AI Automation Consulting

Build AI workflows around the business, not the hype.

MASH helps founders and operations leaders identify where AI, rules-based automation and integrations can improve real work. We assess the current process, data quality, approvals and measurement before recommending a solution.

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Choosing the right level of automation

Not every workflow needs an autonomous agent. Use our AI agents versus workflow automation decision guide to compare fixed rules, bounded AI interpretation and adaptive planning before requesting a proposal. It includes a permission map and practical pilot evaluation questions.

What problem are you trying to solve?

Teams often adopt AI tools before defining the workflow. A support assistant without an approved knowledge base can invent answers; a sales agent connected to inconsistent CRM data can act on the wrong record. We start by tracing the process, finding its failure points and deciding whether AI is needed at all.

Typical triggers include missed enquiries, repetitive support requests, disconnected CRM records, manual reporting, inconsistent handoffs and growing demand without equivalent operational capacity.

From assessment to an accountable pilot

  1. Discover

    Map the trigger, inputs, owner, decisions, exceptions and expected outcome.

  2. Check readiness

    Review available data, system access, privacy constraints, consent, integration limits and required approvals.

  3. Design

    Choose between deterministic rules, retrieval-based assistance and generative AI; define where humans must approve.

  4. Test

    Run representative and failure-case tests before using production data or messaging customers.

  5. Measure

    Compare errors, time spent, customer experience and operational outcomes with the current process.

A pilot acceptance checklist before production

Before building, write down what would make one workflow ready to use. Agree the acceptance criteria with the person who owns the process; a working demonstration alone is not a release decision.

  1. Baseline: Record current handling time, manual corrections and missed handoffs for the same type of work you will test. Keep the sample period and exclusions visible.
  2. Test set: Include normal requests, incomplete records, duplicate events, unavailable integrations and questions the system should decline or escalate. Use approved sample data.
  3. Approval boundary: Specify which outputs may be drafted automatically and which customer messages, record changes or commercial commitments require a person to approve.
  4. Failure handling: Name the owner who receives failures. Check that retries do not create duplicate messages or records, and that unresolved work remains visible.
  5. Stop and rollback: Decide how to pause the workflow and resume the previous process. Test this before granting production write access.
  6. Release decision: Compare results with the agreed baseline, record remaining limitations and obtain the process owner’s acceptance. Expand only when the evidence supports it.

For example, a lead-routing pilot should demonstrate that an incomplete enquiry goes to a review queue, a duplicate event does not create a second lead, and an integration outage leaves a recoverable task. These are illustrative checks, not MASH client results or a promise of savings.

Use our AI automation ROI pilot template to plan measurement, or request a Growth & Automation Diagnostic to discuss the workflow and assessment scope. View starting prices and exclusions.

Comparing implementation partners? Use our AI automation RFP template and vendor scorecard to request consistent proposals and agree pilot acceptance tests before choosing a provider.

Business workflows worth evaluating

Lead operations

Enquiry intake, qualification assistance, routing and reminders with CRM visibility.

Customer support

Knowledge-based answers, ticket classification and clear escalation to people.

Internal operations

Document intake, approval routing, structured summaries and error-aware integrations.

GTM execution

Research support, content workflows and pipeline hygiene with human review.

Read more about customer support automation and founder GTM execution. The same principles apply across regions, subject to local laws and platform policies.

What a successful engagement should prove

A pilot should establish whether the workflow is accurate, safe, maintainable and genuinely useful. Track failed handoffs and customer complaints alongside completion rates. Do not assume a cost saving or revenue gain before measuring a baseline.

For a remote engagement, see global delivery. MASH does not promise a fixed integration timeline or a universal AI solution before assessing requirements.

Frequently asked questions

Can you work with teams globally?

Yes. Remote engagements are considered across markets, with practical working arrangements agreed during discovery.

Do we need to replace our existing software?

Not necessarily. We assess whether existing systems can be connected safely and reliably before recommending new tools.

Can a pilot start without a large AI platform?

Yes. A small rules-based workflow may be the right first step.

What about security and privacy?

Access, data handling, approvals, retention and local compliance requirements are part of scoping and testing.

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