Meta AI Tools Audit

Meta AI Tools Audit

Purpose

Produce a structured, client-specific evaluation of AI marketing tools calibrated to Uganda/East Africa budgets, payment infrastructure, and team capacity. Map every tool by function, assess EA accessibility explicitly, and deliver a recommended stack for the client's budget profile. Based on Johnsen, M. (2024) AI in Digital Marketing and Upadhyay, M. A. (2024) Generative AI for Marketing.

Use When

• Use this skill for auditing AI marketing tools for fit, access, cost and governance.
• Confirm that meta-tools-stack-evaluation is not the closer route before proceeding.

Do Not Use When

• Use meta-tools-stack-evaluation when its narrower output is requested.
• Do not publish, spend, change a live account, certify compliance, or invent missing client evidence.

Required Inputs

Outputs

Evidence Produced

Capability and permission boundary

Read and search access to the supplied artefacts are required; calculation or file-rendering capability is optional. This is read-only by default: inspect and report without changing source records, accounts, skills or campaigns. Editing the deliverable requires explicit authorisation; publishing, production mutation, destructive action, spend, and certification claims require separate explicit authority and evidence.

Degraded mode

If files, platform access, network, rendering, fonts, or calculation tools are unavailable, return the narrowest useful qualified AI tool assessment and recommended stack. Mark each blocked check not assessed, state the consequence, and provide the exact evidence needed to resume. Never convert an unavailable check into a pass.

Decision rules

Workflow

1. Confirm the requested decision, consumer, market, period and permission boundary; route to meta-tools-stack-evaluation if its contract is closer.
2. Inventory the required inputs and their provenance. Stop any decision whose critical evidence is absent; recover by requesting it or recording a bounded assumption.
3. Apply the domain method in the core sections below, following the decision table whenever evidence conflicts or scope changes.
4. Verify calculations, dates, named platforms and claims against the supplied sources; label inference and uncertainty.
5. Produce the AI tool assessment and recommended stack, decision/source register and explicit next owner. Do not mutate live systems without separate authority.
6. Run the repository anti-slop ship gate. If a blocking factual, permission or evidence defect remains, fix it or withhold release.

Quality Standards

The output is client-specific, uses British English and the stated market/currency, distinguishes observed fact from inference, exposes gaps, and gives a checkable acceptance condition. Recommendations must be feasible within the confirmed budget, capacity and permissions.

Anti-Patterns

• Using an undated benchmark as the client's result. Fix: use account evidence or label the benchmark as a provisional comparator.
• Producing the AI tool assessment and recommended stack without current tool inventory. Fix: stop the affected decision or issue a clearly bounded partial output.
• Treating missing access or data as a successful check. Fix: record not assessed, its risk and the recovery input.
• Absorbing meta-tools-stack-evaluation into this workflow. Fix: route the neighbouring output and hand over verified inputs.
• Publishing, spending or editing a live account during planning or review. Fix: obtain separate explicit authority and retain action evidence.

Worked example

Given verified current tool inventory, the skill produces a AI tool assessment and recommended stack with source dates and named assumptions. If that evidence cannot be accessed, it returns only the supported sections plus a recovery list; it does not fill gaps with East African defaults.

Read next

• meta-tools-stack-evaluation for the neighbouring contract.
• anti-ai-slop during production.
• ai-slop-audit at the release checkpoint.

References

• Anti-AI slop production gate
• Follow the directly linked repository skills above and any domain references named in the core sections below. Verify current platform, price, legal and regulatory claims before use.

