AI strategy consulting that can conclude you need less of it

We price it after scoping, not from a rate card. The figure depends on how many processes and teams are in scope, and whether the readiness review and policy drafting are needed. Anything quoted before those are known is a guess. You keep the scope document whether or not you go ahead, and the proposal then carries one fixed figure.

Yes, though most of it doesn't start with a search. The conversations happen inside companies, through referrals and in board meetings, often after a pilot has stalled. Enterprise leaders and owners of smaller firms ask the same thing: which AI opportunities are worth the money?

Generative AI is good at drafting, summarising, sorting and pulling details out of documents. With natural language processing, it can answer questions from a defined body of your own material. It's poor at anything that needs reliable accuracy without a human check, and it can't replace a process that doesn't exist. The specific answer means looking at where your time goes, then using AI to streamline the worst of it.

Ask any proposal three questions. What baseline does it improve on, and what will it take to run in year two, including the person checking outputs? What happens when it's wrong? Proposals that can't answer all three are selling capability rather than measurable business value.

Buy, in most cases, and it's a clearer call than people expect. The category moves fast enough that a bespoke build risks being overtaken by a product feature within a year. Building makes sense where your data or process is genuinely unusual and the competitive advantage is worth maintaining.

Yes, and sooner than most companies think, because staff are already using these tools whether or not anyone approved it. The risk sits in today's unmanaged use: client data pasted into consumer tools and unchecked output going to customers. A short, usable policy beats a long, unread one. It's basic risk management that helps staff use AI responsibly, and drafting it is part of the engagement.

Two to four weeks. Interviews and baseline measurement take most of it. If the readiness review surfaces problems, that finding usually changes the plan more than anything else in the document.

We talk to the people who own the processes and to your leadership team, who hold the budget. The people who do the work matter most, because they know where the time goes in a way the org chart doesn't show. We work in Arabic and English, so everyone can speak in the language they use at work.

Nobody can predict the impact of AI on your numbers before the baseline is measured. Be wary of anyone who names a figure up front. We record that baseline and price each idea with upkeep included, then drop the ones where the numbers don't work. That ties AI spend to business outcomes from the start.

Smaller than you expect, and duller. Common entries are document handling, internal knowledge search, first-draft content with human editing and customer service triage. The dramatic ideas usually take more money and deliver less than the dull ones.

Yes, and the document is written so you don't have to use us. It's deliberately agency-agnostic, so you can use it to compare vendor quotes. If we do the build, we deploy AI against the measures the plan set out, and marketing results are tracked in one place. The Adnika Dashboard: one live view of a client's ads, analytics and CRM data, with AI recommendations reviewed by the strategist.

Usually because tools get bought before anyone agrees what they're for. The complexities of AI adoption are rarely technical: they're about who owns the tool, who checks it and what it replaces. A sound AI transformation strategy settles those points before any contract is signed, so the pilot has somewhere to go when it works.

No. The ideas we review range from chat assistants and AI agents to machine learning models that forecast demand or flag unusual records. Some of the most useful are small AI-powered checks inside software you already pay for, with no LLM involved. The choice between AI technologies comes after the business problem is clear, never before.

Usually, and it should. Most good projects connect AI to an existing CRM, booking system or helpdesk instead of adding another login for staff. The readiness review checks what those tools allow before anything is promised, so AI integration doesn't stall halfway. Where access is poor, fixing the data architecture comes first.

Against the baseline recorded at the start. Each item in the plan names its measure, such as hours saved, error rate or response time, and an owner who reports it. That keeps decision-making data-driven rather than based on how good a demo looked. It also shows operational efficiency in numbers your finance team will accept.

No. The method works the same way for a ten-person business as for an enterprise AI programme, with more interviews at the larger end. Smaller teams often move faster because fewer tools are involved. Scalable AI starts with one process done well, then a second built on what the first one taught you.

It should follow it. We start from your priorities, whether that's faster turnaround, new products and services or customer insights, and test each AI idea against them. AI strategy development that starts with the technology tends to produce clever pilots with no owner. Starting from the business keeps the work on a clear path to value.

Because ambitious ideas carry the most risk and the least evidence. Teams chasing the full potential of AI usually get further by proving one modest win first. That win builds the data, skills and trust the bigger project will need. The dull first project often pays for the innovation in the second one.

Interviews, baseline measurement, a readiness review, a ranked shortlist with effort and upkeep estimated, a governance policy and a ninety-day plan. These are artificial intelligence consulting services in the plain sense: paid advice, kept separate from any build. Everything is written down, and the document belongs to you.

Near the start, because it shapes the broader AI programme. Many transformation plans list AI as a line item with no detail behind it. This engagement turns that line into a ranked list, sized to your AI maturity rather than a vendor's vision. It also shows what must be in place before implementing anything, from data access to who signs off outputs.

Start with the people who will own each tool, because ownership takes more than a login. They need enough AI fluency to adjust prompts, spot bad output and know when to escalate. Everyone else needs a clear grasp of what the policy allows. The plan names who needs what, so learning doesn't get skipped.