3 PPC priorities to focus on in 2027

There is a slightly strange contradiction at the heart of PPC as we head toward 2027.

Today, advertising platforms have more information than ever about what makes an effective campaign. They have more signals, more computing power, and increasingly sophisticated models for deciding which users to reach, what creative to show them, and how much to bid.

At the same time, because of an increasing reliance on AI automation, you have never had less direct control over many of those decisions.

The direction of travel as we head into 2027 is fairly clear. Google Ads is moving more campaigns towards AI-led systems that make decisions on your behalf about targeting, matching, creative, and bidding.

AI Max is perhaps the most obvious example, with Google already migrating some search campaigns and plans in the works to bring Dynamic Search Ads into the same framework in 2027. Other controls that you’ve traditionally relied upon are disappearing, too.

This isn’t necessarily bad. Most of us would happily give up a few manual controls if the result were better performance.

What it is, however, is a change in the source of strategic advantage.

If the platform increasingly controls the mechanics of media buying, you need to focus on what lies outside those mechanics.

• What information does the platform have about your business?
• Where else can customers be reached?
• How can you understand and challenge what the increasingly automated systems are doing?

For 2027, I would put three priorities at the top of the list: treating data as a competitive advantage, diversifying acquisition channels, and managing complexities with AI.

Priority 1: Treat your data as a competitive advantage

There was a time when a PPC specialist could spend an afternoon making hundreds of small changes to an account and feel reasonably confident that they had materially influenced what happened next.

That world is disappearing. Today, the quality of your inputs matters more when you have less control over the outputs.

As platforms make more decisions automatically, the value of those small manual interventions is diminishing. The more important question is whether the algorithm has been given the right information to make those decisions in the first place. 

Consider a lead generation business. A completed form might be the conversion that Google can see, but it isn’t necessarily the outcome the business cares about. Some leads will be unqualified. Some will never be contacted. Others might become opportunities and eventually customers worth tens of thousands of dollars.

If the bidding system is optimizing around the form submission rather than the commercial outcome, it is being given a very incomplete picture of success.

The same problem exists in ecommerce. Revenue is a better signal than clicks, but profit can be a better signal than revenue. A $500 sale isn’t necessarily more valuable than a $200 sale if the margins are dramatically different. Inventory, repeat purchase behaviour and customer lifetime value can all change the definition of a “good” customer.

This is why first-party data needs to move higher up the PPC agenda.

Build the signal chain, not just the tracking

The practical work here isn’t particularly glamorous, but it is increasingly important.

Advertisers should be looking at whether their conversion tracking is reliable, whether CRM and offline conversion data is making its way back into advertising platforms, whether conversion values actually reflect commercial value, and whether product, margin, and inventory information can be incorporated where appropriate.

It is also worth being honest about the gaps.

A beautifully implemented analytics setup is not necessarily a good bidding setup. The question isn’t just whether you can measure something. It is whether the data you are feeding back to the platform helps it distinguish between valuable and worthless outcomes.

Google has been increasingly explicit about the importance of measurement and first-party data as the foundation for its AI-powered advertising products. That makes sense: The more autonomy you give the system, the more important the quality of its information becomes.

The strategic shift is actually quite simple. When you have less control over the decisions an algorithm makes, you need to have more control over the information it is making those decisions with.

Dig deeper: AI Search is driving customers. Can you measure it?

Priority 2: Stop building a business around one acquisition channel

PPC has been an unusually easy channel to build a business around.

When someone searches for something with obvious commercial intent, being able to put your business directly in front of them is an extraordinarily effective form of marketing. For many businesses, that has made search the natural place to put incremental budget.

The problem is that the customer journey is becoming less predictable.

Diversification is becoming a resilience strategy

People are increasingly discovering products and businesses through social platforms, video, creators, marketplaces, and AI-powered search experiences. The traditional sequence of “search, click, website, conversion” is no longer a particularly reliable description of how every customer discovers and evaluates a business.

That doesn’t mean search is suddenly irrelevant. Far from it. It simply means that relying on it to carry the entire acquisition strategy creates a vulnerability.

Imagine that your cost per acquisition suddenly increases by 30%. Or organic search traffic changes materially. Or a platform introduces a major change to how your campaigns access inventory. Or an AI search experience starts intercepting a meaningful proportion of the queries that previously resulted in clicks.

