Last updated: February 10, 2026
The pitch has barely changed in a decade. "Save 10 hours a week on PPC management." "Automated recommendations, one-click implementation." "Let our AI do the heavy lifting so you can focus on strategy." Every PPC optimization tool on the market, from Optmyzr to WordStream to Opteo to Adalysis, makes some version of this promise. And on the surface, they deliver. You do get recommendations. You do save some time. Some of the one-click optimizations genuinely improve performance.
But here is what none of them will tell you: you are still paying for a human to run the show.
The tool flags that your Quality Score dropped on three keywords. Someone has to evaluate whether that matters. The tool recommends pausing five ad groups with declining CTR. Someone has to decide if the decline is seasonal or structural. The tool suggests 47 new negative keywords based on search term analysis. Someone has to review them to make sure none of them would block converting queries. The tool alerts you that a campaign exceeded its daily budget by 2pm. Someone has to decide whether to increase the budget, redistribute spend, or leave it alone.
That someone is a human. It might be you. It might be an employee. It might be an agency. But they are there, and they are expensive. The tool did not eliminate them. It made them slightly more efficient. And in 2026, as Google's own AI makes campaign management more complex rather than simpler, "slightly more efficient" is no longer good enough.
Let us be precise about what semi-autonomous PPC tools actually do, because the marketing language is deliberately vague. These platforms scan your Google Ads data, identify potential optimizations, and present them to you as recommendations. Some recommendations can be applied with a single click. Others require configuration. All of them require a human to review and approve before anything happens.
This is the "recommendation engine" model, and it is the dominant paradigm in PPC software. Optmyzr calls them "optimizations you can apply with one click." WordStream built its entire brand around the "20-Minute Work Week" concept. Opteo presents an "improvement inbox" of prioritized suggestions. Adalysis surfaces audit findings and A/B test results. The language is different but the architecture is identical: the software analyzes, the human decides.
The capabilities of modern PPC tools are genuinely impressive in isolation. Bid adjustment suggestions based on historical performance data. Budget pacing alerts when campaigns are spending too fast or too slow. Negative keyword identification from search term analysis. Quality Score monitoring and decline notifications. Ad copy performance comparisons and rotation recommendations. Account audits that check for common structural issues. Reporting dashboards that aggregate data across campaigns. Rule-based automation that executes predefined actions when specific conditions are met (Optmyzr's Rule Engine, for example).
These are all valuable features. They catch problems faster than a human manually checking every metric. They surface patterns that might take hours of spreadsheet analysis to identify. They standardize processes that would otherwise depend on individual expertise.
Here is the list that matters more. Semi-autonomous PPC tools cannot decide whether a recommendation is appropriate for your specific business context. They cannot evaluate whether pausing a campaign aligns with your quarterly revenue goals. They cannot determine if a negative keyword suggestion would block queries that convert through a different attribution path. They cannot assess whether an ad copy recommendation fits your brand voice. They cannot decide how to restructure a campaign when the current structure has fundamentally stopped working. They cannot recognize that a performance decline is caused by a competitor's new product launch rather than a campaign configuration issue. They cannot adjust strategy in response to changes in your business: new product launches, pricing changes, market shifts, seasonal promotions.
In other words, they cannot do the things that actually drive performance outcomes. They can tell you what might be wrong. They cannot fix it. That is your job. Or your team's job. Or your agency's job.
The PPC tool industry has successfully repositioned "recommendation engine" as "automation," and this linguistic sleight of hand obscures the real cost structure. Here is what businesses actually pay when they adopt a semi-autonomous PPC tool:
The tool itself costs between $99 and $600 per month depending on the platform and your ad spend. Optmyzr starts at $249/month. WordStream's paid plans start around $49/month but scale up significantly. Opteo begins at $99/month. Adalysis starts at $149/month.
The human who uses the tool costs dramatically more. If you hire an in-house PPC specialist, you are looking at $55,000 to $85,000 per year in the US (roughly $4,500 to $7,000 per month when you include benefits and overhead). If you outsource to an agency, management fees typically run 10-20% of ad spend or $500 to $5,000+ per month as a flat fee. If you do it yourself as a business owner, the opportunity cost of your time spent reviewing recommendations instead of running your business is incalculable but very real.
