Human-First Automation
AI & Automation Readiness Report
Prepared for Northbank Communications
Prepared by Nexolve
Your AI Readiness Blueprint
Where you are. Where the gaps are. Exactly where to start.
51/100
Readiness score
Established
Maturity tier
Prepared for
Northbank Communications
Date
28 July 2026
Report ID
NBC-2026-0728
Contents

What's inside

AI Readiness Audit — Northbank Communications

Part 1 Your Snapshot
The 5-minute version
01 Executive summary
02 Your agency profile
Part 2 Where You Stand
03 Where do I stand? — your eight dimensions
Part 3 Strategic Context
04 What is this worth?
05 EU AI Act & GDPR compliance
06 What does waiting cost?
07 How do I compare?
Part 4 What To Do
08 Your action list
09 What should I do first?
10 Quick wins — your first 30 days
11 Your sequence — the phased plan
Part 5 Intelligence & Appendices
12 Department scorecards
13 Recommended next steps
A Tool ecosystem analysis
B How do I know this is right? — methodology
C Template pack
D Implementation notes
E What you told us — your answers
F Making it stick — rollout & adoption
Final word

How to use this report: Start with the 5-minute version, then go to whichever question matters most to you today. Part 4 is the plan — it opens with your numbered action list, and every other part refers back to those numbers.

The 5-minute version

If you only read one page

Everything else in this report expands on what’s below. Read this, do the first move, come back for the rest when you’re ready.

51/100
Overall readiness
“Established” tier
69
Your strength: Tools & Stack
build on this
28
Your biggest gap: Measurement & ROI
close this first

Your first move

1

Publish a one-page AI usage policy

Cover approved tools, client-data handling rules and disclosure obligations on a single page. This is the unlock for everything else — the prompt library, the reporting automation and any client-facing AI output all depend on it existing first.

⏱ Half a day High impact

The numbers that matter

The full set of quick wins recovers an estimated 60.3 hrs/week across the agency — that is your 86.2 automatable hrs/week after a workflow-maturity reduction; the working is in Section 04.

Doing nothing costs roughly £13,568/month in unrecovered capacity.

Your one next step

Do the first move above this week — that’s it, everything else can wait until it’s done. Your Action Plan spreadsheet is a separate Google Sheet, linked in the email that delivered this report. When you want the full plan, go straight to Part 4: Roadmap & Recovery.

One
Part One
Your Snapshot

A high-level view of where your agency stands today — overall score, benchmark position, and the key findings that matter most.

The 5-minute version
01
Executive summary
02
Your agency profile
The takeaway

You scored 51/100 — the “Established” tier.

Strongest: Tools & Stack (69). Biggest gap: Measurement & ROI (28).

Section 01

Executive summary

The headline view: your overall score, the shape of your readiness across eight dimensions, and the findings that matter most.

51 out of 100
Established
StrategyGovernanceDataToolsAutomationPeopleMeasurementInnovation

Northbank Communications scored 51/100 on the Nexolve AI Readiness Index, placing the agency in the "Established" tier and slightly ahead of the 47 average for agencies of similar size. The stack is your strength: modern, API-capable and largely already paid for. The constraint is everything wrapped around it — no written AI policy, no measurement, and 60 recoverable hours a week still being done by hand. Close the governance gap first, because it unblocks everything else in this plan.

Strategy & Vision 58
Governance & Risk 32
Data & Privacy 48
Tools & Stack 69
Workflow Automation 39
People & Skills 52
Measurement & ROI 28
Innovation & Experimentation 44
★ Strength
Your tool stack is well ahead of peers and already includes unused AI capability you pay for.
! Priority Gap
No documented AI governance framework, despite daily AI use across four departments.
★ Strength
Leadership engagement is genuine and roughly 45% of the team uses AI weekly without being asked.
! Priority Gap
No measurement of AI value, which will block approval for later-phase investment.
★ Strength
Client Services has documented its processes, which makes them immediately automatable.
! Priority Gap
Client data fragmented across five platforms, blocking the highest-value automations.
Estimated annual hours recoverable
Through automation of identified manual workflows · 60.3 hrs/week (86.2 automatable, after the workflow-maturity multiplier — Section 04) × 48 working weeks
2,894 hrs
Section 02

Your agency profile

The information you provided, reflected back. Spot an error? Reply to your delivery email (or write to hello@nexolve.co.uk) within 7 days and we will regenerate your report on the corrected answers, free. Appendix E plays back every questionnaire answer.

Agency name
Northbank Communications
Team size
31–50 people
Agency type
Full-Service
Primary services
Public relations & reputation management, Integrated brand campaigns, Media planning & buying, Content & social
Average retainer
£8k–£20k / month
Website
https://northbank.example
CRM & sales
HubSpot
Project management
Asana, Monday.com
Content & creative
Canva, Figma, Adobe Creative Suite
Media monitoring & PR
Cision, Meltwater, CoverageBook
AI tools in use (as you reported)
ChatGPT Team, Claude Pro, Canva Magic Studio

Current AI activity

~45%
of your team
use AI tools regularly
3
AI tools
currently in use
0
documented AI policies
Two
Part Two
Where You Stand

Your score on each of the eight scored dimensions — what the data shows, where the gaps are, and what to do about each one.

03
Where do I stand? — your eight dimensions
The takeaway

Your biggest gap is Measurement & ROI (28) — this part explains why, and what to do about it first.

Just want the plan? Skip ahead to Part 4 — the roadmap stands on its own.

Section 03

Where do I stand?

Your score across all eight dimensions, with the weight each carries. The dashed marker shows the score you can realistically reach by completing the roadmap.

Strategy & Vision
20%
58
→ 79
Governance & Risk
10%
32
→ 57
Data & Privacy
10%
48
→ 69
Tools & Stack
20%
69
→ 85
Workflow Automation
10%
39
→ 63
People & Skills
15%
52
→ 74
Measurement & ROI
10%
28
→ 54
Innovation & Experimentation
5%
44
→ 67
Ad-hoc 0–20 Emerging 21–35 Developing 36–50 Established 51–70 Integrated 71–85 Optimised 86–100 Achievable target

Each target’s “via R…” note names the numbered actions that move that score. Every R-number is stated in full in your action list (Section 08).

1. Strategy & Vision

Weight: 20% of overall score
58
Established
→ Target: 79 via R13, R14

Leadership is engaged and AI is discussed at board level, but intent has not yet been converted into a written plan with owners and milestones. That gap is what separates your 58 from the 80 this dimension could reach.

✓
Leadership buy-in is genuine — AI is a standing board agenda item, not a side project.
✓
Two partners are actively sponsoring AI experiments within their teams.
✗
The AI section in the strategy deck is aspirational — no KPIs or milestones — so priorities shift between quarters.
✗
No named owner for AI adoption, which leaves progress dependent on individual enthusiasm.
Agencies that formalise an AI owner and a written plan move roughly two maturity tiers faster than those relying on distributed enthusiasm.

→ Where to start

  1. Name one accountable AI owner this month.
  2. Write a one-page strategy covering the next two quarters.
  3. Add a standing AI item to the monthly leadership meeting.
What “good” looks like: A named owner, a two-year roadmap reviewed quarterly, and AI capability referenced in new-business credentials.

2. Governance & Risk

Weight: 10% of overall score
32
Emerging
→ Target: 57 via R1, R12

This is your lowest score of the eight and your most urgent. ChatGPT and Claude are already running across client accounts without a written policy, which means every team member is making their own judgement call about what client data can go in.

✗
No documented AI usage policy, despite daily AI use across four departments.
✗
No DPAs in place with AI vendors processing client data.
✗
No incident escalation path when an AI output is wrong or exposes data.
✓
Access to client data is already restricted on a need-to-know basis.
Four departments are already using AI daily with no written policy, so the gap here is not awareness — it is that nothing has been written down yet.

→ Where to start

  1. Publish the one-page AI usage policy — half a day of work.
  2. Audit vendor DPAs against your GDPR obligations.
  3. Define who is called when an AI output causes a problem.
What “good” looks like: A published policy every employee has read, signed DPAs with every AI vendor, and a rehearsed incident path.

3. Data & Privacy

Weight: 10% of overall score
48
Developing
→ Target: 69 via R4, R9

Your data hygiene fundamentals are sound, but client data lives across five disconnected platforms. That fragmentation is the single biggest blocker to the highest-value automation opportunities in your roadmap.

✓
A PII inventory is maintained and kept current.
✓
Vendor security posture is reviewed before adoption.
✗
Client data is fragmented across five platforms with no unified layer.
—
Data residency for AI tools is only partly documented.
Data fragmentation is the most common reason agency automation projects stall after the first quick win.

→ Where to start

  1. Document where each client data type actually lives.
  2. Confirm the data residency of every AI tool in use.
  3. Scope the unified data layer (one place the numbers land, so every report draws on the same figures) before phase 4 begins.
What “good” looks like: One queryable data layer, documented residency for every tool, and classification driving what AI may touch.

