Concrete numbers or nothing.
- "What if after PoC they conclude that AI is not the solution?" Serious answer: "We will tell you honestly and propose better alternatives". Bad answer: awkward silence or insisting that AI is always the answer.
- "Do they include knowledge transfer and training or do they leave me dependent?" Vendor lock-in is real. Good companies: complete documentation, training your technical team, accessible code. Bad: proprietary black box, total dependency.
- Ask for contactable references (not only web testimonials)
- Search LinkedIn for real equipment working there
- Check GitHub for open source contributions (indicates technical expertise).
- Google "[company name] + scam/reviews" (obvious but many do not do it)
What enterprise AI applications generate real ROI today (not futuristic experiments)?
What enterprise AI applications generate real ROI today (not futuristic experiments)?
- Ecommerce: Order tracking, return policies, product availability, etc.
- SaaS: Troubleshooting technical level 1, onboarding users, billing
- Services: Reservations, schedules/price information, general FAQs
- 60-80% queries resolved without human
- Average response time from 4h to 15min
- Cost per ticket resolved -50 to -70%.
- Customer satisfaction maintains 85%+ if well implemented
- Predictive lead scoring: Model predicts which leads convert based on historical behavior. Commercials prioritize better, increase conversion 15-30%.
- Content personalization: Emails, product recommendations, dynamic landing pages adapted to each user. Lift engagement 20-40%.
- Optimized timing: AI determines best time to send email to each contact. Open rates +25-35% vs. single mass mailing.
- Content generation (with supervision): Drafts emails, social posts, product descriptions. Save 10-15h/week but ALWAYS check human.
- Increased marketing conversion 20-35%.
- 15-25% reduction in acquisition cost
- Marketing team time savings 30-50%.
- Forecasting demand: Retail/ecommerce optimizes inventory, reduces stockouts -40%, overstock -30%.
- Churn prediction: SaaS identifies customers at risk cancel, proactive intervention reduces churn 20-35%.
- Dynamic Pricing: Adjusts prices according to demand, competition, inventory. Revenue +8-15%.
- Fraud detection: Fintech/ecommerce identifies suspicious transactions real-time, reduces fraud 60-80%.
- Invoice processing: Automatic data extraction, matching POs, validation, routing approvals. Saving 12-20h/week accounting teams.
- Screening CVs: Automatic parsing, matching requirements, ranking candidates. HR processes 5-10x more CVs at the same time.
- Contracts & legal: Extraction of key clauses, identification of risks, comparison of terms. Lawyers review in half the time.
- 60-80% reduction in processing time
- Error rate from 10-15% to 1-3%.
- Cost per document processed -70% -70
- Logistics: Optimization of delivery routes, reduces km traveled 15-25%, fuel costs -20%.
- Manufacturing: Predictive maintenance of machinery, reduces downtime 30-40%, maintenance costs -25%.
- Workforce planning: Optimize shifts/schedules based on predicted demand, reduce overstaffing/understaffing
- Fully autonomous creative generation (images, video marketing)
- AI "making strategic decisions" without a human loop
- Humanoid robots in retail/hospitality
- AGI doing "all the work".
Is my company really ready to implement AI or am I getting ahead of myself?
Is my company really ready to implement AI or am I getting ahead of myself?
- Process consumes >20h/week equipment in repetitive manual tasks
- Historical data >1,000 records clean, accessible
- Calculable ROI: if you automate X, you save Y€ or generate additional Z€.
- Executive sponsor committed + budget approved
- Realistic timeline: 6-12 months see full ROI
- Relatively stable process (does not change every week)
- You look for AI because "competitors do it" or "it sounds cool".
- Processes are undocumented chaos
- Data in disconnected spreadsheets without structure
- Expectation: AI will solve strategic problems that are your responsibility
- Budget <5K€ or "let's see if it works and then invest more".
- Equipment actively resisting automation
- Consolidate data in centralized systems (CRM, ERP)
- Document current processes (flowcharts, SOPs)
- Automate with simple non-IA tools (Zapier, Make, scripts)
- Measure everything (set baseline metrics)
- Implement standard tools well (CRM used 100%, analytics configured).
- Accumulate sufficient data (historical need to train models)
- Educate team on AI (realistic expectations)
- Identify ideal AI pilot process (high volume, repetitive, available data)
How do I measure the success of an AI project and which KPIs are really important?
How do I measure the success of an AI project and which KPIs are really important?
- Hours saved/week: Process used to take 20h/week manually, now 4h → 16h saved.
- Cost per transaction: Processing invoice cost 8€ manual, now 1.2€ → -85%.
- FTE equivalent savings: Automation equals X people full-time → redeploy to higher value work.
- Increased conversion: Lead scoring AI increased sales conversion from 12% to 18% → +50% conversion
- Revenue per customer: IA recommendations increased AOV from €85 to €110 → +29%.
- Churn reduction: Churn prediction + intervention reduced monthly churn from 6% to 4% → 33% less losses
- Overall Accuracy: % correct predictions. Target >85% majority of cases, >95% critical (healthcare, finance)
- Accuracy: Of positive predictions, how many correct. Important when false positive expensive.
- Recall: Of real positive cases, how many are detected. Important when false negative critical (fraud, churn).
- % cases solved without human: Chatbot solves 68% tickets without escalation → 32% need human
- Straight-through processing rate: 75% invoices processed fully automatically, 25% require intervention
- Response time: Chatbot responds <3 seconds, AI search <500ms
- Throughput: System processes 1,000 documents/hour vs. 50 manually
- % team using system: Out of 20 sales reps, 18 use lead scoring IA → 90% adoption
- Frequency of use: Tool used daily vs sitting idle
- Resistance/Complaints: Tracking negative feedback, reported issues
- Internal NPS: Equipment using AI, how likely would you recommend it?
- Customer satisfaction: Yes AI customer-facing (chatbot), track CSAT. Target: maintain >85% vs. human service.
- Cost/ticket: 6.20€ → 2.10€ (-66%)
- Monthly savings: €4,800
- Cumulative ROI: Month 6 = 0.9x, Month 12 = 2.1x
- Automatic resolution: 72%.
- Accuracy of responses: 89%.
- Average response time: 8 seconds
- CSAT post-interaction: 4.2/5
- % users prefer chatbot vs. waiting for human: 68%.
- ❌ Measuring only technical accuracy ignoring business impact
- ❌ Do not set baseline pre-IA (do not know if you improved).
- ❌ Compare with perfection (100%) vs with previous process (human with errors too).
- ❌ Do not track user adoption (perfect system but nobody uses)
- ❌ Celebrate "automation rate 90%" when total cost went up (you automated but more expensive).
- Journal: Uptime, critical errors
- Weekly: Technical performance (accuracy, latency), adoption
- Monthly: Business metrics (savings, ROI), user satisfaction
- Quarterly: Strategic review, decision to re-invest/ scale up/pause
- Accuracy dropping >10% month-over-month (severe model drift)
- Adoption <50% after 6 months (usability or value prop problem)
- Projected ROI at month 18 still <1.5x (not justified to continue)
- User complaints increasing (quality deteriorating)