The tool's promise was to reduce the human cost. And it does, by maybe 25-35%. An Optmyzr user might spend 8.5 hours per week managing campaigns instead of 12 hours without the tool. A WordStream user might spend 20 minutes per week on basic optimizations (hence the "20-Minute Work Week" branding) but still needs hours of strategic work on top of that. The human is still there. They are still expensive. They are just slightly less busy.
For a business spending $10,000 per month on Google Ads, the total management cost under the semi-autonomous model looks something like this: PPC tool subscription at $250/month, plus agency management at $1,500/month (15% of spend), equals $1,750/month or $21,000 per year in management overhead. The tool saved the agency some time, which means they can manage more clients, but the cost to you has not fundamentally changed.
This is the economics of semi-autonomy. It is marginally better than manual management. It is not transformative. And in 2026, marginal improvements are losing ground.
The semi-autonomous tool model made sense in 2018 when Google Ads was primarily a keyword management game. You chose keywords, wrote ad copy, set bids, and monitored performance. The variables were manageable. A competent human could stay on top of a medium-sized account in 5-10 hours per week, and tools that automated the routine parts of that work provided genuine time savings.
Between 2018 and 2026, Google systematically made the job more complex, not less. Every major update added new dimensions that need management, new settings that need configuration, new campaign types that interact in non-obvious ways, and new AI features that generate unpredictable behavior.
In 2018, exact match meant exact match. In 2026, exact match means "semantically related queries that Google's AI considers equivalent in intent." Phrase match is even looser. Broad match, which Google now aggressively promotes through Smart Bidding and AI Max, matches queries that are only thematically related to your keywords. This means that keyword management is no longer about choosing the right words. It is about continuously monitoring what Google's AI does with those words and correcting course when the matching goes wrong. The volume of search terms reports has exploded. The number of irrelevant matches has multiplied. A semi-autonomous tool can flag some of these, but the sheer volume of decisions required has overwhelmed the "review and approve" model.
AI Max for Search, rolled out through 2025, takes your existing Search campaigns and automatically expands keyword matching, generates dynamic ad copy variations, and selects landing pages. Google reports a 14% average conversion improvement, but independent testing shows 84% of advertisers reporting neutral or negative results. The difference comes down to how well the account is prepared: comprehensive negative keywords, quality conversion tracking, strong landing pages.
Managing AI Max effectively requires near-constant monitoring. The feature generates new query patterns daily. Some are valuable. Many are not. A tool can surface these patterns in a report. But evaluating whether each new pattern represents opportunity or waste requires human judgment applied at a frequency that most humans simply cannot sustain. Weekly reviews are too slow. Daily reviews are too time-consuming. The semi-autonomous model breaks down when the volume of decisions exceeds what the human in the loop can reasonably process.
Performance Max serves ads across Search, Shopping, Display, YouTube, Gmail, Discover, Maps, and Waze. Managing PMax effectively requires understanding cross-channel attribution, creative asset optimization across multiple formats, audience signal management, search theme configuration, negative keyword strategy (now up to 10,000 per campaign), and the interplay between PMax and Search campaigns that compete for the same queries.
In 2025, Performance Max gained channel reporting, search term visibility, expanded negative keywords, and doubled search themes. These transparency improvements are welcome, but they also mean more data to analyze, more settings to configure, and more optimization decisions to make. Each new control is another lever that needs active management. Semi-autonomous tools can surface the data. They cannot make the cross-channel strategic decisions that the data demands.
This is the uncomfortable truth that PPC tool vendors rarely discuss. Google's AI features, including Smart Bidding, AI Max, Performance Max, and broad match expansion, are designed to maximize conversions within your budget. But Google generated $296 billion in advertising revenue in 2025. The platform's economic incentives are structurally misaligned with your goal of minimizing cost per acquisition.
Smart Bidding optimizes for the conversion actions you define, but it has no mechanism for questioning whether those conversion actions accurately represent business value. It will happily optimize toward low-quality form fills if that is what you are tracking. AI Max will aggressively expand matching to queries that generate clicks, even if those clicks rarely convert. Performance Max will distribute budget to channels where Google has excess inventory, not necessarily where your customers are most likely to convert.
Managing these tensions requires an optimization layer that is independent of Google's own AI, one that evaluates what Google is doing and corrects course when the platform's behavior diverges from your business interests. Semi-autonomous tools can flag anomalies. They cannot actively counterbalance Google's incentive misalignment in real time. That requires an autonomous system making continuous decisions, not a recommendation engine waiting for human approval.
