An increasing number of agencies are including promises of AI benefits in their pitches. Typically, these are framed in the context of improved productivity through automation, allowing the agency to provide more for less. A.K.A. – savings.

This is a valuable opportunity for any marketer facing heightened demand, a tight budget, or any procurement team focused and measured on cost reductions.

However, the claims of savings are becoming increasingly unbelievable, with some boasting of up to 60% more in savings than what the marketer is currently paying. It’s not just content and creative agencies involved; media agencies also offer lower fees and, in some cases, no fees at all, arguing that automation has substantially reduced their labour costs and, therefore, the fees required to provide those services. Additional factors beyond automation efficiency come into play with media agencies, including non-transparent payments from media sellers and principal-based trading. For this discussion, let’s concentrate on content creation.

ChatGPT was launched in November 2022, and in this brief period, agencies have raced toward the shiny new offering. Major holding companies and groups have announced significant investments in AI technology amounting to millions and even, in some cases, billions of dollars.

While many herald this integration of Artificial Intelligence (AI) and automation within advertising agencies as a pathway to increased efficiency and reduced operational costs, the underlying investments required for AI adoption necessitate a re-evaluation of agency compensation models. This often means that the anticipated cost reductions may not materialise or may even lead to new cost structures.

While AI undoubtedly offers the potential to streamline workflows, enhance creativity, and provide deeper insights, the assumption that its application will automatically translate to lower client costs is a significant oversimplification.

Embracing change within the agency

There is an early mover advantage for any agency in adopting AI, through the faster generation of insights, more agile strategy iteration, and adaptable marketing processes. However, implementing AI solutions in an advertising agency involves several key considerations, including:

  1. Strategic Planning and Roadmap: Agencies must establish a clear roadmap for AI adoption in marketing. This entails setting up a cross-functional AI council to provide direction and drive strategy. The council should concentrate on risk management, upskilling talent, and fostering trust. It also needs to review existing client contracts and collaborate with clients to identify and mitigate risks.
  2. Data Readiness: Many agencies neglect data governance and metadata management, which needs to be implemented at the enterprise level. Identify key marketing use cases for AI and designate a marketing data champion to oversee and maintain data quality and security. This may require many agencies to recruit essential skills in data and technology to manage this process. Data management, a critical component for effective AI utilisation, demands robust systems and expertise, adding another layer of expense.
  3. Change Management: Successful implementation requires change management across every element of the current workflow within the agency, the clients and the agency suppliers. This includes training and upskilling employees to prepare them for AI integration.
  4. Financial Costs: While the holding companies have announced significant investments in this area, the financial cost of implementing AI for an independent agency or a local office of a network agency can vary widely depending on the scale and scope of the project. Costs will include software and hardware investments, data management, training, ongoing maintenance, improvements and upgrades. The budget for these expenses must be considered against potential cost savings from increased productivity and efficiency. We explore these costs further below.
  5. Timeline: The timeline for implementing AI solutions can also vary. Initial pilot projects may take a few months, while full-scale implementation could take a year or two or more. It’s crucial to have a phased approach, starting with pilot projects and gradually expanding to widespread implementation. We look at these in more detail below.

The timeline and cost of fully automating an advertising agency using AI can vary significantly based on several factors, including the agency’s size, the complexity of the tasks, the technology stack chosen, and the level of customisation required.

Financial Investment

While the financial cost of designing and implementing an AI-enabled transformation of an agency will vary wildly depending on the size of the agency, the range of services and processes impacted, the level of complexity of integrating into existing client business and more, for this exercise, we have developed costs for a medium size independent creative agency offering strategic, creative and production across digital advertising.

