The Future of Search: How Service-Based Businesses Can Prepare for 2025
Patrick McFadden • December 6, 2024
The search landscape is undergoing significant changes, and service-based businesses—like commercial cleaning companies, electricians, remodelers, and repair services—are feeling the impact. Google is prioritizing paid placements, while AI tools like ChatGPT are changing how consumers find and choose providers. These shifts are creating challenges for businesses relying on traditional search strategies, but they also present new opportunities for those willing to adapt. Here’s what’s happening and how to prepare for what’s coming in 2025.
What’s Changing in Search
- Google’s Prioritization of Paid Ads
- Local Service Ads (LSAs): These are now dominating the top of search results, especially for service industries like cleaning, electricians, and remodelers. LSAs prioritize businesses based on proximity, reviews, and ad spend, making organic rankings less impactful.
- Ads in the Map Pack: Google is integrating paid ads directly into the local map pack, further reducing visibility for organic listings.
- Generative AI in Search Results: AI-powered summaries often highlight paid advertisers and well-optimized businesses, leaving little space for organic results.
- Impact on Service-Based Businesses:
- Companies relying solely on organic rankings are seeing fewer inquiries.
- Increased competition in paid ads is driving up costs, making it harder for smaller businesses to compete.
- The Rise of AI Tools Like ChatGPT
- AI platforms are becoming a go-to resource for consumers researching services. These tools prioritize brand reputation and authority over traditional SEO factors like keywords and proximity.
- Impact on Service-Based Businesses:
- Businesses with a strong reputation and recognizable brand are more likely to be recommended by AI platforms.
- Those relying only on Google’s keyword-based ranking systems may struggle to stay visible.
How Service Businesses Can Prepare for 2025
1. Invest in Targeted Advertising
Paid advertising is a short-term solution to maintain visibility in an increasingly competitive online space. Here’s how different service businesses can use ads effectively:
- Commercial Cleaning Companies:
- Use Local Service Ads to target office managers and facility owners in your service area.
- Focus on Google Ads for terms like "daily office cleaning" or "floor waxing services near me."
- Electricians (Residential and Commercial):
- Promote emergency services through PPC campaigns targeting "24-hour electrician near me."
- Use LSAs to target high-intent customers for specific services like rewiring or panel upgrades.
- Remodelers:
- Target kitchen and bathroom renovation keywords in your local area through PPC.
- Highlight high-ticket services like custom home additions or luxury remodels.
- Installation/Repair Companies:
- Run ads promoting seasonal services (e.g., HVAC repairs in winter, appliance installation for holidays).
Takeaway: Small investments in LSAs and PPC ensure your business stays visible while preparing for longer-term solutions.
2. Build a Brand That Works Across Google and AI
Branding is critical for service-based businesses looking to reduce dependency on paid ads and thrive in the evolving search ecosystem. When customers recognize your business name, they’ll search for you directly, bypassing the competition.
Here’s how to build a strong brand:
- Showcase Expertise Through Educational Content
- Create e-books or downloadable guides like:
- "5 Questions to Ask Before Hiring a Commercial Cleaning Company."
- "The Ultimate Guide to Choosing the Right Electrician for Your Business."
- "10 Signs You Need a Bathroom Remodel."
- Post blogs or social media content addressing common customer questions.
- Leverage Video Content
- For commercial cleaning: Show before-and-after videos of deep cleaning projects.
- For electricians: Film a walkthrough of an electrical upgrade explaining the process.
- For remodelers: Highlight completed projects with a virtual home tour.
- For installation/repair companies: Create tutorials or maintenance tips to build trust.
- Encourage Reviews and Referrals
- Launch referral programs offering discounts for successful recommendations.
- Ask satisfied customers to leave detailed reviews on Google and other platforms.
- Engage in Community Activities
- Sponsor local events or participate in trade shows to increase visibility.
- Partner with complementary businesses (e.g., real estate agents, interior designers) to expand your reach.
Takeaway: A strong brand ensures your business ranks well in AI searches while also attracting direct inquiries.
3. Diversify Lead Generation Channels
While Google remains a key player, diversifying how you generate leads reduces your dependency on any single platform. Here’s how service-based businesses can diversify effectively:
- Social Media Presence
- Use Facebook and Instagram to run targeted ads featuring testimonials and completed projects.
- Post short-form content on TikTok or YouTube Shorts to engage a broader audience.
- Email Marketing
- Send newsletters highlighting special promotions, seasonal services, or recent successes.
- Optimize Alternative Platforms
- Ensure your business is well-represented on Yelp, Angie’s List, and other industry-specific directories.
Takeaway: A multi-channel strategy protects your business from over-reliance on Google’s changing algorithms.
Everyone is scaling outputs. Almost no one is scaling judgment.
