What Does AI Consulting Include for SMBs? Kaj vključuje AI svetovanje za MSP?

AI consulting for SMBs includes much more than choosing a tool. In practice, it means identifying the right business use cases, assessing your data and processes, selecting or designing the solution, integrating it into day-to-day operations, managing change, and measuring ROI. The best AI consulting engagements help small and mid-sized businesses move from curiosity to implementation without wasting budget on vague pilots or overengineered systems.

For SMBs, this matters because AI adoption is no longer just for enterprises. According to McKinsey, 72% of organizations reported using AI in at least one business function in 2024 McKinsey, The State of AI in Early 2024. But using AI successfully is a different challenge than simply buying software. That is where consulting becomes valuable: it turns business goals into a practical, scoped roadmap with measurable outcomes.

If you are asking what does AI consulting include, the short answer is: strategy, prioritization, implementation planning, technology selection, process redesign, team enablement, and performance tracking. A capable partner should also help you avoid the common SMB trap of pursuing AI before the business case is clear.

What AI consulting actually includes

At a high level, AI consulting helps an SMB answer four questions: Where can AI create value? What data and systems are available? What solution should be built or adopted? And how will results be measured?

A complete AI consulting engagement often includes the following components:

• Business opportunity assessment: identifying repetitive tasks, bottlenecks, customer pain points, and revenue opportunities where AI can make a measurable difference.
• Data and systems audit: reviewing existing software, databases, content, workflows, and data quality to determine what is feasible.
• Use-case prioritization: ranking possible projects by impact, complexity, cost, and implementation speed.
• Solution design: deciding whether to use off-the-shelf AI tools, custom automation, retrieval systems, predictive models, or a hybrid approach.
• Implementation planning: defining scope, milestones, owners, budget, risks, and compliance requirements.
• Integration support: connecting the AI solution with CRM, ERP, e-commerce, knowledge bases, support systems, or internal workflows.
• Change management and training: making sure employees know how to use the solution correctly and consistently.
• Performance measurement: setting KPIs such as time saved, conversion lift, lower support volume, or improved forecast accuracy.

For SMBs, the most valuable AI consulting is usually highly practical. It should not end with a slide deck. It should result in a realistic roadmap, a prioritized shortlist of use cases, and ideally an implementation path that fits your budget and internal capabilities.

This is also where specialized providers can make a difference. For example, M-AI d.o.o focuses on turning AI into working business systems rather than abstract recommendations, whether the need is workflow automation, AI-powered search, retail optimization, or tailored solutions tied to existing operations. You can explore the broader service approach at m-ai.info.

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

Sundar Pichai

That may sound large-scale, but for SMBs the practical version is simple: use AI where it removes friction, improves decisions, or increases capacity without adding headcount at the same pace.

The 6-step AI consulting process for SMBs

While every business is different, a reliable AI consulting process for SMBs usually follows six steps.

1. Discovery and business diagnosis

The first step is understanding the company, not the model. A consultant should review your business goals, margins, service model, customer journey, and operational bottlenecks. In SMBs, useful AI projects often start in customer support, sales operations, internal knowledge access, reporting, demand planning, or document-heavy workflows.

This stage should answer questions like:

• Which tasks consume the most time?
• Where do employees repeat the same work every day?
• Which customer interactions could be faster or more personalized?
• Where are decisions currently based on guesswork instead of data?

If a consultant jumps straight to recommending a chatbot or a custom model without first understanding the business process, that is a warning sign.

2. Data, tools, and readiness assessment

Next comes feasibility. AI consulting includes evaluating what data you actually have, where it lives, how clean it is, and whether your current software stack can support the solution. SMBs often have useful data spread across email, spreadsheets, ERP systems, e-commerce platforms, PDF documents, and support tools.

A good consultant will also review governance, privacy, and security requirements. This matters especially in industries handling sensitive customer, financial, or operational information. IBM reports that the global average cost of a data breach reached $4.88 million in 2024 IBM, Cost of a Data Breach Report 2024, which is one reason AI projects cannot ignore data handling and access controls.

