It only took a few short years, but the advertising industry is awash with AI solutions. AI can help AdOps teams with a multitude of tasks, including generating ad copy and images, as well as analyzing data for new opportunities. Publisher-native AI tools can even help improve performance channel-specific strategies.
Choosing the right AI solutions for your advertising teams isn’t easy. There are many factors to consider—security, ease of use and implementation, ROI—and stakeholders to involve. With so many options available, how can you discern which AI tools are right for your team’s biggest challenges?
This article examines:
- The difference between AI point solutions and integrated AI
- The benefits and challenges of both point solutions and integrated AI
- How each of these AI solutions impacts your advertising operations, both in the short term and the long run
- How to vet the right AI tools for your advertising business, including important questions to ask
Integrated AI vs. AI point solutions: what’s the difference?
Like most digital solutions, the AI tools available to advertisers fall into two distinct categories: point solutions and integrated solutions. Let’s look at the difference between them.
Point solutions: AI point solutions are single-function tools, typically designed to address a specific pain point. They can help your team improve an isolated component of your AdOps workflow. The most prolific example of advertising point solutions is AI that helps you write, modify, or improve ad assets like headlines and descriptions.
One reason point solutions are so widely available is that they are typically easier to implement. Point solutions very rarely interact with other tools in your tech stack, acting as stand-alone tech that often requires you to import data each time you want to utilize the AI’s specialized function.
Integrated solutions: Integrated AI tools refer to solutions that are embedded within existing systems. These tools have direct access to any data that exists within the system, allowing the AI to make recommendations relevant to the system in which it exists.
The most well-known integrated AI solutions in advertising are tools like Gemini, Google’s AI, which is integrated into Google Ads. Since Gemini is embedded within the Google Ads ecosystem, it can perform tasks across a wide range of functionalities (ad creation, asset generation, reporting, etc.) using data that lives in your Google Ads ecosystem.
A quick note on specialized vs. general-purpose AI
If you want AI solutions designed to help you with niche use cases, like solving specific challenges in advertising, you will want a specialized AI solution. Both point solutions and integrated solutions typically use specialized AI: they are purpose-built to solve specific problems or help with particular tasks.
Specialized AI is considerably different than general-purpose AI tools like ChatGPT. General-purpose AI is, simply put, best for general-purpose tasks. This type of AI can perform a wide range of tasks, typically those that are language-based (for example, summaries, content generation, and basic data analysis).
(You can learn more about the differences between specialized and general-purpose AI in our Ultimate Guide to AI and Automation for Digital Advertising.)
The challenge of point solutions: why generic AI tools fall short in advertising
Because they’re often easier to adopt from a technical perspective, point solutions are among the most common advertising AI solutions available today. However, their convenience and accessibility can exacerbate the fragmented process workflows and siloed strategies that often bog down AdOps teams.
Some of the core challenges that arise with point AI solutions include:
- Lacking context for wider initiatives or processes: By their very nature, point solutions exist as stand-alone tools that rarely interact with other tools in your tech stack. This means they can’t bring other important factors, like different publisher data or company-specific process workflows, into consideration when generating outputs.
- Continuously training with updated information: Even though most AI point solutions can be “trained” with company-specific strategies, terms, or objectives, they must be re-trained every time something changes. Each time you update your launch playbook or new campaign results come in, someone needs to manually bring this data into the point solution. Otherwise, it’ll never know that it needs to change its approach. This constant upkeep can be both time-consuming and tedious.
- An increase in errors, risk, and security concerns from manual data handling: Point solutions often require your team to juggle CSV uploads, manually copy-and-paste AI outputs, and platform-hop to compile data from multiple sources. These manual data workflows increase risk. Misskeyed data, incorrect data sources, or transposing the wrong data into prompts can cause problems ranging from incorrect recommendations to customer data safety breaches.
- Further fragmented workflows that bottleneck AdOps efficiency: Even the best single-point solutions typically require you to manually copy and paste prompt outputs to use them somewhere else. Why? Outputs from point solutions don’t easily feed back into other systems. Using AI for creative content generation or data analysis can save time, but that benefit is hard to justify if you still need to manually transfer the outputs elsewhere.
The benefits of integrated AI for AdOps workflows
Point solutions are useful in tackling isolated problems but they often leave AdOps teams grappling with inefficiencies, fragmented processes, and the added burden of constant oversight. This is where integrated AI steps in.
