AI & The Death of Search Ads: The New $Trillion Outcome Economy
Discover how Jobs-to-be-Done thinking and AI are creating new value exchanges beyond traditional advertising.
Table of Contents
Why Current Search Revenue is Vulnerable: The JTBD Perspective
AI's Ascent: From Search Enhancement to Search Replacement
Working Today (That Few Fully Exploit)
Novel Concepts (The Future of Value Exchange)
Strategic Imperatives for Businesses in the AI Era
For Incumbents (e.g., Google, Microsoft)
For SMBs & Entrepreneurs
For All Businesses
The Inevitable Sunset of Search Advertising
For decades, the digital world has danced to the tune of search advertising. It's a multi-billion dollar symphony conducted by a few giants, built on the premise of connecting users with information, and businesses with users. But what if the very foundation of this model – the search query, the keyword, the click – is becoming an archaic way to measure and deliver value? The current search advertising model is built on an outdated understanding of user needs, and its dominance is facing an existential threat.
Artificial Intelligence (AI) is not merely a new tool to refine search algorithms; it's a force poised to fundamentally replace the core monetization engine of search. How? By shifting the focus from finding information to directly delivering on user outcomes.
Think about traditional search. When users type into a search bar, they are "hiring" that engine for a variety of "Jobs-to-be-Done" (JTBD). These are the underlying goals they are trying to achieve: perhaps to locate a specific piece of data, compare different product options, or access a particular online resource. However, the act of searching is often just a means to an end. Users don't fundamentally want a list of links; they want their problems solved, their questions answered comprehensively, their tasks completed efficiently. This is where the concept of elevating the level of abstraction comes in. AI promises to deliver on these higher-context jobs, moving beyond the limitations of current search paradigms.
Why Current Search Revenue is Vulnerable: The JTBD Perspective
The current search revenue model, predominantly reliant on advertising, thrives on volume: clicks, impressions, and keyword bids. While immensely profitable, it has inherent limitations when viewed through the Jobs-to-be-Done lens:
Focus on Intermediaries, Not Always Outcomes: The model profits from connecting users to potential solutions (advertisements or organic links), not necessarily from the successful achievement of the user's ultimate goal. A click isn't a completed job.
Unmet Needs in the "Information Retrieval and Utilization" Job: Users often face significant friction. They expend effort sifting through numerous results, tolerate intrusive or irrelevant ads, and invest time synthesizing information from multiple sources. These are all signs of unmet needs. For example, the job of "determine the best financial investment strategy for my retirement goals" is poorly served by simply providing links to financial advisors and articles; it requires deep analysis and personalized recommendations.
AI's Direct Path to Job Completion: AI, especially generative AI and advanced reasoning engines, can directly address these unmet needs. It can provide synthesized answers, automate complex information gathering, offer personalized recommendations, and even execute tasks. This bypasses much of the "search" process, and therefore, the traditional points of ad monetization.
When a new solution (AI) can get the user's actual job done significantly better – faster, more accurately, with less effort – the old way of doing things (traditional search and its ad model) becomes vulnerable.
AI's Ascent: From Search Enhancement to Search Replacement
AI's journey in the realm of information access is rapidly evolving from a helpful assistant to a potential successor to traditional search.
Working Today (That Few Fully Exploit)
Even now, elements of this AI-driven future are visible and impacting user behavior:
AI-Powered Summaries and Direct Answers: Platforms like Google with its AI Overviews, and dedicated AI answer engines like Perplexity, are already conditioning users to expect direct, synthesized information rather than just a list of links. Users are starting to receive answers instead of just searching for them.
Niche AI Tools for Specific Jobs: Specialized AI tools are emerging that can automate specific research, analysis, or creative tasks far more effectively than a general-purpose search engine. For instance, AI tools for scientific literature review can accelerate discovery for researchers in ways that traditional keyword search cannot.
The Shift from "Searching" to "Achieving": These early examples hint at a broader behavioral shift. The focus is moving away from the act of searching towards the act of achieving an outcome with AI's help.
Novel Concepts (The Future of Value Exchange)
The true transformation lies in novel AI applications that get higher-level jobs done in entirely new ways, often with fewer visible features because the complexity is managed by the AI:
Proactive, Personalized AI Assistants: Imagine AI that moves beyond reactive query processing. These assistants would anticipate information needs based on context, history, and goals. For example, an AI assistant for a project manager could proactively gather status updates, identify potential risks from various data sources, and prepare draft reports before even being asked. The "job" isn't just "find X," but "ensure my project stays on track."
Outcome-as-a-Service (OaaS): This is where AI platforms are designed to get a defined job done completely, abstracting away the multitude of individual tools, services, and information sources a user might currently employ.