These cost estimates are broken down into the following four categories:

  1. Technology Costs: This is based on purchasing existing software and developing custom solutions. It includes designing, training and testing the AI platform with costs ranging from $250,000 to several million dollars.
  2. Consulting Fees: There are already a plethora of AI and Tech Consultants hawking their services in marketing and advertising. Hiring one or more of these experts to guide the automation process may cost $100,000 to $200,000 per year.
  3. Training and Talent Costs: The human cost is often overlooked when discussing technology-driven transformation. Investing in training your existing employees on a new system or recruiting subject matter experts could cost between $50,000 and $100,000 per year, excluding additional salary costs.
  4. Ongoing Maintenance and Enhancements: Technology is not a set-and-forget investment. Keeping up with ongoing maintenance, updates, and upgrades could cost 10% to 20% of the initial cost annually.

Depending on the scale and scope of automation, the total investment might range from $500,000 to several million dollars per agency or office. Since agencies are at best reporting a 15% EBIT on income, the agency would need to sacrifice almost three and a half million in revenue to fund even the entry-level investment, and it just goes up from there.

Investment in time.

Many estimates put the entire AI transformation process at anywhere from one to two years for a typical independent agency. While some argue that they are further developed in less time, it is unlikely that they have embraced a total business transformation and instead have looked at piloting a smaller, more defined implementation of generative AI rather than a total business transformation.

While it may be argued that the race to AI integration started in 2022, many agencies have been slow to start. This is because of a number of key issues.

  1. Decision paralysis – caused by a lack of knowledge or understanding of the opportunities.
  2. Risk mitigation – hoping that others will lead the way and reduce the inherent risks of mapping their own transformation.
  3. False starts – with successive small projects that have led to poor outcomes or abandoned due to loss of momentum and commitment.

Considering that the market is allowing the following timelines for each of the transformation steps identified above across the one-to-two-year transformation process:

  1. Initial Assessment (1-3 months): Evaluate current processes and identify areas for automation.
  2. Development and Integration (6-12 months): Build or implement AI tools, integrate them into existing systems, and customise them to fit the agency’s needs.
  3. Testing and Iteration (3-6 months): Test the systems, gather feedback, and make necessary adjustments.
  4. Training and Transition (2-4 months): Recruit new staff capabilities and train existing staff on new systems and transition workflows.

Impact on Agency Fees

With the agencies facing a two-year, multimillion-dollar investment to transform their business model to leverage the advantages of AI and automation technology, it is worthwhile to consider how this will be funded, not just for the upfront transformation but the ongoing maintenance and upgrade costs.

The overwhelmingly standard agency fee model continues to be a resource-based cost model based on hourly rates, billable hours, overhead, and profit margins. How will an agency build the cost of the AI transformation process into the current fee model, including the external and internal cost of resources? A simplistic approach would be to increase the overhead of their resource fees. However, these fees and rates have been under significant competition and negotiation processes for a number of years, and many agencies would struggle to have their existing clients agree to pay more for the same services.

Instead, after years of agencies clinging to traditional fee models, we see more agencies embrace new fee approaches. These evolving pricing models highlight a crucial shift for agencies moving away from billing for their time and towards pricing based on the value and results delivered, often enabled by AI.

Efficiency-Based Enhanced Profitability Pricing: Agencies that use AI to produce more output with less time and cost may maintain fixed-fee pricing without reducing revenue. While the agency benefits from increased profitability due to AI-driven efficiencies, the client does not necessarily see a direct cost reduction.

Tech Fees Surcharge: Agencies are introducing a surcharge, typically 1-5% of agency fees, to account for their technology investments. This direct pass-through of costs ensures that clients contribute to the agency’s AI infrastructure.

Increased Traditional Pricing Models: With increased expertise and quality of agency staff, particularly in tech and data, AI can increase existing labour-based or output-based models. For example, agencies may charge for additional, higher-level staff required to manage AI tools or to generate revenue from new, AI-enhanced creative deliverables. This won’t necessarily lead to lower costs and could potentially increase costs.

Other Non-Traditional Pricing Models: Agencies that develop specific and customised AI applications and platforms, such as LLMs or workflow bots, are exploring subscription or licensing models, treating their AI as intellectual property.