3. Use-case selection and prioritization

Most SMBs have more possible AI ideas than they can realistically execute. Consulting helps prioritize them based on:

• Business value: revenue growth, cost savings, speed, quality, or customer experience
• Ease of implementation: data availability, technical complexity, and integration needs
• Time to value: how quickly results can be demonstrated
• Risk level: compliance, operational dependency, or customer-facing exposure

The best first project is rarely the most ambitious one. It is usually the one that proves value quickly and builds confidence internally.

For example, a retailer may begin with AI-assisted product search or inventory insights before moving into advanced forecasting. A furniture or catalog-heavy business might benefit from smarter product discovery and merchandising, similar to what platforms like Shelfze are built to support. A public-sector or regulatory workflow may need document automation and structured decision support, where domain-specific solutions such as FURS can be more relevant than generic tools.

4. Solution architecture and vendor selection

Once priorities are set, AI consulting includes defining the right solution approach. That might involve:

• Using a proven SaaS AI tool
• Connecting a large language model to internal knowledge
• Building workflow automations around existing systems
• Developing a predictive model for sales, churn, or inventory
• Creating an internal assistant for staff or customer service teams

At this point, a consultant should be vendor-neutral in thinking, even if they implement selected platforms. The right recommendation depends on your processes, team skills, timeline, and total cost of ownership.

Deloitte found that 74% of surveyed organizations said their most advanced generative AI initiative was meeting or exceeding ROI expectations Deloitte, The State of Generative AI in the Enterprise, 2024. But those results typically depend on selecting the right use case and implementation model, not just adopting the newest tool.

5. Pilot, implementation, and integration

This is where strategy becomes operational. Depending on the project, the consultant may help create a proof of concept, pilot, or phased rollout. The real work often includes API connections, workflow redesign, prompt and retrieval tuning, knowledge base preparation, dashboarding, testing, and fallback procedures.

For SMBs, implementation should be scoped carefully. The goal is not to build an enterprise-scale AI stack on day one. It is to get a reliable solution into production with clear guardrails and a manageable support model.

“You don’t have to see the whole staircase, just take the first step.”

Martin Luther King Jr.

That principle fits AI adoption well. A focused pilot that saves 10 hours a week for a team can be more valuable than a broad initiative that never reaches production.

6. Training, optimization, and ROI measurement

AI consulting should not stop at launch. Employees need usage guidance, managers need visibility into performance, and leadership needs proof of value. This final step includes training, process documentation, KPI reviews, and iterative optimization.

PwC has estimated that AI could contribute up to $15.7 trillion to the global economy by 2030 PwC, Sizing the Prize, but individual SMBs only benefit if projects are measured in concrete business terms. Useful KPIs include:

• Hours saved per employee or team
• Reduction in manual processing time
• Customer response time improvement
• Increase in lead conversion or average order value
• Inventory turnover or forecast accuracy gains
• Error reduction and compliance consistency

Costs, ROI and red flags to watch for

AI consulting costs vary based on scope. For SMBs, engagements usually fall into three broad categories:

• Advisory and roadmap projects: focused assessments and recommendations
• Pilot or proof-of-concept projects: limited implementation to validate one use case
• Full implementation programs: design, integration, training, and optimization

The cheapest option is not always the best value. If the outcome is only generic advice, the real cost may be delay and confusion. A better question than “What does AI consulting cost?” is “What measurable business result will this project target?”

Good consultants will discuss ROI in practical terms. For example:

• If AI reduces 200 hours of monthly manual work, what is that worth?
• If support response time falls by 40%, does retention improve?
• If better product discovery lifts conversion by even a few percentage points, how much revenue does that create?

There are also red flags SMBs should watch for:

• No discovery process: they pitch a solution before understanding your business.
• Overpromising: claims of guaranteed transformation with no mention of data quality, adoption, or integration challenges.
• No ROI framework: they cannot define KPIs or business outcomes.
• Tool-first thinking: they sell a platform instead of solving a process problem.
• No governance discussion: they ignore privacy, security, and permissions.
• No implementation support: they deliver a strategy deck with no path to execution.

A strong consulting partner will be candid about trade-offs. Some use cases are not ready yet. Some datasets need work first. Some teams need simpler automation before more advanced AI. That honesty is usually a positive sign.