If almost all of your demand generation sits inside one ecosystem, your options are limited.

Build the ability to move budget

Channel diversification shouldn’t mean blindly spreading budget across every platform available.

The point is to understand what different channels contribute to the customer journey and have enough capability in reserve to increase investment when circumstances change.

For one business, that might mean developing paid social alongside search. For another, it could mean investing in YouTube, email, SEO, partnerships, or a stronger owned audience. For some businesses, emerging AI platforms will eventually become meaningful acquisition channels in their own right.

The exact mix will vary. The principle doesn’t.

A resilient marketing operation shouldn’t need to predict which channel will be the next big winner. It should be capable of moving when the market moves.

That also means getting more sophisticated about measurement. If YouTube creates demand that is later captured by search, last-click reporting will naturally make search look more important than it really is. Conversely, a channel that generates awareness but rarely captures the final conversion can look inefficient if it is judged entirely on direct response.

As customer journeys become more fragmented, understanding the role each channel plays becomes increasingly important. Diversification without that understanding is just spreading the same budget more thinly.

Dig deeper: PPC budget planning: Aligning business goals, ad spend, and performance

Priority 3: Use agentic AI to manage the complexity

There is a temptation to look at the rise of AI agents and conclude that advertisers should simply build an AI system that takes over PPC optimization.

That probably isn’t the most useful way to think about it.

Don’t compete with the platforms at the job they’re already good at

Google has spent years building systems that are very good at making high-frequency bidding and targeting decisions across enormous datasets. Trying to recreate that capability with an AI agent is unlikely to be the best use of an advertiser’s time.

The more interesting opportunity is to put an intelligent layer around those systems.

An agent can pull together information from advertising platforms, analytics, CRM systems, product feeds, and internal business data and use it to investigate what is actually happening. It can monitor performance, spot anomalies, investigate changes in search behavior, check whether tracking is behaving as expected, and surface issues that would otherwise require someone to spend hours digging through reports.

More importantly, it can ask questions that the advertising platform itself isn’t necessarily designed to answer:

• Why did lead quality fall while CPA improved?
• Are we spending more on products that are becoming less profitable?
• Has a change to the website affected the conversion signal being sent back to Google?
• Are we seeing a genuine change in demand or simply a change in the way the platform is attributing conversions?
• Which campaigns are being limited by demand, inventory or budget?

Those are not simply optimization questions. Instead, they are business questions that happen to require advertising data to answer.

The human role changes, rather than disappears

This is where I think the agentic AI conversation is often framed incorrectly.

The opportunity isn’t necessarily to remove the PPC specialist. It is to remove more of the manual investigation that currently consumes their time.

If an agent can continuously monitor an account, identify something unusual, pull together the relevant evidence, and explain what it thinks is happening, then the human can spend more time deciding what should actually be done.

That becomes particularly valuable as the platforms themselves become more automated. The less visibility advertisers have into individual platform decisions, the more useful an independent layer of analysis becomes.

Google is already introducing more agentic capabilities across its advertising and analytics products. Advertisers should expect that trend to continue rather than wait for the platforms to solve every analytical and operational problem for them.

The best use of agentic AI may, therefore, sit around PPC rather than inside the bidding system itself.

Dig deeper: Top AI tools and tactics you should be using in PPC

The PPC advantage in 2027 will come from what sits outside the platform

These three priorities are ultimately connected.

Better data gives the platforms better signals. A more diversified acquisition strategy reduces the risk of becoming dependent on any one source of demand. Agentic AI gives marketers a way to monitor increasingly complex systems and spend less time manually interpreting them.

None of this means the fundamentals of PPC disappear. Understanding customers, economics, creative, and intent will remain important. What changes is where advertisers can exert influence.

For a long time, the competitive advantage in PPC came partly from being better at operating the platform: structuring campaigns, choosing keywords, adjusting bids, and finding the settings that produced an incremental improvement.

That advantage is being steadily absorbed into the platforms themselves.

The next advantage is likely to come from everything surrounding them.

The advertisers best positioned for 2027 will be the ones with better information about their customers, a marketing operation that isn’t dependent on a single source of demand, and the technology to make sense of what is happening across increasingly automated systems.

In other words, the future of PPC may have less to do with controlling the platform and more to do with making sure the platform has something intelligent to work with.

Dig deeper: Claude skills for PPC: How to turn one-off prompts into scalable systems