Required Input

Ask for all of the following before generating any output:

1. Client business name, industry, and country/city — e.g., "Nile Organics, food retail, Kampala"
2. Primary marketing goal — e.g., increase brand awareness, generate leads, grow email list, improve content output
3. Monthly tools budget range (UGX) — Starter: under UGX 500,000 / Growth: UGX 500,000–2,000,000 / Scale: above UGX 2,000,000
4. Current tools already in use — list every tool, free or paid, currently in the workflow
5. Team technical comfort level — None (no prior software experience) / Basic (email and social apps) / Intermediate (uses multiple SaaS tools) / Advanced (API-comfortable, automation-literate)
6. Primary channels — Facebook, Instagram, WhatsApp, email, website/SEO, TikTok, LinkedIn, YouTube

Do not generate any output until all six inputs are collected.

Section 1 — AI Tool Evaluation Framework

Before recommending any tool, apply this five-question test. If any critical question is answered No, do not recommend the tool.

1. Does it address a specific, named marketing problem the client has today? — Do not recommend AI tools on novelty grounds alone.
2. Is it accessible with EA payment methods? — USD credit/debit card (Visa/Mastercard issued in EA), Mobile Money (MTN, Airtel), or free tier. Tools requiring USD PayPal or US billing address only are inaccessible for most EA clients.
3. Is there a free or low-cost entry point the team can test? — Prioritise tools with free tiers before recommending paid subscriptions.
4. Will the team realistically use it? — Match AI capability complexity to the stated technical comfort level. Advanced automation tools assigned to non-technical teams will go unused.
5. Does it process customer personal data? — If yes, a Data Processing Agreement (DPA) is required under the Uganda Data Protection and Privacy Act 2019 before recommending.

Apply this framework as a named section in every deliverable so clients can use it independently.

Section 2 — Tool Evaluation Tables by Function

Produce one table per category. Use the ratings below consistently.

EA Recommendation ratings:

• Recommended — accessible, affordable, strong EA fit
• Conditional — accessible but USD-priced; viable only if budget confirmed
• Defer — enterprise pricing, inaccessible payment, or low EA market penetration

Category 1 — Content Creation

EA note: ChatGPT and Claude are the most widely used AI writing tools in Uganda and East Africa. Jasper's USD 49/month entry point is prohibitive for most EA SME budgets; use Copy.ai starter or Claude free tier instead.

Category 2 — SEO and Content Optimisation

EA note: RankMath is the recommended default for EA clients with WordPress websites. Surfer SEO and SEMrush are USD-priced and appropriate only when a client has a dedicated SEO budget and an active content programme generating measurable organic traffic.

Category 3 — Social Media Management

EA note: FeedHive and Metricool offer the strongest value for EA budgets. Hootsuite is an enterprise product; recommend only to agencies managing 10+ client accounts with confirmed budget.

Category 4 — Email Marketing

EA note: Brevo and Mailchimp free tiers are the recommended starting points. USD card payment is required for all platforms — confirm the client has a USD-capable Visa or Mastercard before recommending any paid tier.

Category 5 — Marketing Automation

EA note: Africa's Talking is the primary EA-native automation tool and must be included in any EA marketing automation recommendation. It supports UGX billing, bulk SMS, WhatsApp Business API, and USSD — capabilities no other tool on this list replicates for the EA market. See playbook-ai-automation-workflow/SKILL.md for building automation sequences using these tools.

Category 6 — Analytics and Insights

EA note: GA4 and Meta Business Suite Insights are free, essential, and must be in every client's stack regardless of budget. Brandwatch and Sprout Social are enterprise products appropriate only for large corporate or NGO clients with confirmed analytics budgets.

Category 7 — Paid Advertising

EA note: All three platforms are accessible via Meta, Google, and TikTok business accounts using a Ugandan or EA Visa/Mastercard. Minimum ad spend thresholds apply — confirm with the client before recommending. Note that this skill covers tool selection only; paid ad campaign strategy and creative testing are out of scope (refer to playbook-paid-social-advertising/SKILL.md).