How to choose an AI partner who can deliver

If you are comparing providers, look for an AI consulting partner that combines business understanding with implementation capability. SMBs benefit most from teams that can bridge strategy and execution, because there is often no large internal AI department to take over after the planning phase.

Here are the most important selection criteria:

Industry and workflow relevance

Ask whether they understand your actual operating model. AI for a retailer, distributor, public agency, or professional services firm looks very different in practice. Relevant examples matter more than buzzwords.

Ability to prioritize value

The right partner should help you say no to weak use cases. If every idea is presented as urgent, you are not getting strategic advice.

Technical and integration depth

Even small AI projects often need integration with existing systems. Ask how they connect tools, handle access controls, structure data, and maintain reliability.

Focus on adoption

The solution only matters if your team uses it. Ask how they train users, document workflows, and support change management.

Clear measurement model

Your partner should define success before implementation starts. If they cannot describe the expected KPI movement, the project may not be ready.

For many SMBs, the ideal partner is one that can start with a practical assessment, identify a short list of high-value use cases, and then help implement the selected solution. That execution-oriented model is where firms like M-AI can be especially useful, particularly when the project requires tailored AI systems, operational integrations, or productized solutions such as Shelfze and FURS.

Final thoughts: AI consulting should create clarity, not complexity

So, what does AI consulting include for SMBs? It includes identifying where AI can create measurable business value, checking whether your data and systems can support it, selecting the right use case, designing the solution, implementing it responsibly, and proving ROI over time.

The best AI consulting is not about making your business sound innovative. It is about making your business work better. For SMBs, that often means starting with one focused, high-impact opportunity and building from there.

Ready to explore what AI can do for your business?

If you want a practical view of where AI fits in your operations, sales, support, or product workflows, talk to M-AI d.o.o. We help businesses move from ideas to working AI solutions with a clear business case and implementation path. Visit m-ai.info/#contact to start the conversation.

AI svetovanje za MSP vključuje veliko več kot le izbiro orodja ali kratko delavnico. V praksi pomeni strukturiran proces: od prepoznave poslovnih priložnosti, analize procesov in podatkov, do izbire pravih primerov uporabe, priprave pilotnega projekta, uvedbe rešitev, merjenja učinkov in usposabljanja ekipe. Če vprašanje “what does AI consulting include” prevedemo v jezik malih in srednje velikih podjetij, je odgovor preprost: AI svetovanje pomaga podjetju ugotoviti, kje umetna inteligenca res prinese vrednost, kako jo varno in ekonomsko upravičeno uvesti ter kako iz poskusa narediti merljiv poslovni rezultat.

Za MSP je to posebej pomembno, ker nimajo neomejenih virov za eksperimentiranje. Napačna odločitev pri izbiri tehnologije, ponudnika ali primera uporabe lahko pomeni izgubljene mesece in stroške brez učinka. Dobro AI svetovanje zato ni “prodaja hype-a”, ampak kombinacija poslovne analize, tehnološkega znanja, izvedbe in upravljanja sprememb.

V podjetju M-AI tak pristop pomeni osredotočenost na konkretne procese, hitre pilotne projekte in rešitve, ki jih je mogoče vključiti v vsakodnevno delo podjetja. Cilj ni pokazati, kaj je AI zmožen v teoriji, ampak kaj lahko zares izboljša v prodaji, administraciji, podpori strankam, financah, kadrih ali skladnosti poslovanja.

What AI consulting actually includes

Ko podjetja iščejo odgovor na vprašanje what does AI consulting include, pogosto pričakujejo tehničen seznam storitev. V resnici pa kakovostno AI svetovanje združuje štiri ravni: poslovno strategijo, procesno optimizacijo, tehnološko izvedbo in uvedbo v organizacijo.

1. Analiza poslovnih ciljev

AI nima vrednosti sam po sebi. Vrednost nastane šele, ko podpira cilj, kot so nižji stroški, hitrejša obdelava zahtevkov, boljša podpora strankam, več prodajnih priložnosti ali manj administrativnega dela. Zato svetovanje običajno začne s pogovorom o prioritetah podjetja:

• Kje zaposleni izgubljajo največ časa?
• Kateri procesi so ponavljajoči, ročni ali nepregledni?
• Kje prihaja do napak, zamud ali neizkoriščenih podatkov?
• Kateri cilji so v naslednjih 6–12 mesecih poslovno najpomembnejši?

