AI Marketing: Adobe & Google Ads in 2026

Using artificial intelligence in marketing decisions isn’t some far-off idea. It’s the standard operating procedure for 2026, giving us a level of precision in strategic choices that was impossible before. AI-powered marketing automation now gives us real-time campaign adjustments and predictive insights that are frankly mind-boggling compared to just a few years ago. But how do we as consultants actually implement these systems to get clients a return they can see on a spreadsheet?

Step 1: Setting Up AI-Driven Customer Segmentation in Adobe Experience Platform

Good AI marketing starts with a deep, granular understanding of your audience. The days of generic personas are over, by 2026, you need dynamic, AI-fueled segments that constantly adapt. We’ll start this process in Adobe Experience Platform (AEP) because it’s so good at pulling all the customer data together and running machine learning on it.

1.1 Accessing the Audience Builder

First, get logged into your AEP instance. On the main dashboard, look for the navigation pane on the left side and click on “Audiences”. A submenu will pop out. From there, pick “Segments”. You’ll land on a page showing any segments you already have. We’re here to build a new one using AI.

1.2 Creating a New Segment with AI Predictive Scores

1. Once you’re on the “Segments” page, find the big blue “Create Segment” button in the top right corner and click it.
2. The “New Segment” window pops up. Give it a name that makes sense, like “High-Value Churn Risk Q2 2026,” and add a quick description so you remember what it’s for later.
3. Look for the “Segment Definition” area, where you’ll find a bunch of options. Scroll down until you see “AI-Powered Predictive Scores”. This is where you plug in the machine learning.
4. Click “Add Predictive Score”. You’ll get a dropdown menu with pre-trained models or any custom models your team has built. To find people likely to leave, you’d pick a “Churn Likelihood Score.” For finding buyers, you might choose a “Next Best Offer Score.”
5. After you pick a score, the system asks you to set a threshold. For example, if you want to target high-risk customers, you could set the rule to “Churn Likelihood Score is greater than 0.8” (assuming a 0 to 1 scale), which effectively targets the top 20% most likely to churn.
6. You can make this even more powerful by combining predictive scores with other data points using AND/OR logic. A good example is “Churn Likelihood Score > 0.8 AND Last Purchase Date is within the last 90 days,” which helps you focus on recent customers who are unexpectedly at risk.
7. Click “Save”.

Pro Tip: You’ll get much better results if you combine predictive scores. For instance, layer the churn likelihood score with predicted customer lifetime value (CLTV) to pinpoint your most valuable customers who are about to leave, which is perfect for targeted retention campaigns. I see people make this mistake all the time: they create dozens of tiny micro-segments but have no plan for what to do with them. Stick to segments that represent a real, distinct marketing opportunity you can act on.

Expected Outcome: You’ll have a customer segment that updates itself automatically based on AI predictions. This segment is ready to be used in your marketing tools like Adobe Campaign or Adobe Journey Optimizer for truly personalized campaigns.

Step 2: Implementing Dynamic Content Personalization with Adobe Target

After building smart segments, you need to actually personalize the experience for those people. Adobe Target has an AI engine that serves up the right content, offer, or experience to each person in real time, all with the goal of boosting engagement and conversions.

2.1 Creating an A/B Test Activity with AI Allocation

1. Go to Adobe Target from your main Adobe Experience Cloud home screen.
2. On the “Activities” tab, click the “Create Activity” button.
3. Choose “A/B Test” for the activity type. Even though Target has other AI options like “Automated Personalization,” starting with an A/B test that uses AI allocation is a great way to show value and learn what works.
4. Select the channel, like “Web” or “Mobile App,” and enter the URL of the page you want to test on. Click “Next”.
5. The Visual Experience Composer (VEC) will open. This is where you create the different versions of your content, for example, two different headlines for a product page or two different CTA buttons.
6. When your experiences are ready, click “Next” to move on to targeting.
7. In the “Audience” section, find and select the AI-driven segment you made in AEP (like our “High-Value Churn Risk Q2 2026” segment). This makes sure your personalized test is shown to the right people.
8. Now for the important part: under “Traffic Allocation Method,” you must select “AI-driven Personalization.” This tells Target’s machine learning engine to send traffic to the winning variation for each *individual user* as it learns, instead of just splitting traffic 50/50.
9. Set your main goal. This could be “Conversion,” “Revenue,” or just “Engagement.” Target’s AI will automatically optimize your test to improve this specific metric.
10. Click “Save and Go Live”.