The fundamental limitation of the semi-autonomous model is not that the tools are bad. They are not. Optmyzr, Opteo, Adalysis, and others are well-engineered platforms built by people who understand PPC deeply. The limitation is architectural. They were designed to assist humans, not replace them. Every feature assumes a human in the loop reviewing, deciding, and approving. This design choice was reasonable when the complexity of Google Ads was manageable by humans. In 2026, it is becoming a bottleneck.
Full autonomy means something fundamentally different. It does not mean "better recommendations." It does not mean "faster alerts." It does not mean "one-click implementation." It means the system makes the decisions, implements the changes, monitors the results, and adjusts continuously without any human reviewing or approving individual actions.
A semi-autonomous tool might identify that your campaign's CPA has risen 23% over the past week and surface this as an alert with three recommended actions. A human then evaluates the recommendations, decides which to implement, and clicks the button. Total time from problem detection to resolution: somewhere between 4 hours (if the human is responsive) and 7 days (if they check recommendations weekly).
An autonomous system identifies the CPA increase, diagnoses the likely cause (increased competition on three keywords, one ad copy variant underperforming, and a shift in device mix toward mobile where your landing page converts poorly), implements a coordinated response across all affected levers simultaneously (adjusting bids on the problematic keywords, reallocating budget toward better-performing campaigns, pausing the underperforming ad variant, and applying device bid modifiers), and monitors whether the changes resolve the problem within hours rather than days.
The difference is not just speed. It is the ability to make coordinated decisions across multiple optimization levers simultaneously. A human reviewing recommendations tackles them sequentially: first address the bid issue, then the ad copy issue, then the budget issue, each in isolation. An autonomous system addresses them as the interdependent variables they actually are.
This is the most important distinction, and it is the one that semi-autonomous tool vendors work hardest to obscure. With semi-autonomous tools, you reduce the human's workload by maybe 25-35%. You still need the human. Their salary still appears on your expense sheet. Their capacity still limits how many accounts they can manage. Their attention still degrades over time as they get pulled into other priorities.
With full autonomy, the human cost of day-to-day campaign management goes to zero. Not "almost zero." Not "significantly reduced." Zero. There is no one reviewing recommendations because there are no recommendations to review. There is no one approving changes because changes do not need approval. There is no one conducting weekly account audits because audits happen continuously.
The human's role shifts entirely from tactical execution (reviewing, approving, implementing) to strategic oversight (defining business goals, evaluating overall performance, making high-level decisions about budget allocation and market focus). This is a fundamentally different job that requires less time, different skills, and creates more value.
For the business spending $10,000 per month on Google Ads, the comparison becomes stark. Semi-autonomous model: $250/month for the tool plus $1,500/month for agency management equals $1,750/month. Autonomous model: a single platform fee that replaces both the tool and the agency. The savings are not incremental. They are structural.
A natural question is whether existing semi-autonomous tools could add autonomous capabilities and close the gap. Some are trying. Most will fail. Here is why.
Semi-autonomous tools were built for users who want control. Their entire user experience, their marketing, their product roadmap, everything assumes a human who wants to see what is happening and make decisions. Adding autonomous capabilities to these platforms means asking their users to relinquish the very control that drew them to the platform in the first place. This is not a feature addition. It is a fundamental product repositioning that risks alienating the existing customer base.
Making good autonomous decisions requires a different data architecture than making good recommendations. A recommendation engine needs to surface potential optimizations and present them clearly. An autonomous system needs to model the expected impact of every possible action across interdependent variables, execute the highest-value combination, measure the actual impact, and feed those results back into the model for continuous learning. This is not a UI change. It is a complete reengineering of the underlying system.
When a tool makes a recommendation and a human approves it, the human bears responsibility for the outcome. When an autonomous system makes and implements a decision independently, the platform bears responsibility. This changes the legal, financial, and reputational dynamics significantly. Semi-autonomous tool vendors have deliberately avoided this responsibility by keeping humans in the loop. Moving to autonomy means accepting accountability for outcomes, which most PPC tool companies are unwilling or unable to do.
Building a recommendation engine requires understanding what "good" looks like and being able to identify when metrics deviate from it. Building an autonomous system requires understanding the causal relationships between every optimization lever and every performance outcome, and being able to predict the second and third-order effects of every change. These are categorically different engineering challenges. Teams that are excellent at building recommendation engines are not necessarily equipped to build autonomous decision-making systems.
