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The numbers don’t lie. Ad agencies are grappling with slow growth and challenges integrating AI into their processes.

Barclays analysts recently downgraded the stock of several major ad agencies – saying that fears of slow growth over billing models as client bypass agencies over accessible AI tooling.

“AI automating many aspects of the advertising value chain and lowering barriers to entry into certain channels or ad formats for advertisers may expand the number of businesses globally that deploy ad spend,” Barclays Analysts – Wall Street Journal

You think you need more people when what you actually need is better systems. The harsh reality? A mere 4% of businesses have achieved fully automated workspaces, meaning 96% of agencies are still drowning in manual processes that could be solved with the right approach.

After working in agencies for the last 10+ years of varying sizes – I understand the pinch points of where agencies struggle, and ultimately where agencies are losing out.

The good news? Every single one can be automated with AI tools that already exist today.

1. Client Onboarding: The 72 Hour Make-or-Break Window

The Reality Check: Your new client just signed a £5,000 monthly retainer, and they’re buzzing with excitement. Then comes the onboarding process: scattered email chains, missing access credentials, delayed project kickoffs, and that awkward moment when you realise you’ve forgotten to set up their folder structure for the third time this month.

Within 72 hours, their excitement turns to doubt. By day seven, they’re questioning their decision entirely.

What This Actually Costs You: Consider a typical mid-sized agency onboarding 2 new clients monthly. Each onboarding takes approximately 12 hours of fragmented work across multiple team members. That’s 24 hours of non-billable time monthly, or roughly £1,200 in lost productivity (assuming a £50/hour internal rate).

From personal experience – I can tell you that a difficult onboarding process can take months to recover from, rebuilding lost trust, relationships and recovering from doubt. Even with the most robust processes within your agency – client onboarding can go wrong!

More critically, poor onboarding directly impacts retention. Research consistently shows that clients who experience smooth onboarding are 67% more likely to remain with an agency beyond the first year.

The AI Solution in Action:

Take this example which illustrates a typical client onboarding process in an agency environment with typical manual processes, and how the process could be developed with AI solutions:

Before:

  • New client signs contract
  • Account manager manually creates project boards
  • Separate email requesting access credentials
  • Another email with brand guidelines request
  • Yet another email with meeting scheduler
  • Finally, a welcome call gets booked for next week.

After:

  • Contract signature triggers automated sequence
  • Welcome email with branded video introduction
  • Smart questionnaire collecting success criteria, goals, target audience, and access credentials
  • AI processes responses and creates project boards in Asana
  • Automated access requests sent to all platforms
  • Client folder pre-populated with templates
  • Welcome call automatically scheduled within 48 hours
  • Success metrics document generated based on client responses.

Real Result: Reduction in onboarding time from ~12 hours to 30 minutes, ensuring the client gets a top-tier onboarding experience – and that your agency has the success documentation you need to hit the ground running. The process may seem simple – but it can be complex to implement properly, yet can save you over £1,000 of overheads per new client – and I guarantee less headaches from your team!

2. Paid Campaign Management: The Constant Context-Switching Nightmare

The Daily Struggle: Your PPC specialist starts the morning checking performance across Google Ads, Meta, LinkedIn, and TikTok for 12 different clients. By 11 AM, they’ve logged into 48 different accounts, manually exported data from each platform, and discovered three campaigns that need immediate attention. By lunchtime, they haven’t actually optimised anything yet.

This scenario isn’t an exaggeration. Agency PPC managers spend roughly 60% of their time on data gathering and reporting, leaving just 40% for actual strategic work.

Having led paid media departments in agencies myself – even with automated spend trackers and anomaly trackers – it is often a tedious and time-consuming process for any team to monitor the basics effectively.

The Hidden Costs: A senior PPC manager earning £45,000 annually spends approximately £27,000 worth of time on manual tasks that could be automated. Multiply this across a team of three specialists, and you’re looking at £81,000 in wasted salary budget annually.