2. Mapiranje procesov in primerov uporabe

Naslednji korak je pregled procesov. Tu svetovalec preveri, kateri postopki so primerni za avtomatizacijo, podporo odločanju ali generativni AI. Za MSP so tipični primeri:

• AI pomoč pri pripravi ponudb, e-pošte in dokumentacije
• klepetalni pomočniki za podporo strankam ali interno znanje
• avtomatska klasifikacija dokumentov, računov in zahtevkov
• napovedovanje povpraševanja, zalog ali prodajnih trendov
• iskanje informacij po internih bazah znanja
• avtomatizacija administracije in skladnosti

Če podjetje posluje v okolju z več regulative, je lahko pomemben del svetovanja tudi povezava med AI in digitalizacijo poročanja, dokumentov ali davčnih procesov. Pri takšnih nalogah je relevanten tudi ekosistem rešitev, kot je furs.m-ai.info, kjer je v ospredju praktična digitalna uporabnost za slovensko poslovno okolje.

3. Pregled podatkov in sistemov

AI svetovanje vključuje tudi odgovor na zelo praktično vprašanje: ali ima podjetje podatke in sisteme, ki omogočajo smiselno uvedbo AI? To pomeni pregled:

• kakovosti in dostopnosti podatkov,
• obstoječih ERP/CRM/HR sistemov,
• integracijskih možnosti,
• pravic dostopa in varnosti,
• zahtev glede GDPR in zaupnosti.

Veliko projektov se ustavi prav tu. Ne zato, ker AI ne bi deloval, ampak ker podatki niso urejeni, procesi niso standardizirani ali pa nihče ni določil odgovornosti za uporabo sistema.

4. Izbira rešitev in arhitekture

AI svetovanje zajema tudi primerjavo različnih pristopov: uporaba obstoječih SaaS orodij, uvedba prilagojenih asistentov, razvoj notranjih rešitev ali kombinacija vsega naštetega. Dober partner ne predlaga vedno najkompleksnejše možnosti, ampak tisto, ki prinese največ vrednosti ob najmanjšem izvedbenem tveganju.

V nekaterih primerih je dovolj uvedba varnega generativnega asistenta za interno uporabo. V drugih je smiseln razvoj specializirane rešitve, povezane z internimi bazami znanja, katalogi ali prodajnimi podatki. Pri produktnih okoljih in digitalni prodaji so lahko koristne tudi specializirane platforme, kot je Shelfze, kadar je cilj boljša dostopnost in uporaba vsebine ali znanja.

5. Pilot, merjenje in uvedba

Pomemben del AI svetovanja je priprava pilotnega projekta. Namen pilota ni navdušiti z demonstracijo, ampak preveriti tri stvari:

1. ali rešitev deluje v realnem procesu,
2. ali jo bodo zaposleni dejansko uporabljali,
3. ali učinki upravičijo nadaljnjo investicijo.

Po podatkih McKinseyja organizacije vse pogosteje dosegajo merljive poslovne koristi z uporabo AI, pri čemer največji učinki nastajajo tam, kjer je uvedba povezana s prenovo delovnih tokov in ne le z uporabo enega orodja McKinsey, The State of AI, 2024.

6. Usposabljanje, upravljanje sprememb in governance

AI projekt ni končan, ko je rešitev vklopljena. Svetovanje vključuje tudi usposabljanje zaposlenih, pripravo pravil uporabe, definicijo odgovornosti in spremljanje rezultatov. To je posebej pomembno pri generativni AI, kjer je treba jasno določiti:

• katere podatke je dovoljeno uporabljati,
• kdaj je potreben človeški pregled,
• kako se preverja kakovost odgovorov,
• kako se meri prihranek časa in vpliv na poslovanje.

“Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity.”

Ta misel Fei-Fei Li dobro povzema tudi bistvo AI svetovanja: cilj ni zamenjati ljudi, ampak izboljšati način dela.

The 6-step AI consulting process for SMBs

Za mala in srednje velika podjetja je najbolj učinkovit jasen, postopen model uvedbe. Spodaj je tipičen 6-stopenjski proces, ki zmanjšuje tveganje in poveča možnost, da AI res prinese ROI.