The core issue with semi-autonomous tools is that they optimize individual levers in isolation when the levers are actually deeply interconnected. Google Ads performance is determined by the interaction of at least twelve major optimization levers: bidding strategy, keyword management, negative keywords, ad copy, landing pages, budget allocation across campaigns, campaign structure, audience targeting, search themes, creative asset testing, conversion tracking configuration, and device/location/schedule bid adjustments.
Every change to one lever affects the others. Adjusting bids changes which auctions you win, which changes your keyword mix, which changes which ad copy serves, which changes your Quality Score, which changes your CPC, which changes your budget consumption rate, which changes your impression share, which affects everything else.
Semi-autonomous tools typically optimize 2-4 of these levers reasonably well. Optmyzr excels at rule-based bid management and budget pacing. WordStream focuses on keyword recommendations and basic ad copy suggestions. Opteo is strong on Quality Score monitoring and incremental optimizations. But none of them address all twelve levers simultaneously, and none of them model the interactions between levers.
The result is what you might call "optimization whack-a-mole." You fix the bidding problem and it creates a budget pacing problem. You fix the budget pacing problem and it surfaces a keyword quality problem. You fix the keyword quality problem and it changes which ad variants serve, creating an ad copy problem. Each fix is locally correct but globally suboptimal because no one (and no tool) is considering the full system.
groas was designed from the ground up to manage all twelve levers as a single integrated system. When groas adjusts bids, it simultaneously considers the impact on budget allocation, keyword priority, ad copy selection, and audience targeting. When it adds negative keywords, it accounts for how the changed query mix will affect Smart Bidding's learning, budget consumption, and impression share. This is not a feature of groas. It is the fundamental architecture. The system was not built to make recommendations about individual levers. It was built to optimize the entire account as a single, interconnected system.
The implications of the shift from semi-autonomous to autonomous differ depending on your current setup.
You are spending hours per week on campaign management that an autonomous system could handle better and faster. Every hour you spend reviewing search terms reports, adjusting bids, testing ad copy, and monitoring budgets is an hour you are not spending on product development, customer relationships, hiring, or the other activities that actually grow your business. Semi-autonomous tools reduce this time somewhat but do not eliminate it. Full autonomy gives you that time back entirely.
The typical small business owner managing their own Google Ads spends 5-15 hours per week on campaign management, depending on complexity. At even a conservative estimate of $50/hour for a business owner's time, that is $1,000 to $3,000 per month in opportunity cost. An autonomous platform that costs a fraction of that and delivers better results is not just a tool upgrade. It is a business model improvement.
Your agency is almost certainly using semi-autonomous tools internally to manage your account more efficiently. This is fine for the agency's economics (they can handle more clients per account manager) but does not necessarily improve your outcomes. The account manager is still the bottleneck. They are still reviewing your account weekly or biweekly. They are still making decisions on a schedule that cannot keep pace with how quickly Google's AI changes campaign behavior.
The question to ask is not whether your agency uses good tools. It is whether the inherent latency of the human-in-the-loop model, even with the best tools available, is costing you performance. In most cases, the answer is yes. Agencies add value through strategic thinking, creative development, and cross-channel coordination. Day-to-day campaign optimization is where the human-in-the-loop model breaks down, and it is precisely the part that autonomous AI handles better.
This is the most disruptive scenario, and it deserves honest acknowledgment. Autonomous AI platforms like groas do not just change how campaigns are managed. They challenge the economic model of PPC agencies that charge management fees for the labor of reviewing accounts, implementing changes, and producing reports.
The agencies that will thrive are those that shift their value proposition from tactical campaign management (reviewing, approving, implementing) to strategic consulting (business goal alignment, creative strategy, conversion optimization, full-funnel marketing planning). The labor of button-pushing inside Google Ads is being automated. The strategic thinking that determines which buttons should be pushed is more valuable than ever.
To make this concrete, here is how the two models handle five common Google Ads scenarios.
Scenario 1: A keyword's CPC spikes 40% overnight due to a new competitor.