AI Automation Examples:

Performance Monitoring: Automated solutions could include daily performance summary alerts via slack or MS Teams that alert for any anomalies before 9am of impressions and spend. AI agents automatically forecast and predict the day’s projected performance and run rate for the month across all ad accounts – taking away the emphasis of needing to go into several Excel or Google Sheets every morning!

Bid Management: AI-powered bid adjustments based on performance patterns, time of day, device performance, and audience behaviour. One agency reported a 23% improvement in ROAS after implementing automated bidding rules that adjust every 6 hours based on conversion data.

Creative Testing: Automated A/B testing workflows that pause losing creatives, scale winning variants, and request new creative assets when performance drops below benchmarks. AI automations that monitor and analyse Meta ads for creative fatigue each day based on declining impressions and other indicators – and automatically produce variants to be published on human approval.

Budget Reallocation: Daily budget shifts between campaigns based on performance data. High-performing campaigns automatically receive additional budget from underperforming ones within predetermined limits.

Real Result: Over 40% reduction in time ‘data wrangling’ for your Paid Media team, Monitoring, testing and anomalies flagged up on autopilot – whether that’s a direct message into your Slack/MS Teams from an AI agent awaiting approval or fully automated approach – automating these processes frees your PPC team up to do actual strategic value work for clients.

3. SEO Bottlenecks: When Manual Processes Kill Momentum

The Technical Audit Trap: A comprehensive technical SEO audit should be the foundation of every client strategy, but most agencies either skip it (because they don’t have time) or charge extra for it (because it takes forever). The result? Strategies built on assumptions rather than data.

A proper technical audit typically requires:

  • Site crawling and analysis (2-3 hours)
  • Manual checking of Core Web Vitals (45 minutes)
  • Schema markup validation (1 hour)
  • Internal linking analysis (1.5 hours)
  • Indexation issue identification (1 hour)
  • Competitive analysis (2 hours)
  • Report compilation and recommendations (2 hours)

Total time: 8-10 hours for a senior SEO specialist.

Content Bottlenecks: Content planning, keyword research, and briefing creation consume enormous amounts of time. Most agencies spend 3-4 hours per client monthly just planning content, before any actual creation begins.

The AI Breakthrough:

Automated Technical Audits: Utilising AI automations and agents allows you to run full comprehensive technical and content audits in minutes rather than days. At flowio we have built a bespoke technical audit that

  • Crawls an entire website – through manual entry or sitemap identification
  • Audits each page against industry or agency bespoke technical checklist using advanced o1 models
  • Scores each checkpoint item with impact, effort and KPIs
  • Performs a robust content analysis
  • Outputs into a database and task management system
  • Sends a stylised agency standard audit report in email and Google Sheets
Example client-ready audit from flowio

This output cuts down a standard tech SEO audit from what could have taken 12 hours to 20 minutes – with the end output of a client-ready document – and internal task management already setup.

Content Intelligence: AI systems that analyse top-performing content in client niches, identify content gaps, suggest topic clusters, and generate detailed content briefs. One agency reduced their content planning time from 4 hours to 30 minutes per client while improving content performance by 45%.

Keyword Research Automation: Instead of manually researching keywords, AI analyses competitor content, identifies ranking opportunities, and suggests keyword clusters with search volume and difficulty scores. What used to take hours now happens in minutes.

4. Reporting: The Monthly Time Vampire

The Brutal Truth: Account managers dread report week. They spend days copying data from multiple platforms into spreadsheets, trying to craft narratives that make sense of the numbers, and inevitably discovering errors just before client calls.

According to industry research, agencies spend an average of 8 hours per client monthly on reporting activities. For an agency managing 25 clients, that’s 200 hours monthly, an entire full-time position dedicated solely to data compilation.

Within my last agency – even with automation through the likes of SuperMetrics, Looker Studio and GA4 automation – it could take an entire morning across multiple team members to wrangle data together, and the most time-consuming part which is adding contextual insight.