4. Tools & Stack

Weight: 20% of overall score
69
Established
→ Target: 85 via R8, R11, R15

This is your strongest dimension and a genuine asset. The stack is modern and API-capable — the opportunity is not buying more tools but switching on and connecting what you already pay for.

✓
Every core platform exposes an API (the connection that lets one tool send data to another without anyone copying it across), so automation needs no new procurement.
✓
HubSpot, Slack and Google Workspace all ship AI features already included in your plans.
✗
Cision and Meltwater duplicate four capabilities at roughly £14,400/yr of overlap.
✗
No integration layer (the piece that sits between your tools and passes data along) connects the stack, so data moves by hand.
Most agencies at your tier already own the capability they need; the constraint is activation, not licensing.

→ Where to start

  1. Enable the AI features already included in HubSpot and Google Workspace.
  2. Decide between Cision and Meltwater.
  3. Introduce an integration layer before phase 3.
What “good” looks like: A deduplicated stack where every tool has a defined role and an automation layer connecting them.

5. Workflow Automation

Weight: 10% of overall score
39
Developing
→ Target: 63 via R2, R3

Processes are understood but almost entirely manual. With 169 hours a week across the tracked task areas and 86.2 of those automatable, this dimension carries the largest single financial opportunity in the report.

✗
Reporting consumes 34 hrs/week and is almost entirely manual.
✗
Media monitoring consumes 20 hrs/week of feed-scanning that webhooks (an automatic message one tool sends another the moment something happens) could replace.
✓
Client Services has documented its core processes, which makes them automatable.
—
Two project management tools split process documentation across systems.
Reporting and monitoring are consistently the two highest-recovery task areas for PR-led agencies.

→ Where to start

  1. Automate the media monitoring digest first.
  2. Template the monthly coverage report and let AI draft commentary.
  3. Standardise on one project management tool.
What “good” looks like: Reporting and monitoring running on a schedule, with humans reviewing rather than assembling.

6. People & Skills

Weight: 15% of overall score
52
Established
→ Target: 74 via R6, R7

Appetite is high and roughly 45% of the team already uses AI weekly, but usage is self-taught and inconsistent. Without shared standards, output quality varies by individual rather than by intent.

✓
Around 45% of the team uses AI tools weekly without being asked to.
✓
Content & Editorial has effectively self-trained and is ahead of the agency average.
✗
No structured training, so quality depends on who happens to be writing the prompt.
✗
No shared prompt library, so effort is duplicated daily.
Structured training is the fastest lever on this dimension — monthly 30-minute sessions outperform one-off workshops.

→ Where to start

  1. Build the shared prompt library this week.
  2. Run monthly 30-minute drop-ins.
  3. Have Content & Editorial mentor New Business.
What “good” looks like: A living prompt library, a regular training rhythm, and consistent output quality across departments.

7. Measurement & ROI

Weight: 10% of overall score
28
Emerging
→ Target: 54 via R5, R10

This is your lowest score and it constrains everything else. Without evidence of what AI is returning, phase 3 and phase 4 investment will be difficult to justify internally, however sound the plan is.

✗
No tracking of hours saved or quality improvements from AI-assisted work.
✗
No AI benefits register, so wins are anecdotal rather than auditable.
✗
AI capability is not referenced in credentials or new-business pitches.
—
Time tracking exists and could feed a dashboard with modest effort.
Agencies that measure AI value convert it into commercial positioning; those that do not tend to stall after the quick wins.

→ Where to start

  1. Quantify the cost of current manual workflows.
  2. Build a simple hours-saved dashboard.
  3. Start a quarterly benefits register.
What “good” looks like: A dashboard leadership actually reads, and an AI value story you can take into a pitch.

8. Innovation & Experimentation

Weight: 5% of overall score
44
Developing
→ Target: 67 via R16

Experimentation happens, but informally and without a way to capture what was learned. Good ideas surface and then evaporate when the person who tried them moves on to the next deadline.

✓
Individual team members trial new tools on their own initiative.
✗
No structured experiment process, so learning is not retained.
✗
No budget allocated for testing new capability.
A small ring-fenced experiment budget with a monthly review is the lowest-cost way to move this dimension.

→ Where to start

  1. Ring-fence a small monthly experiment budget.
  2. Log every experiment and its outcome in one place.
  3. Review results at the monthly AI steering meeting.
What “good” looks like: A monthly experiment cycle with recorded outcomes that feed the roadmap.
Three
Part Three
Strategic Context

Financial modelling, compliance posture, and the commercial levers available to your agency right now.

04
What is this worth?
05
EU AI Act & GDPR compliance
06
What does waiting cost?
07
How do I compare?
The takeaway

Waiting has a price: roughly £13,568/month in unrecovered capacity.

Section 04

What is this worth?

What automating the identified tasks is worth to you in hours and pounds, modelled at a blended rate (the average hourly cost of the people doing the work, senior and junior together) of £50–£100/hr (reported).

As a monthly retainer agency, time savings can be re-deployed into margin improvement or capacity expansion.

60.3hrs
Recoverable per week
3k hrs
Hours freed per year
60.3 hrs × 48 working weeks
£163k
Direct cost saving
range £130k–£195k
Recoverable hours: 86.2 hrs/wk raw, 60.3 hrs/wk after the workflow-maturity multiplier
Annual value: 60.3 hrs × £75/hr × 48 working weeks × 0.75 (a 25% conservative reduction) = £162,810/year (estimate range £130,248–£195,372)

Task automation breakdown

Each row’s Annual figure is that task’s share of the £163k headline — the workflow-maturity multiplier and the 25% conservative reduction are already applied, so multiplying one row’s hours by the rate and 48 weeks will give a higher, un-reduced number.

Task Current Automatable % of recoverable Annual
Client reporting & analytics
Automated data pulls + AI-drafted commentary
34h 23.8h 28% £45k/yr
Content creation & first drafts
Prompt library + AI first-draft workflow
30h 13.5h 16% £25k/yr
Meetings & follow-ups
AI transcription and action-point extraction
26h 3.9h 5% £7k/yr
Client communications
Templated AI drafting with human review
22h 8.8h 10% £17k/yr
Media monitoring
Webhook-fed AI digest replacing manual scanning
20h 15h 17% £28k/yr
Admin & operations
Workflow automation across PM and finance tools
16h 8h 9% £15k/yr
Data collection & cleaning
Scheduled API integrations into one data layer
12h 7.8h 9% £15k/yr
Competitor research
AI-assisted landscape briefs on a weekly cadence
9h 5.4h 6% £10k/yr
Total
86.2h raw → 60.3h after the workflow-maturity multiplier — the formula above
169h 86.2h — £163k/yr

Best, likely and worst case

The £163k headline is a run-rate — what a full year looks like once the changes are embedded. These scenarios move the two things the model is least certain about: which end of the published estimate range holds, and how quickly the Section 11 sequence lands. First-12-months figures assume value ramps linearly from zero to the run-rate; the ramp assumptions are listed with the rest of the model in Appendix B.

Scenario Run-rate value Full run-rate by First 12 months
Conservative
The Section 11 sequence slips by half (run-rate at month 18) and recovery runs at the low end of the published estimate range.
£130k/yr Month 18 £43k
Expected (the published figure)
The Section 11 sequence lands on schedule (run-rate at month 12) at the published central estimate.
£163k/yr Month 12 £81k
Upside
Quick wins land in the first quarter, the sequence completes early (month 9), and the high end of the estimate range holds.
£195k/yr Month 9 £122k

If you re-deploy the recovered hours

A scenario, not a forecast. The £163k direct saving above is what the audit supports on its own; the figures below assume 60% of the freed hours are re-sold as billable work.

Direct cost savings (time x blended rate) + £163k
Billable hour uplift (60% re-deployment) + £98k
AI tooling costs (est. core stack for your size)
A size-band estimate — AI assistant seats plus 2–3 workflow tools at 2026 prices, not a quote for named tools. Your recommended stack is in Appendix A.
− £14k
Plan upgrades recommended in Appendix A − £17k
Net annual financial benefit (after plan upgrades) £230k
741%
ROI
2mo
Tooling payback
licence cost only — not the implementation timeline

Net benefit is stated after the £16,560/yr (£1,380/mo) of plan upgrades recommended in Appendix A, on top of the core-stack tooling budget. Those upgrades are not a contradiction of the “activate what you already pay for” finding: they are a phase-4 dependency, bought for the API access the integration work needs, not to unlock features you already own.

Payback above is how long the recovered hours take to cover that tooling spend — licences only. It is not the implementation timeline: the work itself runs across the phases in Section 11, Your sequence, and is your team’s time, which this model does not price.