Ask anyone in tech where AI is headed, and they’ll tell you: “The next leap is reasoning.” “AI needs judgment.” “We need assistants that think, not just answer.” They’re right. But while everyone’s talking about it, almost no one is actually shipping it. So we did. We built Thinking OS™ —a system that doesn’t just help AI answer questions… It helps AI think like a strategist. It helps AI decide like an operator. It helps teams and platforms scale judgment, n ot just generate output. The Theory Isn’t New. The Implementation Is. The idea of layering strategic thinking and judgment into AI isn’t new in theory. The problem is, no one’s been able to implement it effectively at scale. Let’s look at the current landscape. 1. Big Tech Has the Muscle—But Not the Mind OpenAI / ChatGPT ✅ Strength: Best-in-class language generation ❌ Limitation: No built-in judgment or reasoning. You must provide the structure. Otherwise, it follows instructions, not strategy. Google DeepMind / Gemini ✅ Known for advanced decision-making (e.g., AlphaGo) ❌ But only in structured environments like games—not messy, real-world business scenarios. Anthropic (Claude), Meta (LLaMA), Microsoft Copilot ✅ Great at answering questions and following commands ❌ But they’re assistants, not advisors. They won’t reprioritize. They won’t challenge your assumptions. They don’t ask: “Is this the right move?” These tools are powerful—but they don’t think for outcomes the way a strategist or operator would. 2. Who’s Actually Building the Thinking Layer™? This is where it gets interesting—and thin. Startups and Indie Builders Some small teams are quietly: Creating custom GPTs that mimic how experts reason Layering in business context, priorities, and tradeoffs Embedding decision logic so AI can guide, not just execute But these efforts are: Highly manual Difficult to scale Fragmented and experimental Enterprise Experiments A few companies (Salesforce, HubSpot, and others) are exploring more “judgment-aware” AI copilots. These systems can: Flag inconsistencies Recommend next actions Occasionally surface priorities based on internal logic But most of it is still: In early R&D Custom-coded Unproven beyond narrow use cases That’s Why Thinking OS™ Is Different Instead of waiting for a lab to crack it, we built a modular thinking system that installs like infrastructure. Thinking OS™: Captures how real experts reason Embeds judgment into layers AI can use Deploys into tools like ChatGPT or enterprise systems Helps teams think together, consistently, at scale It’s not another assistant. It’s the missing layer that turns outputs into outcomes. So… Is This a New Innovation? Yes—in practice. Everyone says AI needs judgment. But judgment isn’t an idea. It’s a system. It requires: Persistent memory Contextual awareness Tradeoff evaluation Value-based decisions Strategy that evolves with goals Thinking OS™ delivers that. And unlike the R&D experiments in Big Tech, it’s built for: Operators Consultants Platform founders Growth-stage teams that need to scale decision quality, not just content creation If Someone Told You They’ve Built a Thinking + Judgment Layer™… They’ve built something only a handful of people in the world are even attempting. Because this isn’t just AI that speaks fluently. It’s AI that reasons, reflects , and chooses. And in a world that’s drowning in tools, judgment becomes the differentiator. That’s the OS We Built Thinking OS™ is not a prompt pack. It’s not a dashboard. It’s not a glorified chatbot. It’s a decision architecture you can license, embed, or deploy— To help your team, your platform, or your clients think better at scale. We’ve moved past content. We’re building cognition. Let’s talk.
In every era of innovation, there’s a silent bottleneck—something obvious in hindsight, but elusive until the moment it clicks. In today’s AI-driven world, that bottleneck is clear: AI has speed. It has scale. But it doesn’t have judgment . It doesn’t really think . What’s Actually Missing From AI? When experts talk about the “thinking and judgment layer” as the next leap for AI, they’re calling out a hard truth: Modern AI systems are powerful pattern machines. But they’re missing the human layer—the one that reasons, weighs tradeoffs, and makes strategic decisions in context. Let’s break that down: 1. The Thinking Layer = Reasoning with Purpose This layer doesn’t just process inputs— it structures logic. It’s the ability to: Ask the right questions before acting Break down complexity into solvable parts Adjust direction mid-course when reality changes Think beyond “what was asked” to uncover “what really matters” Today’s AI responds. But it rarely reflects. Unless told exactly what to do, it won’t work through problems the way a strategist or operator would. 2. The Judgment Layer = Decision-Making in the Gray Judgment is the ability to: Prioritize what matters most Choose between imperfect options Make decisions when there’s no clear answer Apply values, experience, and vision—not just data It’s why a founder might not pursue a lucrative deal. Why a marketer might ignore the click-through rate. Why a strategist knows when the timing isn’t right. AI doesn’t do this well. Not yet. Because judgment requires more than data—it requires discernment . Why This Is the Bottleneck Holding Back AI AI can write. It can summarize. It can automate. But it still can’t: Diagnose the real problem behind the question Evaluate tradeoffs like a founder or operator would Recommend a path based on context, constraints, and conviction AI today is still reactive. It follows instructions. But it doesn’t lead. It doesn’t guide. It doesn’t own the outcome. And for those building serious systems—whether you’re running a company, launching a platform, or leading a team—this is the wall you eventually hit. That’s Why We Built Thinking OS™ We stopped waiting for AI to learn judgment on its own. Instead, we created a system that embeds it—by design. Thinking OS™ is an installable decision layer that captures how top founders, strategists, and operators think… …and makes that thinking repeatable , scalable , and usable inside teams, tools, and platforms. It’s not a framework. It’s not a chatbot. It’s not another playbook. It’s the layer that knows how to: Think through complex decisions Apply judgment when rules don’t help Guide others —human or AI—toward strategic outcomes This Is the Missing Infrastructure Thinking OS™ isn’t just about better answers. It’s about better thinking—made operational. And that’s what’s been missing in AI, consulting, leadership development, and platform design. If you’re trying to scale expertise, install judgment, or move from tactical to strategic… You don’t need a faster AI. You need a thinking layer that knows what to do—and why. We built it. Let’s talk.
