How to Evaluate an AI Agency Beyond ChatGPT Kako oceniti AI agencijo dlje od ChatGPTja

The short answer: if you want to know how to evaluate an AI agency, do not stop at whether they can write good ChatGPT prompts. That is now a baseline skill, not a strategic advantage. The agencies worth hiring can connect AI to real business workflows, handle data responsibly, integrate with your systems, measure ROI, and deliver solutions that keep working after the demo. For SMBs especially, the right AI partner should be able to translate AI into practical outcomes: less manual work, faster decisions, better customer experience, and clear operational gains.

That matters because AI adoption is moving from experimentation to execution. According to McKinsey, 72% of organizations reported using AI in at least one business function in 2024, up from 55% the year before McKinsey, “The state of AI in early 2024”. Meanwhile, IBM found that 42% of enterprise-scale companies had actively deployed AI in their business, with another 40% exploring or experimenting with it IBM Global AI Adoption Index 2023. In other words, the market has matured. Your evaluation criteria should mature with it.

If you are comparing providers, think less about flashy prompting and more about whether the agency can identify high-value use cases, build reliable systems around AI models, and support adoption inside your company. That is the difference between a vendor that sells excitement and a partner that creates results.

Why ChatGPT prompting is no longer a real differentiator

Not long ago, simply knowing how to prompt large language models looked impressive. Today, it is expected. Public AI tools are widely accessible, prompt patterns are easy to learn, and many businesses already have in-house team members who can produce decent results with ChatGPT, Claude, or Gemini.

So if an agency positions itself mainly as “we know how to prompt ChatGPT,” that should raise a question: what else do they bring?

Prompting still matters, but only as one small part of a bigger delivery capability. In real business environments, AI projects typically require:

• Use case discovery and prioritization
• Workflow mapping
• Data preparation and access controls
• Model selection and testing
• System integration with CRMs, ERPs, document repositories, or support tools
• Human oversight and exception handling
• Compliance, security, and governance
• Monitoring outputs and improving performance over time

This is where many “AI agencies” fall short. They can generate polished prototypes, but they struggle to move beyond prompt demos into production-ready business systems.

“Generative AI has the potential to automate work activities that absorb 60 to 70 percent of employees’ time today.”

McKinsey, “The economic potential of generative AI”

The implication is important: the real value is not in asking a chatbot one clever question. The value is in redesigning work so AI can reliably handle parts of it. That takes process thinking, technical architecture, and operational discipline.

For example, an agency helping a retail business automate product content may need to combine prompt design with catalog data cleaning, approval workflows, brand tone rules, and integration into e-commerce systems. An agency helping a tax or compliance team may need to create a structured retrieval process over internal documentation, with auditable outputs and strict permissions. If you look at practical AI products such as FURS or AI-driven commerce solutions like Shelfze, the value comes from solving a workflow problem end to end, not from prompting alone.

What genuine AI expertise looks like in business projects

Genuine AI expertise is visible in how an agency approaches business reality. The strongest teams do not begin with “Which model should we use?” They begin with “What process are we improving, what data supports it, what risk do we need to control, and how will we prove impact?”

In practice, real expertise usually shows up in five ways.

1. They start with business outcomes

A serious AI partner asks about cycle time, error rates, conversion, service response times, staff workload, margin leakage, or document throughput. They know AI is a means, not the goal. If an agency cannot define success in business terms, it may not be ready for business deployment.

2. They understand workflow design

Most valuable AI solutions are not standalone bots. They sit inside operations. A good agency maps where the AI should act, where humans should review, and what should happen when confidence is low or the model fails.

3. They can work with your actual systems

Useful AI rarely lives in isolation. It needs to connect to knowledge bases, CRMs, internal documents, forms, product databases, finance tools, or support channels. Integration skill is often a better predictor of project success than model knowledge alone.

4. They treat data, privacy, and governance seriously

According to Cisco, 95% of organizations said customers would not buy from them if data was not properly protected Cisco 2024 Data Privacy Benchmark Study. Any AI agency you hire should be able to explain where your data goes, who can access it, how outputs are logged, and how risks are mitigated.

5. They measure and improve

AI outputs drift, business needs change, and edge cases appear. Strong agencies define quality metrics, collect feedback, and iterate after launch. They do not disappear once the first version goes live.