What this model assumes

Blended rate: £50–£100/hr (reported) — modelled at £75/hr, the midpoint of the band you reported (at £50/hr the direct saving is £109k/yr; at £100/hr, £217k/yr) · Recoverable: 60.3 hrs/week · All figures are conservative estimates, which is why the headline saving is shown as a range. Every assumption behind these numbers — recovery rates, working weeks, the 25% reduction — is listed in full in Appendix B. The direct saving and billable uplift scale with the hours you reported — if your team's real week on these tasks is a fifth higher than your estimate, so are they; the tooling costs stay fixed.

↪ You reported 169 hrs/week across the eight tracked task areas.

↪ Your Workflow Automation score of 39/100 sets the ×0.7 maturity multiplier.

Section 05

EU AI Act & GDPR compliance

Your self-reported governance and data privacy posture. Not a legal assessment — a map of where action may be needed under GDPR, the EU AI Act, and ISO/IEC 27001.

38%
Checks passed
Gaps identified

3 of 8 governance checks confirmed. 5 items require attention before client-facing AI deployments at scale. The Data governance depth items further down are graded on the 0–5 scale and sit outside this count.

✓ Yes
PII inventory is maintained and up to date
GDPR Art. 30
✓ Yes
Client data access restricted to need-to-know basis
GDPR / ISO 27001
● Not sure
Data Processing Agreements (DPAs) in place with AI vendors
GDPR Art. 28 Using AI tools that process personal data without a DPA puts your agency in breach of GDPR Art. 28.
✗ Not yet
Clear process for escalating AI-related incidents
EU AI Act / GDPR Without escalation procedures, AI errors and data incidents risk going unreported — a regulatory liability.
● Not sure
Team aware of EU AI Act obligations for your use cases
EU AI Act GPAI model providers and deployers of AI in regulated categories have documentation/risk obligations from 2025.
✓ Yes
Security posture of AI vendors reviewed before adoption
ISO 27001 / SOC 2
● Not sure
Data residency location of AI tools is documented
GDPR Ch. V Data transferred outside the EEA without adequate safeguards violates GDPR Chapter V restrictions.
✗ Not yet
Clients informed of — and agreed to — AI use on their accounts
GDPR Art. 28 Feeding client data into AI tools is a processing activity clients must be told about — undisclosed AI use risks contractual breach and client trust.

Data governance depth

The L number is the maturity level you answered on our 0–5 scale, from L0 (nothing in place) to L5 (running and reviewed). L4 and above is counted as in place.

L2 — Partial
DPIA conducted for AI tools handling personal data
GDPR Art. 35
L3 — Partial
Client and internal data classified by sensitivity
ISO 27001 A.5.12

Important: This compliance check is based on your self-reported answers. It is not legal advice. We recommend consulting a data protection officer or legal counsel to validate your GDPR and EU AI Act obligations.

Section 06

What does waiting cost?

This report shows what you gain by acting. This section shows what you lose by waiting. Every month without action carries the same cost again.

£13,568
Lost per month in unrecovered capacity every month. That is 60.3 hours a week of work your team is doing by hand that the tools you already own could absorb.
18 months
Competitive window. is the head start competitors gain. Agencies acting now will have automated reporting and AI-enhanced service lines live within two quarters.
261 hrs
Capacity on the table, per month. worth of additional client capacity sitting unused inside manual workflows, at your current average retainer value.
↑ Risk
Risk level: High. Governance exposure compounds daily. With AI already live across four departments and no written policy, every week increases the likelihood of an incident you cannot explain to a client.

The competitive window is an illustration of pace, not market intelligence: it is how far ahead a similar agency starting this report’s roadmap today would be by the time you begin — on the same phase timings as Section 11, Your sequence.

The cumulative effect

These costs recur every month you wait. Over 6 months that is £81,408 in unrecovered capacity. Over 12 months, £162,816 — the monthly figure repeated, not compounded. Meanwhile, agencies that act now will have established AI workflows, trained teams, and measurable efficiency gains — widening the gap with every passing month. The question isn't whether to adopt AI, but how much the delay will cost you.

↪ Based on your audit: Derived from 60.3 recoverable hrs/week at your reported blended rate.

Section 07

How do I compare?

How your readiness compares to agencies of similar size, revenue and service mix, drawn from the Nexolve benchmark dataset for 31–50 person agencies.

Your dimension-by-dimension comparison

How each of your scores compares to the average for agencies of similar size.

DimensionYour ScorePeer AverageGapPosition
Strategy & Vision 58 52 +6 Ahead of peers
Governance & Risk 32 35 -3 In line
Data & Privacy 48 42 +6 Ahead of peers
Tools & Stack 69 56 +13 Ahead of peers
Workflow Automation 39 46 -7 Behind peers
People & Skills 52 50 +2 In line
Measurement & ROI 28 54 -26 Well behind
Innovation & Experimentation 44 40 +4 In line
How a big dimension gap becomes a small overall gap: the overall gap of +2 is each dimension gap above × its weight (Appendix B), summed. Largest terms: Tools & Stack +13 × 20% = +2.6 · Measurement & ROI −26 × 10% = −2.6 · Strategy & Vision +6 × 20% = +1.2 · the remaining 5 sum to +0.1. Total +1.3 — the engine carries unrounded scores, so this sum can sit a fraction from the +2 shown above.

↪ Based on your audit: Benchmarked against agencies in your size band (31-50 people) from the Nexolve sector peer set, April 2026. Each row is that band's average for the dimension; the cohort (the group of comparable agencies your scores are measured against) average uses the same dimension weights as your own score.

Peer averages are modelled from Nexolve's benchmark dataset for your size band; positions are directional, not percentiles.

0–20
Ad-hoc
8% of agencies
21–35
Emerging
22% of agencies
36–50
Developing
30% of agencies
↓ Avg: 49
51–70
Established
26% of agencies
↓ You: 51
71–85
Integrated
10% of agencies
86+
Optimised
4% of agencies

26% of agencies share your Established tier — 60% sit in the tiers below yours, 14% in the tiers above.

What separates you from the top quartile

Agencies scoring 70+ share four capabilities you can realistically build within this roadmap.

AI content generation at scale

Top competitors deploy AI for all first-draft content, cutting production time by around 60%.

Real-time media monitoring

AI-powered sentiment and coverage tracking across 10,000+ sources, delivered as a morning brief.

Predictive pitch targeting

AI identifies the best journalist matches for a story, measurably improving open rates.

Automated client reporting

Dashboards refresh automatically, reducing reporting turnaround to near-zero.

Your Tools & Stack score of 69 is well ahead of the peer average of 53 — you already own most of the capability required. The work is activation, not acquisition.

Where Northbank Communications sits: at a readiness score of 51, you are in line with the average of 49 for agencies your size (Nexolve benchmark data, April 2026). The gap between agencies acting on AI strategically and those still experimenting is widening.

Four
Part Four
What To Do

Exactly what to do, in what order. It opens with your numbered action list — every recommendation in this report, stated once — then what to do first, the quick wins, and the phased sequence.

08
Your action list
09
What should I do first?
10
Quick wins — your first 30 days
11
Your sequence — the phased plan
The takeaway

Completing this list recovers an estimated 60.3 hrs/week in total.

Start with: Publish a one-page AI usage policy.

Section 08

Your action list

Everything this report recommends, in one list. There are 16 distinct actions — each one is stated in full once, and referred to by its number everywhere else.

# Action Effort Impact Dimension Phase
R1 Publish and socialise a one-page AI usage policy across the agency
Quick wins · Priority matrix · Your sequence
Low
Half a day
High Governance & Risk 1
R2 Automate daily media monitoring digests with AI summarisation
Quick wins · Priority matrix · Your sequence
Medium
1 day
High Workflow Automation 2
R3 Automate first-draft client reports using AI templates
Priority matrix · Your sequence
Medium High Workflow Automation 2
R4 Create reusable client AI consent and data-handling templates
Quick wins · Priority matrix
Low
2–3 hours
High Data & Privacy 1
R5 Build an AI ROI tracking dashboard
Priority matrix · Your sequence
Medium High Measurement & ROI 3
R6 Build a shared prompt library and AI writing style guide
Quick wins · Priority matrix
Low
Half a day
Medium People & Skills 1
R7 Launch monthly AI training drop-ins for all staff
Priority matrix
Low Medium People & Skills 2
R8 Build a RAG knowledge base for credentials and case studies
Priority matrix · Your sequence
High High Tools & Stack 4
R9 Integrate client analytics into a unified reporting data layer
Priority matrix · Your sequence
High Medium Data & Privacy 4
R10 Quantify manual workflow cost for the investment case
Quick wins
Low
2–3 hours
Medium Measurement & ROI —
R11 Switch on the AI features you already pay for
Quick wins · Your sequence
Low
2 hours
High Tools & Stack 2
R12 Complete vendor DPA and data-residency audit
Your sequence
Low High Governance & Risk 1
R13 Appoint an AI champion per function
Your sequence
Low Medium Strategy & Vision 1
R14 Develop a two-year AI capability roadmap
Your sequence
Medium Medium Strategy & Vision 3
R15 Consolidate Cision and Meltwater onto one platform
Your sequence
Medium Medium Tools & Stack 3
R16 Package AI capability as a client offering
Your sequence
High Medium Innovation & Experimentation 4

How to use these numbers. They are stable references, not a ranking of effort or a schedule — the order follows priority, and the Phase column tells you when each one starts. Use them to assign owners: “who has R3?” means the same thing in this report, in your Action Plan spreadsheet, and in a conversation with your team. Impact and effort are bands, not scores: actions ranked on the priority matrix are banded from their computed impact, and actions that sit only in your sequence are banded from the priority the sequence gives them. Effort throughout this report is hands-on working time — approval, legal review and sign-off cycles add elapsed days on top, so a “half a day” action may take a fortnight to fully land.