Beyond Time Costs: Manual reporting creates several hidden problems:

  • Inconsistency: Different account managers present data differently
  • Delays: Reports are often late, especially when team members are sick or on holiday
  • Superficial Analysis: Time spent on data gathering leaves little time for actual insights
  • Client Frustration: Clients receive data dumps rather than strategic recommendations

AI-Powered Solutions:

Automated Data Integration: AI automation automatically pulls all data direct from platform – warehouses in a data lake such as BigQuery – cleansing and preparing the data for query. Separate AI agents pull what data they need – and search for additional context, what changes have occurred this week via change sheets to add detailed context to data.

Natural Language Insights: The difference of uploading an excel of data to ChatGPT and asking it to produce insights on the numbers, to utilising an AI agent to pull from different sources to add context to data is profound. An AI agent has the ability to pull in contextual data from other sources – change trackers, market research, keyword trend data, even stock prices or weather. This gives it the enhanced ability to add context to insights rather than ‘These numbers were up versus last week’.

Predictive Alerts: AI monitors performance trends and proactively identifies issues before they become problems. Agencies receive automated alerts when metrics show concerning patterns, often with suggested solutions.

Dynamic Reporting: Instead of static monthly reports, clients access real-time dashboards with AI-generated insights updated continuously. Account managers receive automated summaries of key changes to discuss during client calls.

5. Internal Knowledge Management: The Expertise Trap

The Problem Nobody Talks About: Your best strategist leaves for a competitor, taking years of client knowledge, process understanding, and strategic insights with them. Suddenly, the rest of your team is scrambling to piece together how things actually work.

This scenario plays out in agencies constantly because most knowledge exists in people’s heads rather than accessible systems. New hires spend months figuring out processes that should take days to learn. I have seen this happen way too many times in agencies – deals not being done correctly – missing documents – that one piece of information that only they knew.

The Real Cost: Knowledge silos create multiple expensive problems:

  • Training Time: New hires take 3-6 months to become fully productive
  • Inconsistent Delivery: Different team members use different approaches
  • Lost Opportunities: Teams can’t access historical strategies that worked
  • Client Risk: Key knowledge leaves when people leave

AI Knowledge Systems:

The Agency Brain: Agencies can train an AI on all of their internal documentation, SOPs, SoW’s, even contracts and internal policy documents. Utilise RAG (Retrieval Augmented Generation) with embedded vector databases.

New team members can now ask: “What’s our process for setting up conversion tracking for e-commerce clients?” and receive step-by-step instructions instantly. Sales teams query: “Show me case studies for fintech clients with budgets over £10k” and get relevant examples in seconds.

Imagine being able to get the info you need in an instant via Slack or MS Teams rather than having to wait for that one person to come out of a meeting that might know the answer.

Process Documentation: AI automatically documents workflows by analysing team activities and communications, creating up-to-date SOPs without manual effort.

Intelligent Search: Instead of hunting through folders, team members ask questions in natural language and get specific answers with source references.

6. Lead Qualification and Sales Process Inefficiencies

The Sales Time Sink: Your sales team spends hours on discovery calls with prospects who can’t afford your services, don’t understand your value, or aren’t ready to buy. Meanwhile, qualified prospects slip through the cracks because follow-up gets delayed or forgotten.

Most agencies operate with gut-feel sales processes rather than data-driven approaches. They can’t accurately predict which prospects will close or identify the optimal time for follow-up.

The Qualification Challenge: Traditional lead qualification relies on lengthy forms (which reduce conversion rates) or time-consuming discovery calls (which drain sales resources). Many agencies either over-qualify (losing good prospects) or under-qualify (wasting time on poor fits).

AI-Enhanced Sales Automation:

Intelligent Lead Scoring: Utilise AI analysis for prospects across multiple touchpoints, implementing a scoring algorithm that scores based on intent, consumption of content. Did that prospect attend your webinar? Did they look at your whitepaper? At what stage are they at in the buying cycle? Real-time, and dynamic lead scoring is now a thing. High-scoring prospects receive immediate attention, while lower-scoring leads enter nurture sequences.

Conversational Qualification: AI chatbots conduct initial qualification conversations, asking intelligent follow-up questions based on responses. Prospects receive helpful information while the system gathers qualification data automatically.