“AI is one of the most profound things we’re working on as humanity. It’s more profound than fire or electricity.”

Sundar Pichai, CBS interview

That quote is ambitious, but for businesses it carries a practical message: AI is not a novelty purchase. It is an operational capability. You should evaluate an AI agency the same way you would evaluate a strategic systems partner.

At M-AI, this is the lens that matters most: not whether AI looks impressive in isolation, but whether it improves real-world execution.

7 criteria to evaluate an AI agency before you sign

Here are seven concrete criteria to use when comparing vendors.

1. They can identify high-value use cases, not just generic ideas

A quality agency should be able to assess your business and quickly separate interesting experiments from high-return opportunities. Ask them what use cases they would prioritize in your company, why, and what expected value each could create.

Strong answers are specific. Weak answers are broad and trendy.

Look for agencies that can say things like:

• “Your support team repeats the same answers across channels; an AI knowledge assistant could reduce handling time.”
• “Your sales team loses time writing proposals manually; AI can draft first versions using CRM data.”
• “Your operations team reviews too many supplier documents by hand; AI can extract and route key information.”

2. They have a delivery methodology beyond prompting

Ask how they run projects. Do they have a clear process for discovery, prototyping, validation, integration, rollout, and optimization? Can they explain how they test outputs before launch?

If their methodology sounds like “we build a chatbot and see,” that is not enough. Real AI delivery should include use case scoring, pilot KPIs, risk review, stakeholder alignment, and training.

3. They can integrate AI into your existing tools and workflows

This is one of the biggest practical filters. An AI agency may show beautiful examples, but if they cannot connect the solution to your internal environment, adoption will stall.

Ask what platforms they have worked with, how they handle APIs, document repositories, website integration, internal search, ERP or CRM links, and process automation. For SMBs, this often matters more than custom model development.

4. They can explain model choice in plain business language

You do not need an agency that worships a specific model. You need one that can justify trade-offs around cost, accuracy, latency, multilingual capability, privacy, and maintainability.

A trustworthy partner can explain why one approach is better for customer support, another for internal knowledge retrieval, and another for document automation. They should also be honest about where AI is likely to hallucinate or require human review.

5. They address data security, compliance, and governance up front

This should never be an afterthought. Ask direct questions:

• Will our data be used to train third-party models?
• How is data stored and processed?
• What permissions and access controls exist?
• Can outputs be audited?
• How do you handle sensitive documents or regulated workflows?

If an agency gives vague answers here, do not proceed. Deloitte reported that 55% of organizations cited governance as a top barrier to scaling generative AI Deloitte, State of Generative AI in the Enterprise. The right partner helps remove that barrier instead of creating it.

6. They talk about adoption and change management, not just technology

Many AI projects fail because teams do not trust the system, do not know when to use it, or do not see how it fits into daily work. A good agency plans for training, documentation, user feedback, role clarity, and phased rollout.

Ask how they help employees adopt the solution. If they only discuss features, they may underestimate the human side of implementation.

7. They define success metrics and post-launch support

Before signing, make sure success is measurable. That could mean reduced processing time, faster response rates, more qualified leads, lower support cost, or increased content output per employee.

You should also know what happens after launch. Is there monitoring? Prompt or workflow refinement? Quality review? Ongoing support? AI systems improve through iteration, not one-off delivery.

A practical checklist for SMBs choosing an AI partner

For small and mid-sized businesses, evaluating an agency can feel difficult because every provider sounds innovative. Use this practical checklist to simplify the decision.

Business fit checklist

• Can they explain your top 2-3 AI opportunities in concrete business terms?
• Do they understand your industry workflows, not just AI terminology?
• Can they estimate likely ROI, even directionally?
• Do they challenge weak use cases instead of saying yes to everything?

Technical fit checklist

• Can they integrate with your existing systems and data sources?
• Do they know when to use off-the-shelf tools versus custom solutions?
• Can they explain architecture simply and clearly?
• Do they have a plan for testing, monitoring, and improving outputs?

Risk and trust checklist

• Are data handling and privacy policies clear?
• Can they support secure deployments where needed?
• Do they define human review points for sensitive tasks?
• Are responsibilities, scope, and deliverables transparent?