Semi-autonomous tool: Surfaces an alert in the next daily or weekly report. Recommends bid adjustments. Human reviews, evaluates the competitor situation, decides whether to compete on price or shift budget elsewhere. Implementation happens hours to days later.
groas: Detects the CPC spike within hours. Analyzes the competitor's entry point, evaluates the keyword's conversion rate at the new CPC level, models the impact of alternative strategies (bid adjustment, budget reallocation, keyword pausing, ad copy differentiation), selects the optimal response, implements it, and monitors the outcome. Total elapsed time: hours, not days.
Scenario 2: Search term reports reveal a new pattern of irrelevant queries wasting $50/day.
Semi-autonomous tool: Identifies the pattern during the next scheduled search term review (weekly or monthly). Recommends negative keywords. Human reviews and approves. Pattern was wasting $50/day for 7-30 days before being addressed: $350 to $1,500 in preventable waste.
groas: Identifies the pattern within 24-48 hours of it emerging. Adds appropriate negative keywords with the right match types at the right level. Waste is stopped within days of starting: $100-150 in preventable waste versus $350 to $1,500 with the semi-autonomous model.
Scenario 3: An ad copy variant starts underperforming after two weeks of strong results.
Semi-autonomous tool: The ad testing module flags declining performance in the next reporting cycle. Recommends pausing the variant. Human reviews the data, considers whether the decline is real or noise, and decides. If they wait for statistical significance (as they should), the decline continues for another 1-2 weeks before action.
groas: Continuously monitors ad variant performance and detects the shift in real time. Evaluates whether the decline is statistically significant, checks for external factors (seasonality, day-of-week patterns, audience fatigue), and either reduces the variant's serving frequency or replaces it with a new test variant. No waiting for human review cycles.
Scenario 4: Performance Max starts cannibalizing Search campaign conversions.
Semi-autonomous tool: This is extremely difficult to detect through standard reporting. Most semi-autonomous tools do not even monitor cross-campaign cannibalization as a specific metric. The human would need to manually compare search terms across PMax and Search, correlate conversion changes, and identify the overlap. Most never do.
groas: Continuously monitors the relationship between PMax and Search at the query level. Detects cannibalization patterns as they emerge. Adds appropriate negative keywords to PMax, adjusts search themes, or reallocates budget between campaign types to maintain optimal coverage without overlap.
Scenario 5: Weekend conversion quality drops significantly compared to weekdays.
Semi-autonomous tool: If configured to monitor day-of-week performance, surfaces the pattern in a report. Recommends schedule bid adjustments. Human reviews and implements. Most tools do not automatically connect this to the downstream question of whether weekend conversions are actually lower quality or just lower volume.
groas: Identifies the day-of-week pattern, analyzes whether the issue is conversion quality (measured by downstream metrics like lead-to-sale rate or return rate) or just volume, and implements the appropriate response. If weekend leads convert to sales at a lower rate, groas reduces weekend bids or reallocates weekend budget to top-performing campaigns. If the issue is volume, not quality, it leaves the schedule alone. This distinction matters enormously for ROI but requires the kind of multi-signal analysis that most tools and most humans skip.
The PPC tool market is at an inflection point. The semi-autonomous model served advertisers well for nearly a decade. It took the complexity of Google Ads and made it more manageable for humans. But Google's platform has evolved beyond what the "managed by humans, assisted by tools" model can handle efficiently. The variables are too numerous. The interactions too complex. The pace of change too fast. The volume of data too large.
Google itself is pushing toward more automation with every update. AI Max, broad match expansion, Performance Max, automated creative generation, and the continued erosion of manual controls all point in the same direction. Google wants advertisers to trust the AI. The problem is that Google's AI serves Google's interests, not yours.
The answer is not to resist automation. That ship has sailed. The answer is to deploy independent automation that serves your interests: AI that watches what Google's AI does and ensures it aligns with your business goals, that catches the waste Google's platform generates, that optimizes the levers Google does not touch, and that does all of this continuously without waiting for a human to review a weekly report.
groas is the platform that fully closes this loop. Not "mostly closes it." Not "helps you close it faster." Fully closes it. The twelve optimization levers, managed simultaneously, 24/7, with the kind of coordinated decision-making that no human team and no recommendation engine can replicate.
The semi-autonomous model was a bridge. It got us from manual management to the doorstep of autonomy. In 2026, it is time to walk through the door.
