AI marketing services pricing at Brainguru is quoted per project, after a free consultation, because the cost depends on your channels, data and goals rather than a fixed rate card. This page explains how scope usually varies with the size of a business, what moves the price up or down, and how to judge whether the spend is paying off.
How AI marketing services pricing works
We do not publish prices. Two businesses asking for "AI SEO" or "AI lead generation" can need very different amounts of work, depending on the size of the website, the state of their data and the systems that must be connected. A fixed price list would either overcharge the simple project or underprice the complex one.
Instead, each engagement follows the same steps:
- Free consultation: We talk through your goals, channels, current tools and data.
- Scope: We agree what is in and out, the deliverables and how success will be measured. Our AI marketing strategy page explains how to define the objective and a bounded pilot first.
- Written quote: You receive a proposal that lists the work, any setup or onboarding tasks, the reporting and the terms, before you commit. Quotes are exclusive of applicable taxes.
- Review: The scope can be adjusted as priorities change, on the terms set out in the contract.
How scope varies with business size
Scope tends to follow the size of the business and how far its marketing has come. These are not fixed packages: each engagement is quoted on its own scope.
Small businesses and startups: A first step usually covers AI-assisted SEO, such as keyword research, on-page fixes and a technical audit, basic marketing automation like welcome emails and social scheduling, and a simple dashboard with monthly reporting.
Businesses scaling their marketing: Work often adds automation across email, SMS and retargeting, AI lead scoring and chatbot qualification, AI-assisted content with human editing, and attribution reporting that shows which channels produce customers.
Larger organisations with several teams and systems: Work can include a full marketing audit, strategy, models trained on your own first-party data, integration with your CRM, ERP and business intelligence tools, and governance for how AI is used across teams.
Single services are also quoted on their own:
| Service | Typical deliverables |
|---|---|
| AI SEO | Keyword strategy, technical audits, on-page optimisation, content briefs |
| AI content | Articles, social copy, ad copy and landing pages, edited by people |
| AI lead generation | Lead scoring, chatbot qualification, nurture sequences |
| AI analytics | Dashboards, attribution, forecasting, custom reports |
| AI ads management | Search, social and display campaigns, bid optimisation, testing |
What affects the cost of AI marketing?
Scope and number of channels: A plan covering SEO, paid ads, email, social and content needs more setup and management than a single channel.
Industry rules: Healthcare, finance and legal work needs extra review against professional and advertising rules, which takes time from both sides.
Data volume and quality: AI depends on data. A business with clean, well-tracked customer data can use predictive tools sooner. One with patchy data may first need a tracking and clean-up phase.
Level of customisation: Standard tools configured for you cost less than models built on your own data. Custom work makes sense when you have the data and the volume to justify it.
Duration and goals: A short campaign sprint is priced differently from an ongoing programme, and ambitious targets need more testing and optimisation.
Integrations: Connecting CRM, ERP or e-commerce platforms is development work, more so with older systems or custom APIs.
Content review: AI drafts quickly, but people still need to check facts and brand voice. The more content you publish, the more review time it needs.
Tool licences: Some AI and marketing tools charge their own subscription. Your quote should say which tools are included and which you pay for directly.
How much do businesses spend on marketing and AI?
Benchmarks give a sense of scale, although they come from large companies. The Gartner 2026 CMO Spend Survey of 401 marketing leaders in North America, the UK and Europe found marketing budgets at 7.8% of company revenue in 2026, up slightly from 7.7% in 2025. Those leaders allocated an average of 15.3% of their marketing budgets to AI, yet only 30% reported mature AI readiness.
The gap between spending and readiness is the main pricing lesson. Buying AI tools is easy; getting value needs data, process and people. Gartner's 2025 CMO Spend Survey found that generative AI investments were delivering returns mainly through improved time efficiency (49%) and cost efficiency (40%). Plan your budget around those kinds of gains, and measure them, rather than expecting overnight revenue.
AI marketing compared with traditional marketing
AI changes where the money goes more than how much is spent. Less goes on repetitive manual work, more on data, tools and review.
| Area | Traditional approach | AI-assisted approach |
|---|---|---|
| Lead qualification | Manual, by sales staff | Automated scoring, checked by sales |
| Ad optimisation | Periodic manual changes | Continuous bid and budget adjustment |
| Content production | Fully written by hand | AI drafts, people edit and approve |
| Reporting | Static monthly reports | Live dashboards plus monthly review |
| Scaling | Grows with headcount | Grows with data and tools, plus review time |
For a fuller comparison, read traditional marketing vs AI marketing.
How to judge whether AI marketing is paying off
- Set a baseline: Record current cost per lead, conversion rate and customer acquisition cost before anything changes.
- Measure the right outcome: Sales, bookings or qualified leads, not impressions or clicks.
- Count all costs: Include tool licences and your own team's review time.
- Allow time: Paid campaigns show changes sooner than SEO and content, which build over months.
- Review monthly: Keep what works, stop what does not, and adjust the scope.
Questions to ask before you sign
- Inclusions: Exactly which deliverables, tools and reports are in the quote?
- Extras: Are there onboarding, setup or tool costs outside the quote?
- Ownership: Who owns the ad accounts, content, data and models at the end?
- Changes: How can the scope be increased or reduced, and with what notice?
- Measures: Which numbers will be reported, and how often?