Section 09

What should I do first?

Your AI and automation opportunities, mapped by impact versus effort. Each dot is one action from your action list — dot 3 is R3.

↑ Higher impact
Higher effort →
★ Quick winsHigh impact · low effort
Strategic buildsHigh impact · higher effort
Low priorityLower impact · low effort
Consider laterLower impact · higher effort
1
2
3
4
5
6
7
8
9
R1 Publish and socialise a one-page AI usage policy across the agency
↪ Your Governance & Risk score is 32/100.
R2 Automate daily media monitoring digests with AI summarisation
↪ You reported ~20 hrs/week on Media monitoring.
R3 Automate first-draft client reports using AI templates
↪ You reported ~34 hrs/week on Reporting & analytics.
R4 Create reusable client AI consent and data-handling templates
R5 Build an AI ROI tracking dashboard
↪ Your Measurement & ROI score is 28/100.
R6 Build a shared prompt library and AI writing style guide
R7 Launch monthly AI training drop-ins for all staff
R8 Build a RAG (a setup where the AI answers from your own documents instead of from general knowledge) knowledge base for credentials and case studies
R9 Integrate client analytics into a unified reporting data layer

How to read this: violet dots are your recommended starting points; dark dots are your next phase. Effort, impact and phase for every one of these are in your action list. A quick win can also be a prerequisite — some low-effort actions here (a policy, a named owner) unblock the rest, which is why the phase order in Your sequence is the dependency order, not this chart’s.

Section 10

Quick wins — your first 30 days

Actions you can take this month with minimal investment. Each one is scoped to land inside a month and requires no new tools or infrastructure. Lines marked ↪ show the audit answer each recommendation is based on.

R1

Publish a one-page AI usage policy

Cover approved tools, client-data handling rules and disclosure obligations on a single page. This is the unlock for everything else — the prompt library, the reporting automation and any client-facing AI output all depend on it existing first.

↪ Based on your audit: Your Governance & Risk score is 32/100.

⏱ Half a day No new tooling cost High impact — Governance & Risk
R2

Automate the daily media monitoring digest

Your media team spends 20 hours a week scanning feeds. Route alert output into a single AI-assembled morning brief covering coverage hits, sentiment shifts and stories to watch. One day of configuration replaces a daily manual grind.

↪ Based on your audit: You reported ~20 hrs/week on Media monitoring.

⏱ 1 day No new tooling cost · technical build — see Appendix D High impact — Workflow Automation
R6

Build a shared prompt library

Curate 15–20 tested prompts for press releases, pitch angles and boilerplate rewrites, paired with a one-page style guide. Store it somewhere the whole team can reach by the end of the day.

↪ Based on your audit: Your People & Skills score of 52/100 leaves clear headroom.

⏱ Half a day No new tooling cost Medium impact — People & Skills
R4

Create client AI consent and data-handling templates

A short disclosure paragraph and a data-processing addendum, drafted once and inserted into new contracts immediately. This protects both you and your clients, and is a prerequisite before any AI-generated output goes to a client audience.

↪ Based on your audit: You answered "No" to clients having agreed to AI use on their accounts.

⏱ 2–3 hours No new tooling cost High impact — Data & Privacy
R10

Quantify manual workflow cost for the investment case

You have 86.2 automatable hours a week sitting in manual workflows — 60.3 of them recoverable after the workflow-maturity adjustment. A one-page cost-of-inaction summary gives leadership a concrete financial frame for approving phase 2 and phase 3 spend.

↪ Based on your audit: Your Measurement & ROI score is 28/100.

⏱ 2–3 hours No new tooling cost Medium impact — Measurement & ROI
R11

Switch on the AI features you already pay for

HubSpot, Slack and Google Workspace all ship AI capability included in your current plans — the content assistant, Workflow Builder and Gemini in Docs. Nobody has enabled them. This is pure upside with no procurement and no new spend.

↪ Based on your audit: Your Tools & Stack score of 69/100 is your strongest dimension.

⏱ 2 hours No new tooling cost High impact — Tools & Stack
What this unlocks

The automation opportunities identified across this report recover an estimated 60.3 hours per week — worth £4,523/week at your blended rate of £75/hour (from the £50–£100/hr band you reported). That is the direct-saving basis behind Section 04’s headline — the re-deployment scenario there is modelled on top of it, not in addition. The quick wins above are the first step toward it, and need no new tools or budget.

Section 11

Your sequence

Sequenced by dependency, not by deadline. Move at your own pace — but follow the order. Each phase unlocks the next.

51
NOW
→
60
Phase 1–2
→
69
Phase 3
→
78
Phase 4
Phase 1 Foundation Month 1–2
Gate: Strategy doc + AI policy approved
Est. budget: £900–£1.8k your team's existing hours, not new spend — no new licences needed
P1
R1 Publish the AI usage policy
Governance & Risk
Low
P1
R12 Complete vendor DPA and data-residency audit
Review DPAs for every AI vendor against GDPR obligations and log findings in a reusable checklist. · Governance & Risk
Low
P2
R13 Appoint an AI champion per function
Designate one champion per major function and form a steering group that meets monthly. · Strategy & Vision
Low
Phase 2 Quick Wins Month 2–4
Gate: 3+ quick wins live
Est. budget: £2.1k–£4.2k your team's existing hours, not new spend — no new licences needed
P1
R2 Automate the daily media monitoring digest
Workflow Automation
Medium
P1
R3 Automate monthly client coverage reporting
Workflow Automation
Medium
P2
R11 Audit and enable HubSpot built-in AI features
Tools & Stack
Low
Phase 3 Strategic Build Month 4–8
Gate: Data centralised + 2 automations live
Est. budget: £2.7k–£5.4k your team's existing hours, not new spend
P2
Develop a two-year AI capability roadmap
A structured plan covering skill development, tool investment and quarterly milestones, presented at each board cycle. · Strategy & Vision
Medium
P1
R5 Build an AI ROI tracking dashboard
Measurement & ROI
Medium
P2
Consolidate Cision and Meltwater onto one platform
The two share three core capabilities. Evaluate which better serves PR and crisis comms, then redirect the saved licence cost into AI tooling. · Tools & Stack
Medium
Phase 4 Scale & Optimise Month 8–12
Gate: Team trained + processes documented
Est. budget: £5.4k–£13.5k your team's existing hours, not new spend
P3
R8 Build a RAG knowledge base for credentials and case studies
Tools & Stack
High
P3
R9 Integrate client analytics into a unified data layer
Data & Privacy
High
P3
Package AI capability as a client offering
Convert proven internal workflows into a billable service line with its own credentials and pricing. · Innovation & Experimentation
High

Timing is indicative. Phase 1 can start immediately with no new spend — its budget band above is your team’s existing hours priced for planning, not money out the door. Phase 2 timing depends on your approval and procurement cycles. Phases 3 and 4 expand as the early phases prove their return. Your personalised Action Plan — a separate Google Sheet, linked in your delivery email — contains week-by-week tasks for Phases 1 and 2.

Five
Part Five
Intelligence & Appendices

Department scorecards, tool ecosystem analysis, the full scoring methodology, and the template pack.

12
Department scorecards
13
Recommended next steps
A
Tool ecosystem analysis
B
How do I know this is right? — methodology
C
Template pack
D
Implementation notes
E
What you told us — your answers
F
Making it stick — rollout & adoption
Final word
The takeaway

Reference material — dip in as needed. Nothing here is required reading before you act.

Section 12

Department scorecards

AI readiness isn’t uniform across your agency. Each department has different strengths, gaps, and starting points.

• Strong — ready to scale • Developing — needs support • Gap — action required

Ratings come from what you told us about each department in the questionnaire, not from an external audit. Strong means this department can lead the rollout of what already works; Developing means it needs a named owner and support before scaling; Gap means start with the basics here first.