Category 8 — Influencer Marketing

EA note: All three tools are USD-priced and designed for markets with large influencer databases. EA influencer databases are sparse in these platforms. For most EA clients, manual influencer discovery (Instagram search, TikTok discovery, personal network) combined with HypeAuditor's free tier for audience vetting is more practical and cost-effective than a paid subscription. See 08-influencer-marketing-strategy/SKILL.md for manual EA influencer sourcing frameworks.

Section 3 — Recommended Stacks by Budget Profile

Budget Profile A — Starter (under UGX 500,000/month on tools)

Free tools only. Apply as the default starting point for all new clients.

Total monthly platform cost: UGX 0 + ad spend + Africa's Talking usage

Do not introduce paid tools until the client has used this stack consistently for at least one quarter and has identified a specific limitation.

Budget Profile B — Growth (UGX 500,000–2,000,000/month on tools)

Build on Profile A. Add these paid upgrades when free tier limitations are confirmed.

Approximate total: UGX 345,000–500,000/month + Profile A tools + ad spend

Budget Profile C — Scale (above UGX 2,000,000/month on tools)

Build on Profile B. Add these tools when the client has a dedicated marketing team and active programmes across all channels.

Note: Prices stated in UGX are approximate USD-to-UGX conversions at prevailing rates. Reconfirm at time of proposal. All USD-priced tools require a Ugandan or EA Visa/Mastercard with international payments enabled.

Section 4 — Output Structure

Produce the deliverable in the following order:

1. Current Stack Audit — list every tool the client currently uses, its function, cost, and whether it is actively used / partially used / unused
2. AI Tools Evaluation Tables — one table per category (all eight categories; no gaps)
3. Recommended Stack — match to the client's budget profile (A, B, or C); state all costs in UGX
4. Tools to Remove — any current tools that fail the five-question framework, with brief rationale
5. Implementation Roadmap — introduce no more than 2 new tools per quarter; include estimated onboarding time per tool (2–4 hours)
6. DPA Note — flag every tool in the recommended stack that processes customer personal data; note that a Data Processing Agreement is required under the Uganda Data Protection and Privacy Act 2019
7. Next Steps — 3 specific, named actions the client should take in the next 30 days

Quality Criteria

• All 8 tool categories are assessed with a reference table — no gaps in the evaluation
• EA accessibility is rated explicitly for every tool, not assumed; payment method viability is stated
• Budget profiles reflect real UGX costs, not USD assumptions carried over without conversion
• Recommended stack is specific to the client's stated budget profile, channels, and technical comfort level — not a generic list
• At least 3 free or freemium options are included in every budget profile
• Africa's Talking is included as the primary EA-native automation tool in every recommendation
• Output is a reference the client can act on immediately — no vague "explore options" or "consider using" language
• The five-question evaluation framework is applied to every tool, not just listed as a principle

Related Skills

• playbook-ai-automation-workflow/SKILL.md — use for building automation sequences with the tools recommended here
• ai-use-case-mapping/SKILL.md — use before this audit to determine which marketing functions to prioritise for AI adoption
• meta-tools-stack-evaluation/SKILL.md — use when the client needs a broader martech stack evaluation beyond AI-specific tools
• 08-influencer-marketing-strategy/SKILL.md — use for manual EA influencer sourcing when paid influencer tools are deferred
• playbook-paid-social-advertising/SKILL.md — use for paid advertising strategy; this skill covers tool selection only

References

• Johnsen, M. (2024) AI in Digital Marketing. Mercury Learning — AI tool capability classifications and marketing automation frameworks
• Upadhyay, M. A. (2024) Generative AI for Marketing. Packt — generative AI application across content, SEO, analytics, and personalisation
• Chaffey, D. (2024) Digital Marketing: Strategy, Implementation and Practice — RACE framework for tool selection against marketing objectives
• Bodnar, K. and Cohen, J. (2012) The B2B Social Media Book — ROI framework for tool investment decisions: (TLV − COCA) ÷ COCA
• Uganda Data Protection and Privacy Act 2019 — applies to all tools processing customer personal data