Pro Tip: Go beyond just personalizing headlines. You can personalize entire content blocks, product recommendations, or even the page layout itself based on what the AI learns. The real objective is to create a one-to-one journey for each person. A common mistake is ending tests too early, especially with low-traffic segments, before you have statistically significant data. You need to let it run for at least two to four weeks, sometimes longer depending on your site traffic, before you can trust the results.

Expected Outcome: The AI-driven segment you targeted will now see dynamically optimized content. This should lead to higher engagement, better conversion rates, and more revenue for your client. The reports in Adobe Target will clearly show the lift your AI personalization generated compared to a control group.

Step 3: Using AI for Bidding and Placement in Google Ads Performance Max

If you’re a consultant managing paid media, your 2026 workflow is all about letting AI take the wheel instead of tweaking bids by hand. Google Ads Performance Max campaigns are the main tool here, designed to use AI to optimize everything, bids, placements, creative, across all of Google’s channels, all aimed at hitting your specific conversion goals.

3.1 Setting Up a Performance Max Campaign

1. Log into your client’s Google Ads account.
2. In the menu on the left, click “Campaigns”.
3. Click the blue plus icon to start a “New Campaign”.
4. Google will ask you to pick a goal. Choose the one that matches your client’s main objective, like “Leads”, “Sales”, or “Website traffic”.
5. For the campaign type, select “Performance Max”. This is the key to letting Google’s full AI engine do its work.
6. You have to define your conversion goals properly. Double-check them under “Tools and Settings” > “Conversions” to be sure that your primary conversion actions (“Purchases,” “Form Submissions,” etc.) are tracking correctly. Performance Max is completely dependent on accurate conversion data to learn.
7. Set your budget and choose a bidding strategy. With Performance Max, you’ll want to use “Maximize Conversions” or “Maximize Conversion Value”. If you have enough historical data, you can add a target CPA or target ROAS to give the AI some guardrails.
8. Next, build your “Asset Groups.” This is where you upload all your creative elements, headlines, descriptions, images, videos, and logos, that the AI will then mix and match to create ads for every possible placement (Search, Display, YouTube, Gmail, Discover). The more high-quality assets you give it, the better it performs.
9. Provide strong “Audience Signals.” While the campaign is mostly automated, giving the AI some starting hints helps it learn much faster. You can use your AEP segments here by uploading them as a customer match list, or point it to your website visitors and other custom segments.
10. Look over all the settings one last time and then click “Publish Campaign”.

Pro Tip: Let Performance Max do its job without micromanaging it. Its power is in the automation. Your job is to feed it clear goals, great creative assets, and helpful audience signals. The AI needs some freedom to test and learn. I see a lot of people try to manage it like a traditional Search campaign, and it just doesn’t work that way. It’s a completely different animal. You have to trust the machine but also keep a close eye on the high-level KPIs your client cares about.

Expected Outcome: Your client’s ads will run across the entire Google network (Search, Display, YouTube, etc.) with the bids and placements handled by AI to get the most conversions or value for the budget. Over time, as the AI learns, you should see your CPA go down and conversion rates go up.

Step 4: Proactive Customer Churn Prediction with Salesforce Einstein

AI is also incredibly useful for customer retention. Being able to predict which customers are about to churn means you can step in and save the relationship before it’s too late. Salesforce Einstein is a great tool for this because it builds AI directly into the CRM your teams are already using.

4.1 Configuring Einstein Prediction Builder for Churn

1. Log into your Salesforce org.
2. Go to “Setup” (the gear icon in the corner) and use the search bar to find “Einstein Prediction Builder”.
3. Click “New Prediction”.
4. Name your prediction something clear like “Customer Churn Risk” and then choose the object you want to analyze, which is usually the “Account” or “Contact” object.
5. Next, define what you’re trying to predict. For churn, this is typically a custom checkbox field you’ve created called “Is Churned.” Einstein learns by looking at your historical data for accounts where that box is checked versus unchecked.
6. Tell Einstein which records to analyze. You’ll want to filter out old, inactive accounts or any test data. A simple filter might be “Account Status equals ‘Active’.”
7. Now select the fields Einstein should consider when making its prediction. You should include data about customer engagement (like “Last Login Date,” “Support Ticket Count,” “Subscription Tenure”), their purchase history, and demographics. Make sure to exclude the “Is Churned” field itself or anything else that would be a dead giveaway.
8. Review the setup and then let Einstein build the prediction. It will analyze all the data and give you a score showing how good its prediction model is.
9. Once it’s built, you can add the churn score as a field right on the Account or Contact page layouts in Salesforce. This lets your sales and service reps see the risk score in real time.