Execution checklist

• Do they have a structured project plan with milestones?
• Will they train your team and support rollout?
• Do they define measurable KPIs?
• Do they offer post-launch optimization?

One useful test is this: after your first serious conversation, do you feel you understand your own AI opportunity better? The best agencies bring clarity quickly. They help you prioritize, estimate value, and identify constraints. They do not hide behind jargon.

For SMBs especially, a credible AI partner should be able to start with focused, high-impact projects rather than proposing a costly transformation roadmap on day one. In many cases, the smartest path is a narrow but valuable first implementation: internal knowledge retrieval, customer support augmentation, document processing, proposal drafting, or product content automation. Once the business sees measurable gains, expansion becomes easier and safer.

That is also why practical, domain-specific solutions matter. Whether it is a specialized compliance workflow through FURS, AI-enhanced digital operations through M-AI, or smarter retail and shelf execution through Shelfze, the strongest AI work is grounded in a real operational problem.

Final takeaway

If you are deciding how to evaluate an AI agency, use one core principle: judge them by business implementation capability, not by chatbot fluency. Prompting is easy to showcase. Business value is harder to create, but far more important.

The right agency will help you identify worthwhile use cases, design workflows around AI, connect systems, manage risk, support adoption, and prove measurable outcomes. That is what turns AI from a trend into an asset.

Ready to assess your AI opportunities?

If you want a practical conversation about where AI can create measurable value in your business, contact M-AI. We help companies move beyond generic AI demos toward solutions that fit real processes, real teams, and real commercial goals.

Kratek odgovor: AI agencije danes ne izbirate po tem, ali zna “dobro promptati ChatGPT”, ampak po tem, ali zna iz poslovnega problema narediti merljiv sistem: z jasnim ciljem, kakovostnimi podatki, varnim uvajanjem, integracijami v obstoječe procese in dokazljivim donosom. Če agencija govori predvsem o promptih, manj pa o podatkovnih tokovih, odgovornosti, testiranju in ROI, je to opozorilni znak.

To je bistvo vprašanja how to evaluate an AI agency: ne ocenjujete le kreativnosti pri uporabi orodij, temveč zrelost partnerja za resnične poslovne projekte. V praksi to pomeni, da mora AI partner razumeti procese, skladnost, podatkovno arhitekturo, uporabniško izkušnjo in spremembe v organizaciji. Dober partner ne prodaja “čarovnije”, ampak izdela načrt, prototip, merila uspeha in realen plan uvedbe.

Za mala in srednja podjetja je to še posebej pomembno. Napačna izbira AI agencije lahko pomeni izgubo časa, težavno uvedbo, slabo sprejetost med zaposlenimi ali celo tveganja pri ravnanju s podatki. Prava izbira pa pomeni konkretne rezultate: hitrejše procese, manj ročnega dela, boljšo podporo strankam in nove digitalne produkte. Prav zato pri M-AI poudarjamo poslovno uporabnost umetne inteligence, ne le tehnične atraktivnosti.

Zakaj promptanje ChatGPT danes ni več prava konkurenčna prednost

Pred letom ali dvema je bilo dovolj impresivno že to, da je nekdo znal iz generativnega modela iztisniti boljši odgovor od povprečnega uporabnika. Danes to ni več dovolj. Orodja so dostopnejša, modeli boljši, uporabniški vmesniki pa vse bolj poenostavljeni. “Znati uporabljati ChatGPT” je podobno kot “znati uporabljati Excel”: koristno, vendar ne zadosten dokaz za strateškega partnerja.

Podjetja potrebujejo več kot generiranje besedil. Potrebujejo rešitve, ki so povezane z internimi bazami znanja, CRM sistemi, ERP-ji, dokumentnimi tokovi, e-pošto, podporo strankam, računovodskimi procesi in internimi pravili. Če agencija ne zna zgraditi mostu med modelom in vašim poslovanjem, ostane pri demonstraciji, ne pri rezultatu.

To potrjuje tudi širši trg. Organizacije hitro povečujejo naložbe v AI, vendar največjo vrednost ustvarjajo tam, kjer je AI vpet v procese, ne tam, kjer ostane na ravni eksperimenta. McKinsey poroča, da je 65 % organizacij že redno uporabljalo generativni AI v vsaj eni poslovni funkciji, kar je skoraj dvakrat več kot nekaj mesecev prej McKinsey, The state of AI in early 2024. Ko tehnologija postane široko dostopna, se diferenciacija premakne k izvedbi, domeni in integraciji.

