# Decide where to start with AI on written evidence. > Use-case discovery, feasibility, data readiness, build versus buy and an actionable roadmap: AI consulting for SMEs and enterprise teams, with written findings. Source: https://www.algofact.tech/en/services/technology-assessment/ai-consulting Language: en-US Updated: 2026-10-26 Languages: en-US, tr-TR ALGOFACT helps SMEs and enterprises work out where AI will create value, whether their data is ready for it and whether a solution should be built or bought, with written findings. We rank use cases by business impact and feasibility, review feasibility and data readiness for the priority ones and deliver a roadmap with phases, budget lines and owners. If the right advice is to wait, the report says so. The work is run by our engineering team in Türkiye, with a US entity in Delaware. ## Key facts - **Output:** Written findings and a roadmap - **Who it is for:** SMEs and enterprise teams - **Scope:** Use cases, feasibility, data, decision - **Decision options:** Build, buy or wait - **Independence:** Assessment kept separate from delivery - **First step:** Discovery call and scope presentation ## When does AI consulting help? When leadership expects results from AI, several departments are trying different tools on their own, and nobody can say which job comes first or what it will return, consulting is the right first step. The goal is not a vision deck but a written assessment you can base next budget cycle decisions on. If you already know what you want to build and your data is ready, you may not need consulting at all; starting directly with a proof of concept or a build project is faster, and we will tell you that in the discovery call. The assessment is kept separate from delivery work. If an off-the-shelf product, another team or not using AI at all is the better choice, the report says so. ## Questions the engagement answers Every engagement is built around five questions. For SMEs the scope usually centers on one or two use cases; for enterprise teams it spans several departments. ### Use-case discovery In sessions with department leads we identify work that repeats, produces data and is costly to get wrong, then score each idea on expected business impact and feasibility. ### Technical feasibility For the priority use case we assess which approach fits (classical machine learning, a language model, computer vision or rule-based automation) and whether the expected accuracy is good enough for the business. ### Data readiness We check whether the needed data exists, which system holds it, how good it is, whether it is labeled and whether it contains personal data. ### Build, buy or wait We compare off-the-shelf SaaS products, cloud providers’ AI services and custom development on total cost of ownership, data privacy and vendor lock-in. ### Governance and risk We identify who model errors would affect and where human approval is needed. If you sell into the EU, we flag how the EU AI Act might classify your use case; the legal reading comes from your counsel. ## How the engagement runs ### Discovery call and scope In a 30-minute call we listen to your goals, the tools you have tried and your decision timeline. Our founders then present the proposed assessment scope. ### Stakeholder sessions Separate sessions with business units, IT and information security, reviewing current processes and the systems in use. ### Data and system review For the priority use cases we look at sample data and the relevant systems with read access and measure data quality and integration effort concretely. ### Option analysis For each priority use case we write out build, buy or wait options with cost lines, timing, risk and the in-house skills each one requires. ### Roadmap session We review the findings with leadership and agree on the first pilot to launch and the next steps, each with an owner. The decision is yours. ## What you receive ### Use-case inventory A scored and prioritized list of ideas, each with its rationale and assumptions. ### Feasibility notes For the priority areas: the recommended approach, expected risks and what a proof of concept has to prove. ### Data readiness report Data sources, quality findings, gaps and the work needed to close them. ### Build versus buy comparison A side-by-side comparison of options on cost lines, lock-in and privacy. ### Roadmap Phases, budget lines, in-house staffing needs, success criteria and decision points. ## How we rank use cases Scoring exists to rank ideas by evidence rather than excitement. The reasoning behind every score is written into the report. - **Business impact:** Time saved, errors reduced or revenue gained. Measured against today’s baseline where possible; estimated value is written down together with its assumptions. - **Feasibility:** Whether the data exists and how good it is, integration effort, the accuracy required and how likely users are to adopt the result. - **Risk:** Who an error affects, whether personal data is processed, issues flagged under the GDPR, Türkiye’s Law No. 6698 or, for the EU market, the EU AI Act, and dependence on a single vendor. - **Ownership:** Who will run the solution after go-live. If that skill is missing in house, how to build it goes into the roadmap. > Public funding or support programs can be noted as options in the roadmap. Eligibility and approval are decided by the program itself; consulting does not include any guarantee of a grant or subsidy. ## Questions ### Is AI consulting only for large companies? No. For SMEs the scope usually centers on one or two use cases and a quick review of existing data. For enterprise teams it also covers several departments, information security and procurement. We size the scope to the need in the discovery call. ### Will you just recommend that we hire you to build it? Not necessarily. The assessment is kept separate from delivery. If an off-the-shelf product, another team or no AI at all is the better choice, the report says so, and who builds the solution is your decision. ### Does consulting make us eligible for grants or public funding? No, we do not guarantee any grant or support. The roadmap can help you sharpen the project definition and technical rationale a program application needs, but eligibility and approval are decided by the program. ### What data access do you need? For the priority use cases, representative sample data and read access to system structures are usually enough. If sensitive data is involved we work with masked or anonymized samples and manage access under our ISO/IEC 27001:2022 certified procedures. ### What drives the timeline and cost? The number of departments and use cases in scope, the number of stakeholder sessions, how quickly access to data and systems is granted and whether feasibility needs a short proof of concept. Most technology assessments take one to three weeks; we present the exact scope in writing after the discovery call. ## Related - [Technology Assessment](https://www.algofact.tech/en/services/technology-assessment) - [KVKK and ISO 27001 compliant software](https://www.algofact.tech/en/services/technology-assessment/kvkk-iso27001-compliance) - [Custom AI models](https://www.algofact.tech/en/services/ai-ml/custom-ai-models) - [Discovery call](https://www.algofact.tech/en/contact) ## Sources 1. [Personal Data Protection Law (Law No. 6698)](https://www.kvkk.gov.tr/Icerik/6649/Personal-Data-Protection-Law), Personal Data Protection Authority (KVKK) 2. [Regulation (EU) 2024/1689 (Artificial Intelligence Act)](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng), EUR-Lex, Publications Office of the European Union