PR & Media Relations

Developing
•
AI Tool Usage
•
Team Willingness
•
Process Docs
•
Quality Controls
Phase 1–2 starting points: R1 Publish and socialise a one-page AI usage policy across the agency · R7 Launch monthly AI training drop-ins for all staff — picked from Section 08, your action list, for this team’s weakest lights: Process Docs, AI Tool Usage, Quality Controls.

Content & Editorial

Ahead
•
AI Tool Usage
•
Team Willingness
•
Process Docs
•
Quality Controls
Phase 1–2 starting points: R1 Publish and socialise a one-page AI usage policy across the agency — picked from Section 08, your action list, for this team’s weakest lights: Process Docs, Quality Controls.

Client Services

Developing
•
AI Tool Usage
•
Team Willingness
•
Process Docs
•
Quality Controls
Phase 1–2 starting points: R1 Publish and socialise a one-page AI usage policy across the agency · R7 Launch monthly AI training drop-ins for all staff · R13 Appoint an AI champion per function — picked from Section 08, your action list, for this team’s weakest lights: Process Docs, AI Tool Usage, Team Willingness.

Media Planning & Buying

Developing
•
AI Tool Usage
•
Team Willingness
•
Process Docs
•
Quality Controls
Phase 1–2 starting points: R7 Launch monthly AI training drop-ins for all staff · R1 Publish and socialise a one-page AI usage policy across the agency · R13 Appoint an AI champion per function — picked from Section 08, your action list, for this team’s weakest lights: AI Tool Usage, Quality Controls, Team Willingness.

New Business

Behind
•
AI Tool Usage
•
Team Willingness
•
Process Docs
•
Quality Controls
Phase 1–2 starting points: R1 Publish and socialise a one-page AI usage policy across the agency · R7 Launch monthly AI training drop-ins for all staff — picked from Section 08, your action list, for this team’s weakest lights: Process Docs, AI Tool Usage, Quality Controls.

Department opportunity map

DepartmentOpportunityImpactEffort
PR & Media Relations AI media monitoring and sentiment analysis High Low
Content & Editorial AI-assisted first-draft generation with a shared prompt library High Low
Client Services Automated coverage reporting with AI-drafted commentary High Medium
Media Planning & Buying Automated performance data consolidation across platforms Medium Medium
New Business RAG-powered credentials and case-study retrieval for pitches High High
Section 13

Recommended next steps

Based on everything in this report, here are your three highest-priority actions. Do these first.

1

Approve and publish the AI policy

One page, leadership sign-off, shared with the whole team. Everything else depends on it.

2

Pick one automation and ship it

The media monitoring digest is the highest-value, lowest-risk starting point at 20 hours a week.

3

Put ROI tracking in place

Measurement & ROI is your weakest dimension at 28/100. Without evidence, phase 3 investment never gets approved.

Who does the work?

This report is built to be handed over — Northbank Communications does not need Nexolve to deliver it.

Included with this report
Action Plan Spreadsheet
Your personalised Google Sheet action plan with scored priorities, timelines, and progress tracking. A separate deliverable — the link is in the email that delivered this report, not in this PDF.
A contractor, for the builds
Actions marked “Build required”
For the technical builds, the Appendix D card for that R-number is the brief: the systems involved, where it lands in your stack, and the first step. Budget roughly £2–5k for a typical integration if you contract it out.
Appendix A

Tool ecosystem analysis

A complete audit of every tool in your stack — where there’s overlap, where there’s waste, and where API and integration capabilities unlock automations.

Your AI tools — what we can verify

The tools you told us your team uses, judged only against our verified tools database — where we cannot verify a tool, we say so rather than guess.

Not yet assessed
ChatGPT Team
Not yet verified against our tools database, so this report makes no claim about its data-handling posture or plan fit. Until it is assessed, treat the vendor’s own claims with the scrutiny your client data deserves — the vendor checks in Appendix C’s policy template apply here first.
Not yet assessed
Claude Pro
Not yet verified against our tools database, so this report makes no claim about its data-handling posture or plan fit. Until it is assessed, treat the vendor’s own claims with the scrutiny your client data deserves — the vendor checks in Appendix C’s policy template apply here first.
Verified
Canva Magic Studio (verified as Canva) — Design
General-purpose AI tooling. Has an API, so it can join the automations this report recommends.

Your current tool inventory

ToolCategoryCapabilitiesAPIWebhooksMCP
HubSpot CRM & Marketing 5 ● ● ●
Cision Media Intelligence 4 ● ● ○
Meltwater Media Intelligence 4 ● ● ○
Asana Project Management 3 ● ● ○
Monday.com Project Management 4 ● ● ○
Canva Design 3 ● ○ ○
Slack Communication 3 ● ● ●
Google Workspace Productivity 4 ● ● ●
Google Analytics Analytics & BI 3 ● ● ○
SEMrush SEO & Paid 3 ● ● ○

● Available   ○ Not available

Capabilities — how many distinct capability areas (reporting, workflow automation, data collection…) the tool covers in our tools database; the overlap alerts below compare tools on these. API — the tool can send and receive data automatically, so nothing is copied across by hand. Webhooks — it can tell your other tools the moment something happens. MCP — an AI assistant can operate it directly.

Tool overlap alerts

Tools with 3+ shared capabilities — consolidation could reduce costs and complexity.

Overlap
Cision & Meltwater — overlap on: media monitoring, sentiment analysis, coverage reporting, journalist database
Both platforms cover the same four core capabilities. Evaluate which better serves your PR and crisis comms needs, then consolidate to one. Potential saving: £14,400/yr
↪ Both tools appear in your reported stack with 4 shared capabilities.
Overlap
Asana & Monday.com — overlap on: task management, project timelines, workload view
Two project management tools in parallel splits your process documentation. Standardise on one before automating workflows on top of it. Potential saving: £6,200/yr
↪ Both tools appear in your reported stack with 3 shared capabilities.

Pricing is indicative, based on published vendor tiers at time of analysis — confirm current pricing before acting.

Underused features

Features you're paying for but likely not using.

HubSpot — Content assistant and email optimisation
Cuts campaign email drafting time by roughly half. Enable under Marketing → AI tools; no additional licence required on your current plan.
Slack — Workflow Builder
Automates recurring status requests and approvals that currently run over DMs. Available on your Business+ plan under Tools → Workflow Builder.
Google Workspace — Gemini in Docs and Sheets
First-draft generation and formula assistance inside tools the team already lives in. Included in your Business Standard plan; enable per-user in the admin console.

Recommended plan upgrades

Higher tiers that unlock API/automation for the tools you already use.

Why these sit alongside “activate what you already pay for”: together they add £1,380/mo (£16,560/yr), already netted off the benefit figure in the money section. And they buy API access the integration work in the later phases depends on — not features you already own. Budget them against phase 3–4, not phase 1.

HubSpot: Professional → Enterprise (+£1,100/mo)
Predictive lead scoring, custom AI-assisted workflows and the API access required for the unified reporting data layer in phase 4.
↪ API access begins at Enterprise on your current plan.
Canva: Teams → Enterprise (+£280/mo)
Brand controls, approval workflows and bulk AI asset generation across client accounts.
↪ Brand approval controls are Enterprise-only on your current tier.

Pricing is indicative, based on published vendor tiers at time of analysis — confirm current pricing before acting.

Capability gaps

Capabilities no tool in your current stack covers.

Meeting transcription and summarisation — consider AI meeting assistant
You report 26 hrs/week in meetings with manual follow-up. No tool in your current stack captures or summarises them.
↪ You reported ~26 hrs/week on Meetings.
Workflow automation / integration layer — consider iPaaS (Make, n8n or Zapier)
Your tools have APIs but nothing connects them, so data moves between platforms by hand.
↪ Your Workflow Automation score is 39/100.

Integration blueprint

Top integration opportunities already supported by your tools.

→
HubSpot → Slack (native) — Pushes deal and campaign notifications into channels
→
HubSpot → Google Workspace (native) — Syncs contacts and calendar activity to CRM records
→
Cision → Slack (api) — Delivers coverage alerts as a structured daily digest
→
Meltwater → Google Workspace (api) — Exports coverage data into the monthly client report template
→
Asana → Slack (native) — Creates tasks directly from messages
Appendix B

How do I know this is right?

Full transparency on how every number in this report is calculated. Every score and financial figure is produced by a deterministic formula from your questionnaire answers — the same answers always produce the same numbers.

Methodology informed by NIST AI RMF, ISO/IEC 42001, and GDPR/EU AI Act obligations, adapted for agency environments.

The 6-tier maturity scale

0–20Ad-hoc
21–35Emerging
36–50Developing
51–70Established
71–85Integrated
86–100Optimised

Dimension weights

DimensionWeightWhy This Weight
Strategy & Vision 20% Without leadership buy-in and strategic direction, all other efforts fragment.
Governance & Risk 10% The safety net. A low score dramatically increases the risk of incidents.
Data & Privacy 10% Data discipline determines what AI can safely touch.
Tools & Stack 20% Capability lives in the stack the team actually uses every day.
Workflow Automation 10% You can't automate chaos. Process maturity predicts deployment speed.
People & Skills 15% AI is only as effective as the people using it. Skills and culture determine adoption.
Measurement & ROI 10% What isn't measured doesn't compound. ROI evidence sustains investment.
Innovation & Experimentation 5% Structured experimentation keeps the agency ahead of the curve.