These approaches indicate that while AI can drive efficiencies, agencies’ primary focus is leveraging it to enhance their offerings and improve their financial performance, which doesn’t automatically equate to lower client costs.

The bottom line for agencies and marketers

While AI and automation hold immense potential to transform advertising agencies, the narrative of automatic cost reduction overlooks the significant underlying investments in technology, talent, and training required for successful implementation.

To recoup these costs and capitalise on AI’s value, agencies must adapt their fee models through surcharges, efficiency-based pricing, and the monetisation of AI-driven intellectual property.

Consequently, the application of AI in advertising agencies is more likely to result in a restructuring of fee models and a shift towards value-based pricing rather than a straightforward decrease in advertisers’ costs, as agencies often propose in the highly competitive pitching world.

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We love it when a client says to us… “we’ve never thought of asking our agency that…”

More often than not, it’s the questions that haven’t been asked can be hugely valuable in assessing your current agency’s thought leadership capabilities and willingness to collaborate to from a better relationship.

Whether you’re considering hiring an agency or reviewing your current one, these five questions can offer valuable insights. (And yes, the answers might surprise you):

What’s your vision for your agency two or three years from now?

Why this is important: Essentially what you’re asking here is “what’s your vision for the communications business for the foreseeable future?” A good answer should provide insight into the factors you and your team should consider. These should be thought provoking comments that really make you think whether you and your teams are structured for success.

Which of your agency’s experts should we be following on social media?

Why this is important: Your question here is two-fold. First, you’re asking where the real thought leadership in the agency is coming from;  Is it in their strategy department?  Is it in Social leadership? Or is it coming from somewhere else (and why aren’t they on your business…)? You’re also assessing the general level of strategic thinking and industry insights offered by the agency. If (God forbid) the answer is “– er… no.” you should probably ask, “why not…?”

What do you think of our current contract – how should we improve it?

Why this is important: Your question here is really about collaboration. Yes, there’ll be obvious “why do you ask…?” or “yes – we’d like a bigger retainer, please…” but what you’re fishing for is the real contribution being made to your business.  For many marketers, you’ll want to pay special attention to AI, if and how it’s being used on your business and what clauses should be considered to protect your data and your organization.

How do you think we can strengthen our teamwork?

Why this is important: You want to know how the agency sees itself fitting into your broader marketing picture and other agency partners. If the answer is, “we don’t know we never talk to X or Y…” it’s an indication broader collaboration is needed. If you suspect the collaboration isn’t as effective as it should be, despite positive feedback, take proactive steps to address the issue.

How is your agency leveraging AI (on our business)?

Why this is important: First, you need to understand if the agency is using AI technologies on your business and if so, what and how are they being implemented? Second, you want to understand how the agency’s use of AI will affect and potentially enhance your business. At the same time, you’ll also want to understand the agency’s roadmap for leveraging AI, what guardrails are in place, how they’re mitigating potential bias and what contractual issues you should be contemplating.

By asking some or all of these questions, you should be able to strengthen your vision into your incumbent agencies’ capabilities, strategic thinking, and commitment to delivering exceptional results on your business.  And if it’s sparked ideas for additional questions – please let us know!

 

 

Photo: Adobe stock image

We’ve managed a significant number of pitches over the last couple of years and there’s no getting away from the fact that Artificial Intelligence has become the hot topic in pitches. And little wonder when you consider how advertisers are leveraging AI to streamline their marketing efforts, optimize their campaigns and improve their targeting capabilities.

However, as AI technologies continue to evolve, advertisers must ensure they understand the benefits and limitations of AI and its legal and privacy concerns. Here are some fundamental questions advertisers should ask themselves when it comes to AI:

What Are the demonstrable benefits of AI technology?

AI algorithms can analyze vast amounts of data to identify patterns which should lead to improvements in an an array of areas including:

But what does your agency say – what benefits are they proposing – and how can these be proven?