1. Diagnostika in prioritetizacija

Najprej se identificirajo poslovne bolečine, ozka grla in hitre zmage. Rezultat ni dolg seznam idej, ampak kratek nabor prioritet glede na vpliv in izvedljivost.

Po raziskavi OECD številna mala podjetja zaostajajo pri digitalni preobrazbi predvsem zaradi pomanjkanja znanja, virov in strateške usmeritve, ne nujno zaradi pomanjkanja tehnologije OECD, The Digital Transformation of SMEs, 2021.

2. AI opportunity assessment

V tej fazi se vsak primer uporabe oceni glede na:

• pričakovani prihranek časa ali denarja,
• potrebne podatke,
• kompleksnost uvedbe,
• varnostna in pravna tveganja,
• čas do prve vrednosti.

MSP običajno največ pridobijo pri primerih uporabe, ki imajo kratek čas uvedbe in visoko frekvenco ponavljanja.

3. Data and systems readiness

Pred izvedbo je treba preveriti pripravljenost podatkov in sistemov. Če tega koraka ni, se pilot hitro spremeni v drag eksperiment. Dobra praksa je minimalna tehnična priprava: dostopi, struktura dokumentov, pravila rabe in izhodiščni KPI.

4. Pilotna implementacija

Pilot naj bo dovolj majhen, da je hiter, in dovolj konkreten, da ga je mogoče izmeriti. To je lahko na primer AI pomočnik za pripravo odgovorov strankam, avtomatsko povzemanje sestankov ali klasifikacija vhodnih dokumentov.

Po podatkih Microsoftove in LinkedInove raziskave večina zaposlenih že uporablja AI pri delu, pogosto tudi brez formalnega okvira podjetja, kar povečuje potrebo po strukturirani uvedbi in pravilih uporabe Microsoft & LinkedIn, Work Trend Index, 2024.

5. Merjenje rezultatov in poslovni primer

Uspešen pilot mora odgovoriti na vprašanja:

• Koliko časa prihranimo na proces?
• Koliko manj napak nastane?
• Kako se je izboljšala odzivnost ali produktivnost?
• Kakšen je strošek na uporabnika ali proces?

Tukaj se izkaže razlika med demo projektom in resnim AI svetovanjem. Brez merjenja ni mogoče utemeljiti širitve rešitve.

6. Skaliranje in operativna uvedba

Ko pilot dokaže učinek, sledi širitev: na druge ekipe, dodatne procese ali večji obseg podatkov. V tej fazi se pogosto uvedejo tudi standardi, interne smernice in trajna podpora. V podjetju M-AI je to pogosto točka, kjer se svetovanje prevesi v konkretno implementacijo, integracije in stalno optimizacijo.

“AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire.”

Izjava Sundarja Pichaia je ambiciozna, vendar za MSP velja bolj pragmatičen prevod: AI je pomemben takrat, ko reši konkreten poslovni problem hitreje, ceneje ali bolje kot obstoječi način dela.

Costs, ROI and red flags to watch for

Ena najpogostejših skrbi MSP je cena. Upravičeno. Stroški AI svetovanja in uvedbe se lahko zelo razlikujejo glede na obseg, kompleksnost, podatke in stopnjo prilagoditve.

Kaj običajno vpliva na strošek

• ali gre za svetovanje, pilot ali celovito implementacijo,
• število procesov in oddelkov,
• potreba po integracijah v ERP, CRM ali dokumentne sisteme,
• zahteve glede varnosti, hostinga in skladnosti,
• obseg usposabljanja in podpore.

Za MSP je pogosto najbolj smiselna fazna investicija: najprej delavnica in ocena priložnosti, nato pilot, šele potem širitev. Ta pristop zmanjša tveganje in omogoča hitrejši dokaz učinka.

Kako računati ROI

ROI pri AI ni treba ocenjevati abstraktno. Začnite s tremi kategorijami:

1. Prihranek časa: koliko ur mesečno se sprosti zaposlenim?
2. Zmanjšanje napak: koliko manj popravljanja, reklamacij ali zamud nastane?
3. Rast prihodkov: ali ekipa hitreje obdela več povpraševanj, bolje neguje lead-e ali poveča konverzijo?