How your score is calculated

Step 1: Every scored question is answered on a 6-level behavioural scale (L0–L5) and carries a weight within its dimension
Step 2: Dimension score = earned points ÷ maximum points × 100, over the questions you answered
Step 3: Overall = Σ(Dimension Score × Dimension Weight), over the dimensions you answered — a dimension you skipped entirely is left out and its weight shared across the rest

Northbank Communications: (58×0.20) + (32×0.10) + (48×0.10) + (69×0.20) + (39×0.10) + (52×0.15) + (28×0.10) + (44×0.05) = 50.1, your overall score of 51 once rounded. Dimension scores are shown rounded to whole numbers here; the engine carries the unrounded values, so this sum can sit a fraction from 51.

How the achievable target is set

The dashed marker on your scorecard is computed, not aspirational — the uplift is larger for weaker dimensions, because early gains come fastest where least is in place.

Target = your dimension score + an uplift of 30 − (0.15 × your score), held between 10 and 30 points, and capped at 85.
A dimension at 30 is lifted about 26 points to 56; one already at 70 would reach 90 and is held at the 85 cap, which reflects that the top band needs sustained operating change rather than a single roadmap.
Worked for your weakest dimension, Measurement & ROI: 28 + (30 − 0.15 × 28) = 28 + 25.8 = 53.8, printed as 54.

How the phase budgets are set

Each action carries an hour allowance by its effort band — Low 4–8h, Medium 12–24h, High 24–60h of hands-on team time. A phase’s budget band is the sum of its actions’ allowances priced at your blended rate; the spread within a band is the allowance width, and a wide phase range simply means it holds high-effort actions. These are your team’s existing hours priced for planning — not new spend.

How the financial figures are calculated

Step 1: Your reported weekly hours per task × the recovery rate below = recoverable hours
Step 2: A workflow-maturity multiplier adjusts for how ready your processes are, set by your Workflow Automation score: below 40 → ×0.7, 40–70 → ×1.0, above 70 → ×1.1
Step 3: Recoverable hours × your blended rate × 48 working weeks, reduced by a 25% conservative cap
Step 4: Headline figures are published as a ±20% range — they are estimates, not promises

The best/likely/worst scenario table in the money section pairs the ends of that published range with a time-to-run-rate assumption: conservative reaches full run-rate at month 18, expected reaches full run-rate at month 12, upside reaches full run-rate at month 9. First-12-months figures assume value ramps linearly from zero to the run-rate over that period, which realises 33% / 50% / 63% of the run-rate respectively. The ramp lengths are Nexolve modelling assumptions anchored on the 12-month roadmap, not measured delivery data.

The recovery rates below are Nexolve modelling assumptions — the share of the hours you reported for each task area that we model as recoverable. They are not measured from your agency, and they are the same for every report built on your agency profile; the hours they are applied to are yours.

Task areaRecovery rate
Reporting & analytics 70%
Admin & operations 50%
Client communications 40%
Content creation 45%
Data collection 65%
Media monitoring 75%
Competitor research 60%
Meetings 15%

Peer comparison

Peer positions ("ahead of peers", "in line with peers") compare your dimension scores against Nexolve benchmark averages for agencies of your size band. We deliberately avoid percentile claims — positions are stated only where our benchmark data supports them.

The cohort average is the same weighted sum as your own score, applied to the benchmark averages for your size band — so the two numbers either side of the gap are calculated the same way and the difference is yours to check.
It is a benchmark average for a size band, not a live count of agencies audited this quarter. We do not publish a sample size, because the honest one changes every month.

Agency-size benchmark averages: Nexolve benchmark dataset (April 2026), updated quarterly. This report quotes no industry survey statistics — every figure in it is either something you told us or something calculated from it, by the formulas above.

Appendix C

Template pack

These templates turn the recommendations in this report into immediate action. Each one is designed to be customised for your agency in under an hour.

Template 1 of 6

AI Usage Policy

Your one-page AI policy. Customise the bracketed sections, get leadership sign-off, and share with the team. Review quarterly.

1. Purpose — This policy sets out how [Agency Name] uses artificial intelligence tools in our work.

2. Approved Tools — ✓ Approved: [e.g. ChatGPT Team, Claude Pro, Canva AI] ✗ Not Approved: [e.g. Free-tier tools with data training]

3. Data Rules — OK to input: publicly available info, your own drafts, anonymised data. Never input: client financial data, PII, unpublished strategies, credentials.

4. Client Disclosure — We are transparent with our clients about AI use. Position: [Choose approach].

5. Quality Review — All AI-generated work reviewed by qualified team member before delivery.

6. IP & Copyright — Our position on AI-generated creative, image generation and text: [state position]. Client industries with stricter requirements: [list].

7. Incident Response — Report to [AI Champion] immediately. Incidents logged and used to update policy.

Check your finished policy covers
☐
Approved tools — Name which tools are sanctioned and which are prohibited, and record each one’s data-retention position.
☐
Client data rules — Define what “client data” means per department. Never input client-confidential data, PII or financial information into a tool whose policy does not explicitly prevent training.
☐
Disclosure approach — Decide when and how clients are told. Proactive transparency is the defensible position — put it in contracts and proposals.
☐
Quality review — Require human review before delivery, and define the review standard for each content type.
☐
IP & copyright — State your position on AI-generated creative, image generation and text, judged against your clients’ industries.
☐
Incident response — Agree who owns an AI failure, and how the client is told, before one happens.
Template 2 of 6

AI Tool Evaluation Checklist

Use before adopting any new AI tool. Score each criterion 1-5. Tools scoring below 25/40 should not be adopted without exceptional justification.

Criteria: Data policy (GDPR, training) · Integration (API, n8n/Zapier) · Replaces vs Adds · Team adoption curve · Cost vs value · Scalability · Output quality (test with 3 real tasks) · Vendor stability

Template 3 of 6

Prompt Library Starter Kit

Role-specific starter prompts for Account Management, Content & Copywriting, SEO & Performance, PR & Communications, and Operations.

Template 4 of 6

Workflow Mapping Canvas

Document any process before automating it. Covers: trigger, steps, tools, people, time, frequency, pain points, and automation potential.

Template 5 of 6

Client AI Disclosure Statement

Drop into proposals, contracts, or standalone communication. Covers: what you use AI for, what you never use AI for, how you protect data, your quality standard.

Template 6 of 6

Monthly AI Adoption Review

Track progress monthly: team members using AI, hours saved, automations live, incidents, roadmap progress, what's working / what's not, next month priorities.

Appendix D

Implementation notes

Detailed guidance for each item in your roadmap — what tools to use, what to look out for, and the exact first step to take.

R1 Publish the AI usage policy

Phase 1

Establish the minimum governance layer covering approved tools, client-data rules and disclosure obligations. Expect this to take a low effort slot in phase 1, owned by the Governance & Risk lead.

HubSpotSlack

Paste this into ChatGPT or Claude, answer its questions, and review the draft it gives you — you are editing, not starting from a blank page:

“You are drafting a one-page AI usage policy for Northbank Communications, a UK agency. Sections: Purpose (two sentences); Approved tools (ask me for our list); Client-data rules (what may never be pasted into an AI tool — client-confidential material, personal data, unreleased work); Disclosure (when we tell clients AI was used); Quality review (a named human reviews AI-assisted output before it ships); Incident reporting (who to tell, how fast, no blame). Plain British English an account manager can read in three minutes. Where you need a decision from me, ask rather than invent.”
First Step

Block 90 minutes this week to scope "publish the ai usage policy" with the Governance & Risk lead and agree the definition of done.

R12 Complete vendor DPA and data-residency audit

Phase 1

Review DPAs for every AI vendor against GDPR obligations and log findings in a reusable checklist. Expect this to take a low effort slot in phase 1, owned by the Governance & Risk lead.

Google WorkspaceMake

Paste this into ChatGPT or Claude, answer its questions, and review the draft it gives you — you are editing, not starting from a blank page:

“Create a reusable one-page checklist Northbank Communications will run before adopting any AI tool, as a table: what we checked, answer, evidence link. Rows: does the vendor train models on our data; is a DPA available and signed; where is data stored (region); can we delete our data; access controls and SSO; the price tier we would actually need; who owns this tool internally. Finish with a three-line 'reject if' rule. Produce it ready to use.”
First Step

Block 90 minutes this week to scope "complete vendor dpa and data-residency audit" with the Governance & Risk lead and agree the definition of done.