Do you have Clients we can talk to about their experience?

There’s always been a running joke about how small the advertising business in Canada – but my guess is it’s about to get a whole lot smaller globally. And the reason is that when it comes to AI, none of us know what we don’t know, and none of us are quite sure whether it’s a Godsend or the devil incarnate. So advertisers are going need to be talking to their peers about their experiences with AI and whether the hype is real or just smoke and mirrors.

So whether you’re an advertiser working with an incumbent, or you’re in the process of figuring out new solutions with a potential new agency partner – connecting with other clients about their experiences is likely going to be a first port of call as we all try and figure this out together.

What Are The Limitations Of AI?

AI is excellent at identifying patterns and trends but has limited understanding of the context in which those patterns occur. This can lead to inaccurate predictions or recommendations. Advertisers also need to be clear that AI is not creative in the way that humans are. It cannot create entirely new concepts or ideas, only generate variations of what it has been trained on. Ultimately, AI requires human input to operate effectively and advertisers need to ensure that the data and inputs they provide are accurate, relevant, and free from bias.

What Legal and Privacy Concerns Should We Know About?

As advertisers increasingly rely on artificial intelligence (AI) to collect and analyze data for targeted advertising, there are obviously critical legal and privacy concerns to consider:

  1. Data privacy. Advertisers must ensure that they are collecting and using data in a way that complies with applicable data privacy laws
  2. Discrimination. AI can unintentionally perpetuate or exacerbate discrimination by making biased decisions based on demographic or other characteristics and advertisers need to ensure that their AI models are designed in a way that avoids discrimination and bias
  3. Transparency. Advertisers must be transparent about how they are using AI to target ads and should disclose that information to consumers, what data is being collected and how it is being used.
  4. Liability. Advertisers can be held liable for any harm caused by their use of AI, including data breaches or other privacy violations. So all advertisers need to be aware of their legal responsibilities and take appropriate steps to mitigate that risk. Cue your legal team who need to be ahead of the curve on all this before you begin.

Who owns the data?

AI data is a combination of first-party data, which is the data that companies collect from their own customers, and third-party data, which is data that is collected from other sources. So who owns your data depends on who you ask.

If the data is generated and owned by the advertiser and they choose to share it with their advertising agency(s) for the purpose of developing and executing campaigns then it’s likely the advertiser that will want to own it. But if the data is generated by the agency as part of their services, the agency(s) may have a legitimate claim to ownership of that data.

So before anyone does anything with any data, this should determined and agreed between the advertiser, their respective agencies and the AI companies, to ensure before you move forward with any AI marketing initiative.

And… What do you need from us?

It’s likely that not everyone saw that question coming… but the reality is advertisers can’t put AI on autopilot with their agencies. It would perhaps be easy to say ‘invest in training…’ and yes, that’s an option, but in the short term advertisers need to equip themselves with sufficient knowledge to enable them to:

  1. Understand the basics. At the very least, advertisers must have a basic understanding of what AI is, how it works, and what it can (and can’t) do. This will help you communicate your needs and goals to your agency more effectively.
  2. Data quality. AI algorithms require large quantities of high-quality data to function properly. As an advertiser, you should ensure that your agency has access to relevant data and that the data is accurate, secure and scraped of personal information
  3. Privacy and ethics. With the increasing use of AI in advertising, it’s important to consider the privacy and ethical implications of using AI technologies as noted above. At a minimum, advertisers must ensure the have taken appropriate measures to protect user data and ensure ethical advertising practices that follow.

Yes, AI in advertising has the potential to revolutionize the way businesses engage with customers. However, as with any technology, there are a myriad of questions that businesses need to ask themselves about the impact of AI on advertising and these are just a few to get you started.

So, are you ready to embrace AI with your agency? Are you excited, terrified or just confused?

(Yeah, me too).