Podobno Gartner ocenjuje, da bo do leta 2026 več kot 80 % podjetij uporabljalo generativne AI API-je ali modele oziroma uvajalo aplikacije, podprte z generativnim AI, medtem ko jih je bilo leta 2023 manj kot 5 % Gartner, 2023. To pomeni, da osnovna uporaba modelov postaja standard. Vrednost partnerja se zato meri drugje: v tem, kako dobro razume vaše poslovanje in kako varno, učinkovito ter merljivo uvede AI v prakso.

Če agencija kot glavni dokaz kompetence pokaže le zbirko promptov, lepih odgovorov ali generiranih vsebin, je to premalo. Pravo vprašanje ni: “Ali zna model pripraviti dober odgovor?” Pravo vprašanje je: “Ali zna agencija zgraditi rešitev, ki bo čez šest mesecev še vedno delovala, bila skladna, sprejeta med uporabniki in finančno smiselna?”

“There is no AI strategy without data strategy.”

Ta pogosto citirana misel Andrewa Nga povzema jedro problema: brez podatkov, procesov in ciljev tudi najboljši model ne ustvari trajne poslovne vrednosti.

Kako izgleda pravo AI znanje v poslovnih projektih

Resnično AI znanje se pokaže šele, ko se projekt premakne iz demo okolja v vsakdanji poslovni tok. Takrat postanejo pomembna vprašanja, ki jih površni ponudniki pogosto preskočijo: od kod prihajajo podatki, kdo je odgovoren za kakovost izhodov, kako sistem spremljamo, kako rešujemo napake, kdo ima dostop, kako se rešitev poveže z obstoječimi sistemi in kako uporabnike naučimo, da jo dejansko uporabljajo.

Dober AI partner zna poslovni izziv prevesti v tehnično in operativno izvedljiv projekt. To pomeni:

• Jasno opredeljen primer uporabe: ne “uporabili bomo AI”, temveč “skrajšali bomo čas odgovora podpori za 40 %” ali “avtomatizirali bomo razvrščanje dokumentov”.
• Pravilno izbiro pristopa: včasih je najboljša rešitev generativni model, drugič klasifikacija, ekstrakcija podatkov, semantično iskanje ali kombinacija več pristopov.
• Delo s podatki: čiščenje, strukturiranje, povezovanje, pravice dostopa, sledljivost in osveževanje virov.
• Integracije: AI mora delovati tam, kjer delajo ljudje, ne v ločenem “sandboxu”.
• Merjenje uspeha: brez KPI-jev projekt hitro postane občutek namesto rezultata.
• Upravljanje tveganj: halucinacije, napačne klasifikacije, varnost, zasebnost, skladnost in proces eskalacije.

V resničnih projektih je pogosto pomembnejši sistem okoli modela kot model sam. To vključuje arhitekturo rešitev, uporabniški tok, validacijo odgovorov, nadzor nad stroški in načrt za širitev. IBM v svoji raziskavi CEO outlook navaja, da 64 % izvršnih direktorjev čuti pritisk za hitrejše uvajanje generativnega AI kot konkurenca, vendar jih hkrati skrbi, ali njihova organizacija razpolaga s pravimi osnovami za uspešno uvedbo IBM Institute for Business Value, CEO decision-making in the age of AI, 2024. Prav tu se pokaže razlika med “AI navdušencem” in zrelim partnerjem.

Če na primer podjetje želi avtomatizirati davčne ali administrativne procese, ni dovolj, da model “razume dokument”. Potrebna je zanesljiva ekstrakcija podatkov, preverjanje pravil, povezava z internimi postopki in uporabniški vmesnik, ki skrajša delo zaposlenim. Takšne logike so bližje produktnemu razmišljanju kot navdušenju nad novostmi. Dober primer usmerjenega poslovnega AI je specializirana rešitev, kot je FURS AI pomočnik, kjer ni v ospredju splošno klepetanje, temveč hitrejši dostop do relevantnih informacij v specifičnem kontekstu.