Pro Tip: Don’t just look at the score. You have to act on it. Connect Einstein’s predictions to Salesforce Marketing Cloud to set up automated retention campaigns. For example, if an account’s churn score goes above 75%, you could automatically add them to a journey that sends a personalized email from their account manager offering help or a special incentive. The score itself is useless if you don’t do anything with the information.

Expected Outcome: Your Salesforce users will see a live “Churn Risk Score” on customer records. This allows the customer success team to get in front of problems with at-risk accounts, which directly leads to lower churn rates and a higher customer lifetime value. It’s no surprise that a recent Statista report projects the AI in customer service market to hit over $18 billion by 2026, and it’s because of practical applications just like this.

Step 5: Monitoring and Iterating AI Performance

A consultant’s job isn’t done after the AI is turned on. You have to constantly monitor and tweak things to get the most value out of it. AI models aren’t static. They need regular evaluation and tuning to stay effective.

5.1 Establishing Key Performance Indicators (KPIs)

Before you launch any AI project, you and your client need to agree on what success looks like. Define clear, measurable KPIs for each initiative. For example:

• For AI Segmentation (Step 1): Compare the conversion rate of campaigns using the AI-generated segments against your old, manually created segments or a control group.
• For Dynamic Personalization (Step 2): Measure the lift in conversion rates, average order value (AOV), and engagement metrics like click-through rate for the personalized tests.
• For Google Ads Performance Max (Step 3): Your main metrics should be Cost Per Acquisition (CPA), Return On Ad Spend (ROAS), and the total number of conversions. You can find these reports in Google Ads under “Reports” > “Performance Max”.
• For Churn Prediction (Step 4): Track the actual reduction in your churn rate among customers that your model flagged as “at-risk” and who received some kind of proactive intervention.

5.2 Conducting Regular Performance Reviews

Set up weekly or bi-weekly check-ins to review the AI’s performance with your client. You have to look past the surface-level metrics. What good is a high click-through rate on a personalized ad if none of those clicks are turning into sales? It might mean the AI is optimizing for the wrong thing. In Google Ads, you can go to “Campaigns”, click your Performance Max campaign, and then select “Insights” to see what the AI is learning and recommending. In Adobe Target, the “Reports” tab gives you all the details on your A/B test results, including confidence levels and revenue lift.

5.3 Iterating and Refining Models

AI models can get worse over time as customer behavior or market conditions change. This is called model drift. For tools like Adobe Experience Platform and Salesforce Einstein, you need to regularly check on the model’s accuracy. Inside Einstein Prediction Builder, you can easily retrain a model with new data if you see its performance start to slip. For Google Ads Performance Max, this means refreshing your asset groups with new ad creative and making sure your conversion goals are still aligned with the business’s priorities. I always tell my clients to think of AI as an ongoing optimization process, not a one-and-done project. As the IAB often says, AI in marketing is an iterative loop, not a ‘set it and forget it’ button.

Expected Outcome: By constantly monitoring and making adjustments, the AI systems will get smarter and more accurate over time. This leads to long-term improvements in marketing efficiency and better results. This iterative work is what keeps the AI aligned with your client’s business goals as they change.

Using AI in marketing decisions is a must for any consultant who wants to provide real, measurable value today. By methodically setting up tools like Adobe Experience Platform, Adobe Target, Google Ads Performance Max, and Salesforce Einstein, and then keeping a close eye on how they perform, consultants can bring a whole new level of precision and efficiency to their clients’ marketing. For a deeper look at this, check out the guide on AI Email Marketing: 18% CPL Reduction in 2026, which details specific ways to lower your cost-per-lead. It’s also helpful to understand the bigger picture with Marketing Consultants: 2026 Strategy Shifts You Need, which provides context for where these AI tactics fit. And of course, make sure all your work is ethical and above board by reviewing our guide on Cybersecurity Compliance: Marketing Ethics in 2026.