Example: Consider the job of "launch my new small business." Today, this involves searching for legal requirements, finding registration portals, researching marketing strategies, setting up financial accounts, etc. – a complex web of tasks. An OaaS AI platform could execute the entire business launch process, guiding the user through decisions but handling the operational steps. This AI solution would have dramatically fewer visible features to the user than the collection of websites, apps, and services it replaces; its power lies in its integrated ability to deliver the complete outcome.
The New Job Performer: In many scenarios, the AI itself becomes the primary "doer," not just an information retriever. Instead of a user navigating multiple websites to plan and book a complex multi-city business trip, they articulate the requirements (e.g., "arrange a 3-city trip to attend these meetings, optimizing for cost and travel time, within this budget"), and the AI formulates and executes the entire plan. The user interacts with a single, powerful AI, not a dozen booking sites.
This elevation of abstraction is key: AI will take on more complex, multi-step jobs, making the user's interaction simpler and more focused on the desired end-state, not the intermediate steps.
The New Revenue Blueprint: Monetizing Outcomes, Not Clicks
If AI can deliver outcomes so effectively, how will this new ecosystem be monetized, especially if the click-based advertising model fades? The answer lies in aligning revenue with the value of the outcomes delivered.
Premium AI Capabilities: Tiered subscription models will likely become common. Basic AI assistance might be free or low-cost, but access to more powerful AI models, deeper analytical capabilities, higher levels of automation, or specialized AI for specific professional jobs (e.g., legal AI, medical diagnostic AI) will command premium pricing. Users will pay to achieve more complex outcomes more effectively.
Outcome-Based Monetization: This is a paradigm shift.
Direct Outcome Fees: Charging based on the successful completion or tangible result of a job. For example, an AI financial advisor that helps a user optimize their investment portfolio could charge a small percentage of the gains achieved or savings identified. An AI that successfully negotiates a better deal on a major purchase takes a share of the savings.
Licensing Outcome-Driven AI Platforms: Businesses could license AI platforms that enable them to deliver complete outcomes to their own customers. A software company might offer an AI that not only identifies software bugs but also implements and tests the fixes, charging per successfully resolved issue.
Direct Value Exchange: Users and businesses will pay directly for AI solutions that demonstrably save them significant time, make them radically more effective, or provide unique, high-value capabilities that were previously unattainable. This could range from AI that generates innovative product designs to AI that manages complex logistical operations.
Ethical Data Leverage (Beyond Ads): While a sensitive area, the aggregated, anonymized insights from AI interactions—focused on patterns of job completion, common obstacles, and successful strategies—can create new forms of value. This isn't about selling personal data for ad targeting, but about understanding how to get jobs done better at a macro level, which can inform product development, service design, and even policy.
Fewer Visible Features, Higher Intrinsic Value: The new generation of AI solutions will often appear simpler on the surface. They will integrate and obfuscate the complexity of many underlying tools and data streams. Users won't need to learn or manage a dozen different apps if a single AI interface can orchestrate the resources needed to get their job done. The value is in the dramatically improved efficiency and effectiveness of achieving the end result, not in a plethora of features.
This future isn't about finding cheaper clicks; it's about paying for demonstrably better ways to achieve goals.
Strategic Imperatives for Businesses in the AI Era
This monumental shift from search-centric ad revenue to AI-driven outcome monetization carries profound strategic implications for all businesses.
For Incumbents (e.g., Google, Microsoft)
These tech giants face a classic innovator's dilemma. Their current empires are built on search advertising. Shifting to outcome-based models means potentially cannibalizing their primary cash cow. However, clinging to the old model in the face of a superior way to get jobs done is an even greater risk.
The Challenge to the "Free" Services Ecosystem: This is perhaps the most disruptive aspect for companies like Google. A vast array of popular services (Gmail, YouTube, Drive, Maps, etc.) are currently offered "free" to consumers, heavily subsidized by search advertising revenue. If that revenue stream significantly declines, the entire model is threatened.
Transition to Freemium: Many of these services may need to adopt freemium models, where basic functionality remains free, but advanced features, higher usage limits, or an ad-free experience require a subscription.
AI-Powered Subscription Bundles: Incumbents could create compelling new subscription bundles that package these traditionally free services with advanced, personalized AI capabilities that help users get significant jobs done across these platforms. The value proposition shifts from "free tools" to "an integrated AI life/work assistant."
Unbundling and Direct Monetization: Some services might be unbundled and forced to prove their standalone value through direct monetization, if they cannot be effectively integrated into a larger AI value proposition.