Enako velja za digitalne produkte, ki gradijo na AI kot delu celotne uporabniške izkušnje. Pri platformah, kot je Shelfze, ni pomembno le, da AI nekaj predlaga, ampak da rešitev dejansko pomaga uporabniku v realnem scenariju in je tehnično stabilna, smiselno oblikovana ter poslovno vzdržna.

7 kriterijev, po katerih oceniti AI agencijo, preden podpišete pogodbo

Če želite praktičen odgovor na vprašanje how to evaluate an AI agency, začnite s spodnjimi sedmimi kriteriji. Ti hitro razkrijejo, ali imate pred sabo partnerja za poslovni rezultat ali le ponudnika predstavitev.

1. Ali začnejo pri problemu, ne pri tehnologiji?

Dobra agencija najprej vpraša, kaj želite izboljšati: prihodke, stroške, odzivnost, kakovost storitve, interno produktivnost ali zadovoljstvo strank. Slaba agencija začne z modeli, trendi in buzzwordi. Če prva delavnica ne vodi do natančne definicije problema, želenih rezultatov in prioritete primerov uporabe, je projekt že na šibkih temeljih.

2. Ali znajo dokazati poslovne rezultate, ne le tehničnih sposobnosti?

Zahtevajte primere, kjer je agencija pokazala vpliv na poslovanje: prihranek časa, nižje stroške obdelave, večje število obravnavanih zahtevkov, višjo stopnjo avtomatizacije ali boljšo uporabniško izkušnjo. Demo ni isto kot referenca. Vprašajte po tem, kaj je bilo merjeno pred in po uvedbi.

3. Ali razumejo podatke, integracije in omejitve vašega okolja?

Veliko AI projektov ne pade zaradi modela, ampak zaradi nepripravljenih podatkov, slabe dokumentacije ali kompleksnih sistemov. Deloitte ugotavlja, da je razširjanje generativnega AI v organizacijah močno odvisno od upravljanja podatkov, zaupanja in organizacijske pripravljenosti Deloitte, State of Generative AI in the Enterprise, 2024. Dober partner bo zato zgodaj preveril kakovost virov, dostopnost API-jev, pravice dostopa in varnostne zahteve.

4. Ali imajo jasen pristop k varnosti, zasebnosti in skladnosti?

To ni pravna opomba na koncu ponudbe, ampak osrednji del projekta. Agencija mora znati pojasniti, kako ravna z občutljivimi podatki, kje se podatki obdelujejo, kako se beležijo dostopi, kako se preprečuje uhajanje informacij in kako se rešujejo zahteve glede skladnosti. Če delate v reguliranem okolju ali obdelujete osebne podatke, je to nepogajalsko.

5. Ali imajo metodologijo testiranja in nadzora kakovosti?

Pri AI ni dovolj klasičen QA. Potrebni so testni scenariji, primeri robnih situacij, evalvacija natančnosti, spremljanje napak, redno preverjanje delovanja po uvedbi in mehanizmi za človeški pregled. Če agencija nima načina, kako bo merila kakovost odgovorov ali odločitev modela, bo odgovornost za napake na koncu ostala pri vas.

6. Ali znajo postaviti realen ROI in fazni načrt?

Resen partner ne obljublja “revolucije v dveh tednih”. Predlaga faze: od odkrivanja priložnosti, do prototipa, pilotnega uvajanja, merjenja in širjenja. Forrester in drugi analitiki že dlje opozarjajo, da največ AI pobud propade zaradi nejasnega lastništva in neizmerjene vrednosti, ne zaradi pomanjkanja modelov. Vprašajte, kako bodo ocenili korist, stroške, čas do vrednosti in kriterije za nadaljevanje ali ustavitev projekta.

7. Ali delujejo kot dolgoročni partner, ne kot enkratni izvajalec?

AI rešitev po lansiranju ni končana. Potrebuje spremljanje, optimizacijo promptov in tokov, prilagoditve ob spremembah podatkov, nadzor nad stroški uporabe modelov ter podporo uporabnikom. Izberite partnerja, ki razume, da je uvedba AI proces. Prav zato pri M-AI gradimo projekte z mislijo na produkcijsko uporabo, ne le na prvi “wow” učinek.

“Most companies today are not lacking AI models. They are lacking the organizational ability to deploy them effectively.”

To stališče se redno ponavlja v industrijskih analizah in dobro opiše realnost trga: redka dobrina ni več dostop do modela, temveč sposobnost izvedbe.

