AI Consulting and Roadmap

What is AI consulting, and who is it for?

AI use in companies usually starts scattered: one team drafts emails with a chat assistant, another has reports summarised, and a manager says "we need an AI project too". AI consulting turns that scatter into a plan. The aim is to find where AI genuinely saves time or improves quality in your company, roll it out under safe rules and start the investment with a measurable pilot.

UNIT İstanbul is an agency that has managed the digital growth of brands through measurement since 2009. We deliver enterprise AI consulting with the same discipline: business goal first, then data and process, the tool last. If the consulting shows that something needs to be built, our software brand Unit Software steps in; if not, we recommend moving ahead with off-the-shelf tools. The service is especially useful when:

• Leadership wants to invest in AI but it is unclear where to start.
• Employees use different AI tools on their own and nobody knows which data goes into them.
• An earlier trial was run, but its result could not be measured or the project stalled.
• Quotes have come in from several vendors and you cannot decide between an off-the-shelf tool and custom software.
• Repetitive work in marketing, sales or customer service keeps growing and the team cannot carry the load.

This page should not be confused with our digital marketing consulting service: that service audits your ad accounts, SEO and measurement setup, while AI consulting decides where and under which rules AI will be used across the whole company.

How ready is your company for AI?

AI solutions work only as well as the ground they are built on. That is why we start consulting with a readiness assessment. Instead of handing out a score, we ask concrete questions in each area and write every gap into the roadmap as a task.

How are AI use cases found and prioritised?

The best AI ideas usually come from the people who do the work every day. We hold short interviews with department leads and map together where a typical week's work goes. Repetitive, text-heavy tasks with fairly clear rules make the shortlist. We then assess each candidate with two questions: what will its impact be, and how easy is it to implement?

We add a risk dimension to this table: an answer that goes straight to a customer needs more oversight than a summary prepared for internal use. The result is a list of use cases in which every row names an owner, a rationale and a prerequisite.

Off-the-shelf tool or custom solution?

Not every use case needs custom software. For most companies the right start is an off-the-shelf tool with an enterprise plan and the right settings. A custom solution makes sense when you need to connect company data, talk to existing systems or offer a service open to customers.

We stay neutral throughout the consulting: our recommendation follows what the use case needs, not a product we want to sell. When a custom solution is needed, the technical detail is covered on our AI integration page; depending on the work, delivery continues through our AI automation, AI chatbot or AI agent development services.

How is the pilot chosen and how is success measured?

The pilot is the first concrete step on the roadmap. It should be narrow in scope, have a clear owner and produce a measurable result. Before the pilot starts we record the current state, so that at the end the question "did it get better?" rests on a comparison rather than an impression. Depending on the use case, we choose the relevant indicators from these:

• Time saved: how long a task takes from start to finish.
• Requests answered: how many questions or applications the same team can handle.
• Error rate: the share of outputs that need correcting, and the types of error.
• Adoption: which employees actually use the tool, and why the others do not.
• Quality: the reviewer's assessment of the output and customer feedback.

At the end of the pilot one of three decisions is made: roll it out, fix it and try again, or stop. Stopping is a valuable outcome too; it prevents a large budget going to the wrong use case.

Governance: usage policy, data protection and security

The biggest risk in AI use is often not technical but the absence of rules. An employee pasting a customer list or a draft contract into a personal account can have serious consequences under data protection law, such as the GDPR or Türkiye's KVKK, and for commercial confidentiality. That is why the consulting ends with a plain AI usage policy written for your company.

• A list of approved tools and the type of account each one must be used with.
• The tasks in which AI output may not be used without human review.
• How customers are informed when they receive content or an answer prepared with AI.
• The assessment steps to follow when someone requests a new tool.

We carry out the legal interpretation together with your legal adviser or data protection team; we prepare and document the policy text, the data classes and the technical safeguards.

Where does AI help in marketing and sales?

As a marketing agency, this is where our knowledge runs deepest. From our daily work we know where AI speeds up advertising, content and reporting, and where it still needs a human eye. Use cases we often assess for marketing and sales teams include:

• Drafting ad copy and visual variations, leaving the final choice and brand fit to the team.
• Summarising search term reports, campaign data and competitor ads to highlight what deserves a closer look.
• Producing the first draft of weekly and monthly reports and leaving the interpretation to a specialist.
• Classifying incoming leads by topic and priority and passing them to the sales team in the right order.
• Producing drafts in the brand voice for content calendars, product descriptions and FAQs.

All of this is about using AI inside your own company. Getting your brand cited as a source in ChatGPT, Gemini or Google's AI answers is separate work; for that, see our GEO (generative engine optimisation) page.

How do teams adapt to the change?

Buying a tool does not mean it will be used. Employees may worry that their work will change, that they will be blamed for mistakes or that the tool will make them redundant. That is why we identify use cases together with the teams and run the pilot alongside the people who do the work.

At the end of the consulting we prepare a short usage guide for every approved use case: which tool, which steps, which data; how the output is checked; who to contact when something goes wrong. Over time the guides are updated with examples from the people who use the tool best.

When is AI not the right answer?

Honest consulting includes not recommending AI. In the following situations we recommend a simpler solution or preparation work first:

• Work that needs exact calculation or rule-based decisions: conventional software is more reliable and more economical.
• If the process is not yet written down: the process must be clarified first, then automated.
• If the data is scattered or out of date: the data must be organised first.
• If the cost of an error is high and human review cannot be put in place: AI should be used only for drafts and first-pass review.

What do you have when the consulting ends?

At the end of the consulting you hold a written roadmap showing what your company will do with AI, what it will not do and in which order it will proceed. The document is yours; you can carry out the implementation with your own team, a vendor of your choice or Unit Software. To discuss your current situation and get a free initial assessment, reach us through our contact page.