R13 Appoint an AI champion per function

Phase 1

Designate one champion per major function and form a steering group that meets monthly. Expect this to take a low effort slot in phase 1, owned by the Strategy & Vision lead.

HubSpotSlack
First Step

Block 90 minutes this week to scope "appoint an ai champion per function" with the Strategy & Vision lead and agree the definition of done.

R2 Automate the daily media monitoring digest

Phase 2

Feed alert output into an AI call that produces a structured morning brief, eliminating manual feed-scanning. Expect this to take a medium effort slot in phase 2, owned by the Workflow Automation lead.

Google WorkspaceMake

This is a technical build (API connections or a dashboard), not a document. Three realistic routes: someone in-house who is comfortable with APIs; a non-developer working with an AI coding assistant such as Claude or ChatGPT, which can produce and explain a build like this step by step (allow focused days, not hours, and keep client data out of the session); or a freelance integrator — budget roughly £2–5k and 1–2 weeks. “No new tooling cost” on this action refers to licences; the build time is real.

First Step

Block 90 minutes this week to scope "automate the daily media monitoring digest" with the Workflow Automation lead and agree the definition of done.

R3 Automate monthly client coverage reporting

Phase 2

Connect monitoring and analytics data into a template, then use AI to draft commentary for human review. Expect this to take a medium effort slot in phase 2, owned by the Workflow Automation lead.

HubSpotSlack

This is a technical build (API connections or a dashboard), not a document. Three realistic routes: someone in-house who is comfortable with APIs; a non-developer working with an AI coding assistant such as Claude or ChatGPT, which can produce and explain a build like this step by step (allow focused days, not hours, and keep client data out of the session); or a freelance integrator — budget roughly £2–5k and 1–2 weeks. “No new tooling cost” on this action refers to licences; the build time is real.

First Step

Block 90 minutes this week to scope "automate monthly client coverage reporting" with the Workflow Automation lead and agree the definition of done.

R11 Audit and enable HubSpot built-in AI features

Phase 2

Activate the content assistant, predictive lead scoring and email optimisation you already pay for. Expect this to take a low effort slot in phase 2, owned by the Tools & Stack lead.

Google WorkspaceMake
First Step

Block 90 minutes this week to scope "audit and enable hubspot built-in ai features" with the Tools & Stack lead and agree the definition of done.

R14 Develop a two-year AI capability roadmap

Phase 3

A structured plan covering skill development, tool investment and quarterly milestones, presented at each board cycle. Expect this to take a medium effort slot in phase 3, owned by the Strategy & Vision lead.

HubSpotSlack
First Step

Block 90 minutes this week to scope "develop a two-year ai capability roadmap" with the Strategy & Vision lead and agree the definition of done.

R5 Build an AI ROI tracking dashboard

Phase 3

Track hours saved, output quality and cost per AI-assisted workflow. Your weakest dimension needs visible evidence. Expect this to take a medium effort slot in phase 3, owned by the Measurement & ROI lead.

Google WorkspaceMake

This is a technical build (API connections or a dashboard), not a document. Three realistic routes: someone in-house who is comfortable with APIs; a non-developer working with an AI coding assistant such as Claude or ChatGPT, which can produce and explain a build like this step by step (allow focused days, not hours, and keep client data out of the session); or a freelance integrator — budget roughly £2–5k and 1–2 weeks. “No new tooling cost” on this action refers to licences; the build time is real.

First Step

Block 90 minutes this week to scope "build an ai roi tracking dashboard" with the Measurement & ROI lead and agree the definition of done.

R15 Consolidate Cision and Meltwater onto one platform

Phase 3

The two share three core capabilities. Evaluate which better serves PR and crisis comms, then redirect the saved licence cost into AI tooling. Expect this to take a medium effort slot in phase 3, owned by the Tools & Stack lead.

HubSpotSlack
First Step

Block 90 minutes this week to scope "consolidate cision and meltwater onto one platform" with the Tools & Stack lead and agree the definition of done.

R8 Build a RAG knowledge base for credentials and case studies

Phase 4

Index credentials decks, case studies and brand guidelines so any team member can query institutional knowledge in seconds. Expect this to take a high effort slot in phase 4, owned by the Tools & Stack lead.

Google WorkspaceMake

This is a technical build (API connections or a dashboard), not a document. Three realistic routes: someone in-house who is comfortable with APIs; a non-developer working with an AI coding assistant such as Claude or ChatGPT, which can produce and explain a build like this step by step (allow focused days, not hours, and keep client data out of the session); or a freelance integrator — budget roughly £2–5k and 1–2 weeks. “No new tooling cost” on this action refers to licences; the build time is real.

First Step

Block 90 minutes this week to scope "build a rag knowledge base for credentials and case studies" with the Tools & Stack lead and agree the definition of done.

R9 Integrate client analytics into a unified data layer

Phase 4

Consolidate analytics, monitoring and CRM data into one queryable layer — the infrastructure that makes client-facing AI reporting commercially viable. Expect this to take a high effort slot in phase 4, owned by the Data & Privacy lead.

HubSpotSlack

This is a technical build (API connections or a dashboard), not a document. Three realistic routes: someone in-house who is comfortable with APIs; a non-developer working with an AI coding assistant such as Claude or ChatGPT, which can produce and explain a build like this step by step (allow focused days, not hours, and keep client data out of the session); or a freelance integrator — budget roughly £2–5k and 1–2 weeks. “No new tooling cost” on this action refers to licences; the build time is real.

First Step

Block 90 minutes this week to scope "integrate client analytics into a unified data layer" with the Data & Privacy lead and agree the definition of done.

R16 Package AI capability as a client offering

Phase 4

Convert proven internal workflows into a billable service line with its own credentials and pricing. Expect this to take a high effort slot in phase 4, owned by the Innovation & Experimentation lead.

Google WorkspaceMake
First Step

Block 90 minutes this week to scope "package ai capability as a client offering" with the Innovation & Experimentation lead and agree the definition of done.

Appendix E

What you told us

Every answer you gave, played back. Each score, hour and £ figure in this report is computed from these — so check them. Spot an error? Reply to your delivery email (or write to hello@nexolve.co.uk) within 7 days and we will regenerate your report on the corrected answers, free.

Your weekly hours — the input behind every £ figure

Agency-wide totals across the team, as you reported them. Every recovery calculation in section 04 is a fraction of these hours — if one is wrong, tell us and the numbers will be recomputed.

Reporting & analytics
34 hours / week (team total)
Admin & operations
16 hours / week (team total)
Client communications
22 hours / week (team total)
Content creation
30 hours / week (team total)
Data collection
12 hours / week (team total)
Media monitoring
20 hours / week (team total)
Competitor research
9 hours / week (team total)
Meetings
26 hours / week (team total)

Strategy & Vision

Does your agency have a written AI strategy — with goals, a named owner, and a review cadence?
L2 — Developing: Written AI section in agency strategy doc; not yet measured.
Do the agency's leaders use AI visibly themselves — or do they just encourage the team to?
L4 — Integrated: AI use by leaders is embedded in how they run the business
Does your agency have a dedicated budget for AI tools, training, and experimentation?
L2 — Developing: A rough annual figure is allocated but not tracked
Does your agency have an AI positioning statement used in pitches and credentials?
L3 — Established: A written AI narrative used consistently in pitches
Does your credentials deck or standard pitch materials explicitly feature AI capability?
Briefly mentioned but not a defined section or differentiator.

Governance & Risk

Does your agency have any rules about what client data can go into AI tools?
L4 — Integrated: Written policy + data classification by sensitivity, reviewed regularly
Does your agency have a written privacy policy covering how you handle client data?
L2 — Developing: A basic privacy policy exists but it's outdated or not enforced
If an AI tool got something badly wrong in client work, is there a clear escalation process?
No — there is no escalation path.
Does the agency keep a record of what personal data it holds for clients and where it lives?
Yes — we maintain a full inventory.
Are permissions in your agency tools set on a need-to-know basis — not everyone gets admin?
Yes — permissions are managed on a need-to-know basis across tools.
When your team uses client data in AI tools, what GDPR process — if any — kicks in?
L2 — Developing: We avoid obviously high-risk uses (e.g. no personal data in public AI tools)
Have you checked whether your AI tool providers have data processing agreements you can sign?
L1 — Emerging: Aware of DPAs but haven't actually checked
Has anyone at the agency assessed what the EU AI Act means for client campaigns that use AI personalisation or targeting?
Partially — I'm aware it exists but haven't assessed our obligations.
Do you check whether AI tool vendors hold SOC 2 or ISO 27001 certification before signing up?
Yes — security certification is part of our procurement process.
Do you know in which countries your AI tool vendors store and process your data?
For some tools — we know for the major ones but not all.
Do your clients know — and have they agreed — that AI tools are used in work on their accounts?
No — clients haven't been told AI is used on their work.
Does your agency have a list defining which types of client data can go into which AI tools?
L3 — Established: A written data classification matrix reviewed by the team
Has a formal data protection review been done for any AI tools that handle personal data?
L2 — Developing: A basic review done for one or two tools

Data & Privacy

How clean, complete, and consistent is the data your agency runs on day-to-day?
L2 — Developing: Data quality managed reactively — issues fixed when spotted.
Is your agency's data consolidated in one place — or scattered across disconnected tools?
L2 — Developing: A partial data hub (e.g. shared Airtable or Google Drive) used by some teams.
How much manual work does it take to get data into your AI tools — or does it flow in automatically?
L2 — Developing: Common data feeds are semi-automated — someone has built export shortcuts.
Does the agency keep a single, maintained inventory of what data it holds, where it lives, and which AI tools can access it?
L3 — Established: A documented data inventory covering primary tools, data types, and ownership.