Praktičen checklist za mala in srednja podjetja pri izbiri AI partnerja

Mala in srednja podjetja pogosto nimajo časa za dolge razpise ali tehnično poglobljene evalvacije. Zato pomaga preprost kontrolni seznam. Če lahko na večino spodnjih točk odgovorite z “da”, ste verjetno na dobri poti.

1. Je agencija razumela naš poslovni problem v enem stavku?
   Če ga ne zna jasno povzeti, ga verjetno tudi ne bo dobro rešila.
2. So predlagali konkreten primer uporabe z merljivim ciljem?
   Na primer: skrajšanje časa obdelave zahtevkov, avtomatizacija priprave odgovorov, boljše iskanje po interni dokumentaciji.
3. So preverili naše podatke in sisteme pred oddajo končne ponudbe?
   Brez tega je ponudba pogosto preveč optimistična.
4. So pojasnili, kateri del rešitve je generativni AI in kateri del klasična logika ali integracija?
   To je dober znak tehnične zrelosti.
5. So odprto govorili o omejitvah in tveganjih?
   Če slišite le prednosti, ne poslušate celotne zgodbe.
6. So predstavili plan pilotnega projekta?
   Za SMB je to običajno najbolj varen način: manjši obseg, hitri rezultati, jasni kriteriji za širitev.
7. So definirali, kako se meri uspeh?
   Čas, strošek, natančnost, stopnja uporabe, zadovoljstvo uporabnikov ali vpliv na prodajo.
8. So pojasnili stroške po uvedbi?
   Sem sodijo licence, uporaba API-jev, vzdrževanje, nadzor, podpora in morebitne nadgradnje.
9. So pokazali reference, podobne našemu kontekstu?
   Ni nujno isti sektor, pomembni pa so primerljivi procesi in kompleksnost.
10. Imamo občutek, da govorimo s partnerjem, ne s prodajalcem modne tehnologije?
    Ta občutek je pogosto presenetljivo natančen.

Za SMB-je je dodatno pomembno še nekaj: AI projekt naj ne bo izoliran eksperiment. Najboljši rezultati nastanejo, ko rešitev izboljša obstoječe delo zaposlenih ali odpre jasno novo priložnost. To je lahko pomoč uporabniški podpori, avtomatizacija administracije, iskanje po internih pravilih, priprava povzetkov dokumentov ali AI-vodena funkcionalnost v digitalnem produktu.

Če iščete partnerja, ki AI obravnava praktično in poslovno, je smiselno pogledati primere pristopa in rešitev na m-ai.info. Ključno pa je, da pred sodelovanjem zahtevate iskren pogovor o ciljih, podatkih, tveganjih in pričakovanih rezultatih. To vam bo prihranilo več denarja kot katerakoli “ugodna” ponudba brez prave osnove.

Zaključek: pravo vprašanje ni, kdo zna uporabljati ChatGPT, ampak kdo zna z AI izboljšati vaše poslovanje

Če povzamemo: ko razmišljate o tem, how to evaluate an AI agency, se osredotočite na poslovni učinek, podatke, integracije, varnost, testiranje, ROI in partnerstvo po uvedbi. Promptanje je danes osnovna kompetenca, ne pa razlog za izbiro strateškega izvajalca. Prava razlika je v tem, ali agencija zna AI pripeljati iz demonstracije v produkcijo in iz navdušenja v rezultat.

Najboljša AI agencija za vaše podjetje ni nujno tista z največ tehničnimi izrazi ali najbolj spektakularnim demo videom. Je tista, ki zna poslušati, postaviti prava vprašanja, pošteno opozoriti na omejitve in zgraditi rešitev, ki jo bodo ljudje dejansko uporabljali.

Želite oceniti, ali je AI sploh prava poteza za vaš primer?

Če želite konkreten, poslovno usmerjen pogovor o tem, kje ima AI v vašem podjetju največ smisla in kako izbrati pravi pristop, stopite v stik z ekipo M-AI. Skupaj lahko pregledamo vaše procese, prepoznamo najbolj donosne primere uporabe in pripravimo realen načrt uvedbe.

Rezervirajte pogovor prek /#contact in preverite, kako lahko AI v vašem podjetju ustvari merljivo vrednost, ne le zanimivega demota.