Predictive Follow-up: AI analyses communication patterns to predict optimal follow-up timing. Instead of generic sequences, prospects receive personalised outreach based on their engagement behaviour and stage in the buying process.

7. Project Management and Team Coordination Chaos

The Coordination Nightmare: Multiple clients, different project stages, varying team capacities, changing priorities, and constant context switching. Project managers spend more time updating systems and chasing status updates than actually managing projects.

Team members work on tasks without understanding broader context or priorities. Deadlines get missed because workload visibility is poor, and client work gets deprioritised when urgent requests come in.

The Hidden Productivity Drain: Poor project coordination creates cascading problems:

  • Context Switching: Team members lose productivity switching between different client contexts
  • Deadline Pressure: Last-minute rushes become the norm instead of exception
  • Quality Issues: Rushed work requires revisions, creating additional cycles
  • Team Burnout: Constant urgency and unclear priorities stress teams
  • Client Frustration: Delays and quality issues damage relationships

AI Project Automation:

Intelligent Task Allocation: AI analyses team capacity, skill sets, current workload, and project requirements to suggest optimal task assignments. The system considers individual productivity patterns and availability to prevent overloading.

Predictive Scheduling: Using historical data, AI predicts realistic timelines for different types of work and automatically adjusts project schedules when bottlenecks are identified.

Automated Status Updates: Instead of chasing progress updates, AI gathers information from various tools and communication channels to provide real-time project visibility. Project managers receive automated summaries highlighting risks and bottlenecks.

Resource Optimisation: AI identifies when team members are over or under-utilised and suggests rebalancing across projects. The system can predict when additional resources will be needed and recommend hiring or contractor usage.

The Implementation Reality: Where Most Agencies Go Wrong

Here’s where most agencies stumble: they try to automate everything at once or focus on the wrong problems first. The successful agencies I work with follow a specific pattern:

Phase 1: Stop the Bleeding (Weeks 1-4) Start with your biggest time sink. For most agencies, that’s reporting or client onboarding. Pick one workflow, automate it completely, and measure the time savings. With automation – this is easy to translate into ROI – 6 hours a week? 24 hours a month? Multiplied by £50/hour? That’s an instant £1,200 per month saving – or, as I see it – £1,200 additional investment into your growth every month.

Phase 2: Build Momentum (Weeks 5-12) Use the time savings from Phase 1 to tackle the next biggest bottleneck. This creates a positive feedback loop where automation creates time to implement more automation.

Phase 3: Scale Intelligence (Weeks 13-24) With basic automation in place, focus on AI systems that improve decision-making rather than just saving time. This includes predictive analytics, intelligent recommendations, and automated insights.

According to Gartner, integrating hyper-automation technology with improved operational procedures would reduce operational expenses by 30% by 2024. The agencies implementing these solutions aren’t just saving time—they’re fundamentally changing their cost structure and capability.

The Competitive Reality Check

While you’re debating whether to invest in automation, your competitors are already implementing these solutions. The agencies that transform their operations first will dominate their local markets because they can:

  • Deliver faster results with better quality
  • Offer competitive pricing due to lower operational costs
  • Scale without proportional increases in headcount
  • Provide superior client experiences through consistent processes

The question isn’t whether automation will transform agency operations – it already is. The question is whether you’ll lead that transformation or spend the next few years playing catch-up.

The Bottom Line: These seven bottlenecks are stealing your agency’s potential. Every day you operate with manual processes is another day your competitors gain ground. The tools to solve these problems exist right now, and the agencies using them are pulling ahead fast.

The choice is simple: automate or get automated out of business. The agencies that survive and thrive in 2025 will be those that embrace AI not as a marketing buzzword, but as the operational foundation of their business.

Time to stop talking about AI and start implementing it where it actually matters: in the daily workflows that determine whether your agency scales or stalls.

flowio is an AI Growth Solutions Agency that specialises in building bespoke AI workflows backed in deep experience for digital marketing agencies across the UK. Book in a strategy call to discover which workflows are costing you the most time and money.