Tools & Stack

How broad is your team's AI tool use across different types of work — creative, research, comms, ops?
L4 — Integrated: A broad AI stack across all key categories with usage tracked and a named owner per tool.
How integrated is your tool stack — do tools talk to each other or are they siloed?
L3 — Established: Some AI tool integrations built via Zapier, Make.com, or similar.
From your team's daily-work perspective, how integrated does your AI tool stack feel — or are tools still separate islands you have to switch between manually?
L3 — Established: Most daily tasks use a recognisable "stack" — tools feel complementary, even if not technically integrated.
Does your team capture what works in AI — prompts, techniques, workflows — in a shared library the whole team can use?
L3 — Established: A structured, maintained prompt library with categories and usage guidance.
How does your agency manage which AI tools staff can use — is there a formal process, or does everyone sort it themselves?
L4 — Integrated: Tools provisioned via SSO or group licences, with named owners and access reviews.

Workflow Automation

Are your recurring workflows written down clearly enough to follow — or to automate?
L2 — Developing: Some workflows written up informally (e.g. Notion page or Loom video), partially out of date.
How standardised are the recurring processes your account teams rely on — briefs, reports, reviews, handoffs?
L2 — Developing: Shared templates exist for some processes, used inconsistently.
Roughly what share of senior team time is genuinely strategic — as opposed to admin, reporting, and coordination?
L2 — Developing: Roughly half strategic, half operational (40–60% strategic).
How automated is the operational work in account teams — fully manual, partly automated, exception-based?
L2 — Developing: Several automations handling low-complexity, repetitive tasks.

People & Skills

Setting aside the enthusiasts — how consistently does your broader team use AI in their day-to-day work?
L3 — Established: Most staff use AI tools across a broad range of tasks with reliable outputs.
How structured is your approach to AI training — one-off sessions, rolling programmes, or continuous learning?
L2 — Developing: Occasional structured sessions for some staff — but no ongoing programme.
How confident is your team in applying AI to real client work — not just experimenting internally?
L3 — Established: Solid — most staff confident using AI for mainstream client tasks.
Does your agency have a named AI Champion with defined responsibilities and protected time for the role?
L2 — Developing: A named AI lead with informal responsibilities but no protected time.
Are staff performance reviews updated to include AI adoption and capability development?
L2 — Developing: AI adoption encouraged in reviews but not measured.
Can all client-facing staff confidently answer "how do you use AI?" in a pitch or kickoff conversation?
L3 — Established: Most staff can give a clear, honest answer with at least one specific example.

Measurement & ROI

How does the agency track time saved or ROI from AI tools — anecdotal, sampled, dashboarded?
L1 — Emerging: Anecdotal — individuals mention time saved informally, never aggregated.
Do you track how much your team produces per person — and can you show whether AI has changed that?
L1 — Emerging: Some output tracked informally — managers have a rough feel, nothing formal.
How does the agency report AI-derived value to clients — invisibly, in passing, as a tracked line?
L2 — Developing: AI mentioned in reports but not quantified.
Can you say how many hours AI saves your team each month — and what those hours are now being spent on?
L1 — Emerging: A rough sense but no numbers — "AI saves loads of time" but no measure.
What is the agency's primary billing model?
Monthly retainer
What is the agency's approximate blended hourly rate across all staff?
£50–£100/hour

Innovation & Experimentation

How much permission and protected time does the team have to experiment with new AI capabilities?
L2 — Developing: Occasional informal time when workload permits — ad-hoc and unreliable.
How often does the agency run structured AI experiments or pilots — not just try new tools informally?
L2 — Developing: Occasionally — a few informal experiments per year when a specific use case arises.
Are AI pilots run with structure — hypothesis, success criteria, kill / scale decision?
L2 — Developing: Basic structure — informal notes on what was tried and a rough sense of usefulness.
Do you have AI-enabled or AI-led services you can actively pitch and sell?
L3 — Established: One or two defined AI-enabled services that can be scoped and priced.
Do you have documented AI case studies with measurable outcomes?
L2 — Developing: One informal case study — a brief description without hard metrics.
Appendix F

Making it stick

The plan in this report fails or succeeds on adoption, not tooling. This kit is how to roll it out: the sequence of communication, a champions model, and the handful of metrics that tell you whether it is actually sticking.

Rollout, phase by phase

Keyed to the same phases as Section 11, Your sequence. The mechanics here are deliberately the same for every agency — rollout does not vary by score — but every R-number and target is this report’s own.

Before Phase 1 — set the frame

☐
Name one owner for the roadmap and get leadership sign-off on the Section 11 sequence — an unowned plan is a shelved plan.
☐
Tell the whole team what is changing and why, in one all-hands: the goal is recovering the hours this report prices, not reducing headcount. Say that sentence explicitly — the rollout inherits whatever the team assumes in its absence.
☐
Publish the AI usage policy (R1) before the first tool lands, so nobody has to guess what is allowed.

Phase 1–2 — pilot where it is easiest to win

☐
Start in the department your scorecards in Section 12 rate strongest, not the one with the biggest gap — the first story the rest of the agency hears about this rollout should be a win.
☐
R13 Appoint one champion per function (see the model below) and give them the quick wins to land first.
☐
Share one concrete win a week internally — a before/after with hours saved. Adoption follows proof, not policy.

Phase 3 — scale what worked

☐
R7 Put training on a standing cadence — a one-off session decays in a month; a monthly one compounds.
☐
Retire the manual version of any workflow once its automated version has run clean for a month. Running both indefinitely doubles the work and halves the trust.

Phase 4 and after — make it permanent

☐
Review the adoption metrics below quarterly at leadership level, alongside the client and finance numbers — what leadership inspects, the agency maintains.
☐
Log realised savings as they land, so next year’s AI budget is argued from your own evidence rather than a vendor’s.
The champions model

One person per function who is visibly enthusiastic and actually does the work — not the most senior person available. Their job, about 2–3 hours a week:

1. Hold a weekly 30-minute open session where anyone can bring a task and leave with a working prompt or workflow.  2. Collect the wins and the failures — both feed the quarterly review.  3. Be the named route for “can I use this tool for this?” questions, so the policy has a human front door.

The adoption metrics that matter

Track these monthly from Phase 1. They answer “is this sticking?” — the financial model in Section 04 answers “was it worth it?”.

MetricHow to measure itHealthy looks like
Weekly active use of approved tools Count of team members who used an approved AI tool for real work this week, from champions’ sessions or tool seat data Over half the team by month 3, rising
Hours recovered on the priced task areas Lightweight time tracking on the task areas this report prices, against the 60.3 hrs/week the model targets A visible downward trend by month 3 — hitting the full figure is a month-12 test, not a month-3 one
Automations live Workflows running unattended in production, against the 6 quick wins in Section 10 3+ live by month 3
Incidents and near-misses reported Entries in the incident log — wrong output caught in review, data pasted where it should not have been A small, non-zero number. Zero after month 3 usually means under-reporting, not perfection
Readiness re-score Retake the audit at month 12 and compare dimension by dimension Tracking toward the 78/100 (Integrated) 12-month target on your scorecard
Final word

Your journey starts here

Northbank Communications scored 51/100 — placing you at the Established level. That's not a judgement — it's a starting point. Every agency in our database began somewhere, and the ones pulling ahead aren't the biggest or best-funded. They're the ones that started.

This report has given you a clear map: where you are, where the gaps are, and exactly where to start. Your single highest-impact first move is Publish a one-page AI usage policy — it's low effort and can begin this week.

What happens next

1. Open your Action Plan — it is a separate Google Sheet, linked in the email that delivered this report (not a page of this PDF). It has every recommendation scored and prioritised; if the link is not in your inbox, reply to the delivery email and we will resend it. 2. Pick your first Quick Win — start with one automation this week. Momentum matters more than perfection. 3. Name your implementer — hand the action list, Appendix C and Appendix D to a named internal owner, or to a contractor for the actions marked “Build required”. This report is the brief; the R-numbers make the hand-off unambiguous.

Questions about this report?

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