Resource
AI Agent Use-Case Scorecard
AI Agent Use-Case Scorecard is most useful when teams choosing between ai agent ideas need repeatable work handled consistently without losing human control. DH79 maps the workflow, builds private AI agents, connects the right tools, sets approval gates and runs the system as a managed service. The starting point is not a generic AI demo. It is a narrow operational workflow such as research, follow-up, content, meeting preparation, CRM updates, admin or monitoring, launched carefully and improved each month.
Who this is for
- Founders with several automation candidates.
- Ops teams prioritising workflows.
- Agencies deciding where agents could help first.
The business problem
AI ideas are easy to generate and hard to prioritise. A scorecard helps compare value, frequency, repeatability, data readiness, risk and measurability. The important test is whether the work is frequent enough, valuable enough and controlled enough for an agent to help without hiding risk. DH79 starts with a narrow workflow because useful agents need clear inputs, clear outputs and a named human owner.
Example workflow
Score each use case from one to five across value, frequency, source quality, approval clarity, risk and ease of measuring success. Pilot the best safe candidate. The workflow is designed so the agent prepares, drafts, summarises or monitors, while a human remains responsible for approval where judgement, reputation, compliance or customer trust is involved.
What DH79 sets up
- Use-case scorecard
- Risk notes
- Tool access notes
- Pilot recommendation
What the AI agents can do
- Compare use cases
- Choose a pilot
- Document why
- Prepare next backlog
What tools they can connect to
- Gmail, Outlook and shared inboxes
- Google Workspace, Microsoft 365, Notion, Drive and SharePoint
- HubSpot, Pipedrive, Salesforce or lightweight CRM systems
- Slack, Teams, calendars, task tools and internal knowledge bases
- Website CMS, spreadsheets, forms and reporting dashboards where access is scoped
What stays human
- Scoring judgement
- Risk appetite
- Final selection
DH79 deliberately avoids promising fully autonomous business judgement. The safest commercial gains usually come from agents preparing the work, making gaps visible and giving humans better drafts, summaries and reminders.
First 30 days
- Score
- Select
- Build
- Review
Safety and GDPR-aware controls
- Do not let high value override high risk
- Keep humans on external outputs
- Check data access
- Review after launch
Pricing and scope
DH79's managed package starts from £5,000/month inside an agreed operating scope. Work that needs unusual volume, specialist integrations or regulated review is scoped before launch so costs and responsibilities are clear.
How to judge whether this should be your first agent
A good first agent is not the most exciting idea in the business. It is the workflow with clear inputs, repeatable steps, visible mistakes and a human owner who can approve the output. For ai agent use-case scorecard, DH79 looks for a task where the agent can compare use cases, connect only to gmail, outlook and shared inboxes, and leave scoring judgement with a person. That makes the pilot easier to measure and safer to improve.
- Bring two or three real examples of the current workflow, including a strong example and a messy edge case.
- Decide who owns approval, who receives the draft or summary, and what would count as a useful first-month result.
- Start with a draft, research, preparation, triage or monitoring task before allowing any agent to take external action.
FAQs
Can DH79 set up ai agent use-case scorecard without our team managing prompts?
Yes. DH79 maps the workflow, builds the agent instructions and private workspace, connects the agreed tools, sets approval rules, monitors usage and improves the system. Your team should understand the operating rules, but it should not have to manage tokens, hosting or prompt maintenance.
What should stay under human approval?
External messages, legal or financial commitments, sensitive client communication, medical or regulated judgement, unusual edge cases and anything that could affect reputation should remain human reviewed unless a narrower approval policy is agreed.
How quickly can the first workflow go live?
A narrow first workflow is normally designed during the first month. The first 30 days focus on workflow audit, data and tool access, agent build, controlled testing, team feedback and a decision on what to improve or add next.
How does DH79 reduce risk?
DH79 uses scoped permissions, least-privilege access, human approval gates, logs, draft-only modes for sensitive work, clear escalation rules and monthly review. The aim is useful operational leverage without handing important judgement to an unsupervised system.
Is this suitable for teams choosing between ai agent ideas?
It is most suitable when teams choosing between ai agent ideas have repeatable research, drafting, preparation, follow-up, admin or monitoring work and want a managed service rather than a DIY platform. If the first use case is too vague, DH79 starts by narrowing it into a controlled pilot.
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