Po podatkih IBM je glavni razlog za uvedbo AI pri podjetjih prav izboljšanje učinkovitosti in avtomatizacija ključnih procesov IBM, Global AI Adoption Index, 2023. Za MSP je to dober signal: najboljši začetni projekti niso nujno najbolj “napredni”, ampak tisti, ki hitro sprostijo kapacitete ekipe.

Rdeče zastavice, na katere bodite pozorni

• Obljube brez analize: če ponudnik že v prvem klicu “ve”, kaj potrebujete, brez vpogleda v procese, je to slab znak.
• Preveč tehničnega jezika, premalo poslovnega učinka: AI projekt mora biti vezan na KPI-je, ne le na model ali platformo.
• Brez načrta za podatke in varnost: posebej pri občutljivih dokumentih in osebnih podatkih.
• Ni merjenja uspeha: brez baseline-a in KPI-jev ni mogoče dokazati vrednosti.
• Vendor lock-in brez potrebe: izogibajte se rešitvam, ki jih nihče v vašem podjetju ne razume in jih ni mogoče prilagoditi.
• Ignoriranje zaposlenih: če uporabniki niso vključeni, bo uporaba nizka ne glede na kakovost tehnologije.

How to choose an AI partner who can deliver

Pravi partner za AI svetovanje ni nujno tisti z najbolj bleščečo predstavitvijo, ampak tisti, ki zna povezati poslovni problem, realne omejitve MSP in izvedbo v razumnih korakih.

1. Iščite poslovno razumevanje, ne le tehnične kompetence

Dober partner zna govoriti o maržah, odzivnih časih, operativni učinkovitosti, podpori strankam in administrativnih ozkih grlih. Tehnologija je sredstvo, ne cilj.

2. Zahtevajte konkreten proces dela

Vprašajte, kako potekajo diagnostika, izbor primerov uporabe, pilot, merjenje in širitev. Če proces ni jasen, obstaja večja verjetnost improvizacije.

3. Preverite, ali znajo implementirati, ne le svetovati

Veliko ponudnikov pripravi dober strateški dokument, nato pa podjetje ostane samo pri izvedbi. Idealno je sodelovati s partnerjem, ki lahko svetuje, gradi prototip, integrira rešitev in pomaga pri uvedbi v ekipo.

4. Ocenite pragmatičnost

Za MSP je pogosto boljši partner tisti, ki predlaga 80/20 rešitev z merljivim učinkom v nekaj tednih, namesto večmesečnega projekta brez hitrega rezultata.

5. Vprašajte po governance in varnosti

Posebej če boste uporabljali interne dokumente, baze znanja ali osebne podatke. AI partner mora znati pojasniti, kako se upravlja dostop, hramba podatkov, revizijska sled in človeški nadzor.

6. Izberite partnerja, ki razume lokalni kontekst

Za slovenska MSP je pomembno, da partner razume lokalne procese, dokumentacijo, jezikovno rabo, regulativo in realne operativne omejitve. Prav tu lahko specializiran partner, kot je M-AI, ponudi prednost: kombinacijo AI znanja, praktične implementacije in razumevanja potreb podjetij v regiji.

Če povzamemo: odgovor na vprašanje what does AI consulting include je bistveno širši od “izbire AI orodja”. Vključuje analizo poslovnih ciljev, odkrivanje pravih primerov uporabe, pripravo podatkov in sistemov, pilotno uvedbo, merjenje ROI, usposabljanje ekipe in dolgoročno optimizacijo. Za MSP je najboljši pristop postopen, pragmatičen in osredotočen na procese, kjer se učinek pokaže hitro.

Želite preveriti, kje ima AI največ smisla v vašem podjetju?

Če želite praktično oceno priložnosti, prioritetni seznam primerov uporabe ali pilotni AI projekt z merljivim učinkom, stopite v stik z ekipo M-AI. Skupaj lahko pregledamo vaše procese, določimo hitre zmage in pripravimo realen načrt uvedbe.

Kontaktirajte M-AI prek obrazca na /#contact in rezervirajte uvodni pogovor.