Semi-autonomous PPC tools like Optmyzr, WordStream, Opteo, and Adalysis scan your Google Ads account data, identify potential optimizations, and present them as recommendations. Most offer one-click implementation for certain changes (bid adjustments, negative keyword additions, ad pausing). Some provide rule-based automation that executes predefined actions when specific conditions are met. However, all of them require a human to review recommendations, approve changes, set strategy, and handle edge cases. They reduce the workload of managing Google Ads but do not eliminate the need for human management.
The typical time savings from semi-autonomous PPC tools is 25-35% compared to fully manual management. An account manager using Optmyzr might spend 8-9 hours per week managing campaigns instead of 12 hours without the tool. WordStream's "20-Minute Work Week" covers basic optimizations but does not account for the strategic analysis, search term review, ad copy testing, and structural decisions that still require human time. The tools save time on routine tasks but do not eliminate the need for a dedicated human managing the account.
Google's built-in automation (Smart Bidding, AI Max, Performance Max) is optimized for Google's interests, not yours. Google generated $296 billion in advertising revenue in 2025. The platform profits when you spend more. Smart Bidding optimizes for conversions as you define them but has no mechanism for questioning whether those conversions represent real business value. AI Max expands matching to generate more clicks, not necessarily better clicks. Performance Max distributes budget across channels where Google has inventory to fill. You need an independent optimization layer that evaluates what Google is doing and corrects course when it diverges from your goals.
Fully autonomous means the system makes optimization decisions, implements changes, monitors results, and adjusts continuously without requiring a human to review or approve individual actions. It does not mean "no human involvement at all." You still set business goals, define budgets, approve creative direction, and evaluate overall performance. But the day-to-day work of bid management, keyword optimization, negative keyword updates, ad copy testing, budget allocation, and campaign structure adjustments happens automatically. Your role shifts from tactical execution to strategic oversight.
Any optimization system, human or AI, can make mistakes. The question is how quickly mistakes are caught and corrected. A human reviewing accounts weekly might not catch a problem for 7 days. An autonomous system monitoring continuously catches and corrects issues within hours. groas also operates with safety parameters: spending limits, performance thresholds, and anomaly detection that prevents any single change from causing outsized damage. In practice, the risk of autonomous AI making a costly mistake is lower than the risk of a human missing a problem during their weekly review.
The fundamental difference is architectural. Optmyzr and WordStream are recommendation engines: they analyze your account and suggest changes for a human to review and approve. groas is an autonomous system: it analyzes your account, makes decisions, implements changes, and monitors results without human approval for day-to-day optimizations. Optmyzr and WordStream optimize 2-4 levers and leave the rest to the human. groas optimizes all twelve major levers simultaneously and models the interactions between them. The result is faster response times, more coordinated optimization, and the elimination of the human management cost.
Not for day-to-day campaign management. groas handles bidding, keywords, negatives, ad copy testing, budget allocation, campaign structure, audience targeting, and all other tactical optimization autonomously. You or someone on your team should still define business objectives, set overall budgets, provide creative direction, and review high-level performance reports. But this is a strategic oversight role that requires a few hours per month, not a full-time PPC management role that requires hours per week.
This is actually one of the strongest advantages of autonomous AI over human management. When Google releases a major update (as it did repeatedly throughout 2025 with AI Max, PMax negative keyword expansion, channel reporting, and search theme expansion), groas adapts its optimization strategies automatically. A human manager needs to learn about the change, understand its implications, adjust their processes, and implement new workflows. This can take weeks or months. groas incorporates platform changes into its optimization models as they roll out.
groas clients consistently report 25-40% improvements in key performance metrics (lower CPA, higher ROAS, reduced wasted spend) compared to their previous management approach, whether that was self-management, agency management, or management with semi-autonomous tools. The improvement comes from three sources: faster response to problems (hours versus days or weeks), coordinated optimization across all twelve levers (versus sequential, isolated optimization), and continuous operation (versus periodic review cycles). These are structural advantages of the autonomous model, not incremental feature improvements.
Not immediately, but their market position will increasingly narrow. Semi-autonomous tools will remain relevant for large agencies managing hundreds of accounts who need workflow tools and reporting infrastructure, for experienced PPC specialists who genuinely want granular manual control, and for specific use cases where custom rule-based automation is valuable. For the majority of advertisers (businesses managing their own accounts, small agencies, in-house teams without dedicated PPC expertise), autonomous platforms represent a fundamentally better economic model: better results at lower total cost with less human time required.