Integrated AI offers a cohesive, streamlined approach that enriches workflows, eliminates redundancies, and amplifies the impact of your unique data sets. By integrating AI with existing systems, it revolutionizes the way your team works, driving efficiency and smarter decision-making.
To see how integrated AI might work at your organization, let’s look at Muse. Muse is Fluency’s fully integrated AI toolset for AdOps. Since Muse lives inside Fluency’s secure Digital Advertising Operating System (DAOS), it is used within a closed-walled AI architecture with no data egress. Your data (and your clients’ data) stays private and secure in Fluency’s system while still enabling the use of Muse AI to expedite key AdOps processes.
Within those walls, Muse has access to any data across Fluency’s ecosystem. This includes real-time performance data from major ad channels (including Meta, Google, YouTube, Pinterest, plus programmatic DSPs like Basis Technologies), structured data (e.g., data your team uses in formats like CSV, TSV, JSON, XML, etc.), and semi-structured data like RSS feeds. With access to multichannel data and company-specific data sources, you can use Muse to analyze, interpret, and make insightful suggestions across a more holistic strategy.
Muse functions within your team’s existing workflows—budgeting, ad building, campaign/account launching and management, reporting, and more—instead of requiring you to run prompts outside your core tools and then manually activate them. This enables Muse to act as a digital workforce, helping you analyze, create, and take action faster using the data and workflows you already rely on.
Integrated AdOps AI tools like Muse enable:
- Always-on access to real-time data: Context relevance isn’t an issue for integrated AI solutions. Fluency’s integrated AI has full access to your campaign performance history, budgets, KPIs, audience segments, and more. Delivers recommendations based on live, multichannel performance data—not static inputs.
- Unmatched security and governance: By operating within an integrated, closed-walled system, you don’t have to worry about data egress. Integrated AI adheres to the same data security and governance standards as the rest of the data ecosystem it works within, making it easy to promise and adhere to data confidentiality.
- Less platform-hopping and task redundancy: AI that fits within your existing workflows means you spend less time copy-pasting, manually putting insights into action, and managing multiple platforms. With full access to all aspects of your campaigns, integrated AI can help you manage campaign strategies in bulk, end-to-end, in just a few clicks. Your workflows don’t have to be piecemeal and siloed across channels.
- Unified multichannel measurement and learning: Integrated AI tools like Muse are capable of learning and identifying patterns across platforms (Meta, Google, Amazon, etc.), not just one channel at a time. This means you can build, manage, and optimize campaigns with a holistic strategy. It also leads to smarter budget allocations, performance predictions, and creative recommendations over time.
- Speed and efficiency at scale: Integrated AI can help your teams truly do more with less. They don’t have to learn single-purpose tools or rely on multiple sources to inform key decisions. This makes integrated AI solutions ideal for managing large advertising accounts or portfolios.
Vetting AI tools for advertising teams: key questions to ask
As you shop for the right AI tools for your AdOps team, it’s important to think about how AI will fit into your team’s existing work. Cost, ease of use, and access are also important considerations.
Here are some helpful questions you can ask—both of AI vendors and your team—to determine if a specific AI tool is a good fit for your ad operations:
- Does this AI tool integrate or sync with any of my existing AdOps tools, or does it require manual input/output?
- Can this AI access multichannel performance data, our budgeting spreadsheets, copy docs, or other tools we use to build, manage, and optimize campaigns?
- Is the data exchange secure and compliant with my organization’s policies, including those we have with our clients?
- Can this AI tool perform actions for me or my team, or does it provide narrative responses I must manually move or act upon?
- Does the AI learn and improve its work based on usage and feedback?
Building scalable AdOps with the right AI strategy
When it comes to AI in advertising, the choice between point solutions and integrated AI tools carries significant weight for AdOps teams. Choosing the right AI for your advertising operations isn’t just about features: it’s about how AI fits and functions within your environment.
Point solutions can help with one-off tasks, but they often lack the functionality to scale or sustain performance as your business grows. Secure, integrated AI solutions have the potential to completely transform your AdOps team's efficiency. Making AI part of your end-to-end workflow process instead of an isolated step will ultimately drive better decisions, faster execution, and stronger ROI.
Selecting the right AI solution isn’t just about solving today’s operational problems; it’s about paving the way for scalable, sustainable growth.
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