Strategic Re-evaluation: Companies will need to critically re-evaluate the strategic importance versus the cost of maintaining services that can no longer be easily subsidized. Tough decisions about sunsetting or divesting certain offerings may be necessary. The core challenge is to pivot business models to capture value from the new ways AI helps users, rather than relying on the old advertising metrics.
For SMBs & Entrepreneurs
The rise of outcome-focused AI creates fertile ground for innovation:
Niche AI Solutions: SMBs can identify specific, underserved "Jobs-to-be-Done" within particular industries or customer segments and build highly effective AI solutions. Instead of competing on massive ad budgets, they can compete on the efficacy of getting that job done better than anyone else. For example, an AI tool to streamline regulatory compliance for small-scale organic farmers.
Leveraging AI for Competitive Advantage: Entrepreneurs can use readily available AI platforms to build businesses that are leaner, faster, and offer more value. AI can automate many functions previously requiring significant human capital or expensive software, leveling the playing field.
For All Businesses
Every organization needs to rethink how customers interact with their products and services in an AI-first world:
AI as the New Customer Interface: How can AI become the primary channel through which customers achieve their desired outcomes with your offerings? This might mean AI chatbots that don't just answer FAQs but guide users through complex processes or configure personalized solutions.
Embedding Outcome-Driven AI: Consider how AI can be built into products and services to proactively help customers succeed. A SaaS product could include AI that not only provides features but also monitors usage and suggests ways for the user to achieve their objectives more effectively using the software.
Imagining the Future: A World Powered by Outcome-Focused AI
Let's paint a picture of what this looks like:
Scenario 1: The AI Research Analyst.
Job: Formulate a comprehensive understanding of market entry viability for a new sustainable packaging solution in Southeast Asia.
Today: Weeks of sifting through search results, market reports, academic papers, news articles, and potentially hiring consultants. Multiple tools, high cost, significant time.
Tomorrow with AI: The user provides the objective to a specialized AI research analyst. The AI accesses and synthesizes data from diverse global sources, evaluates market conditions, identifies key competitors, analyzes regulatory landscapes, projects potential ROI, and delivers a comprehensive, actionable report with clear recommendations in a fraction of the time. The AI handles the complex underlying tasks; the user receives the complete outcome. Fewer visible "search" steps, a vastly more valuable result.
Scenario 2: The AI Personal Concierge.
Job: Organize my personal and professional life for optimal efficiency and well-being next quarter.
Today: Juggling calendars, to-do list apps, email, project management tools, fitness trackers, budgeting software – a constant, fragmented effort.
Tomorrow with AI: The AI concierge, with permissioned access to relevant data streams, monitors commitments, prioritizes tasks based on stated goals, schedules appointments by liaising with other AIs or systems, suggests time blocks for focused work, recommends downtime, tracks progress against personal and professional objectives, and even automates routine communications or errands. It's a holistic system focused on the higher-level job of "a well-managed life," abstracting away much of the manual coordination.
These examples illustrate a fundamental shift: from users performing many micro-tasks using many tools, to users defining higher-level objectives and AI orchestrating the resources to achieve them. This is the power of elevating abstraction.
Beyond Disruption to Transformation
The potential for AI to replace traditional search revenue isn't merely about creating "better ads" or a slightly more efficient search algorithm. It's about a fundamental transformation in how value is created and captured in the digital economy. It's about fulfilling the user's true "Job-to-be-Done" more effectively and directly than ever before.
This transition to outcome-driven innovation, powered by increasingly capable AI, will be challenging, particularly for incumbents heavily reliant on existing advertising models and the "free" services they subsidize. However, it also unlocks immense opportunities for businesses of all sizes to deliver unprecedented value. The focus will shift from attracting eyeballs to delivering tangible results, from counting clicks to measuring successful job completion.
The companies that thrive will be those that embrace this new paradigm, understanding that the future lies not in finding new ways to interrupt users, but in finding new ways to empower them to achieve their goals seamlessly.
This is a rapidly evolving landscape, and the implications are vast. We'd love to hear your thoughts:
What "job" in your industry or personal life is most ripe for this kind of AI-driven transformation, where an AI could get it done completely and much more efficiently?
How do you think established giants like Google will, or should, navigate the monumental challenge to their "free" services if search ad revenue significantly declines? What's a realistic path forward?
What do you see as the biggest hurdles (technological, ethical, business model) to widespread adoption of outcome-based revenue models for AI services? Conversely, what are the most exciting opportunities?
Share your insights and join the conversation in the comments below!
Follow me on 𝕏: https://x.com/mikeboysen
If you’d like to take action, I would love to help. Here’s are some steps you can take to make that a reality for us:
Join my community and get access to more content and tools
Apply for coaching so we can do projects together and build a new business-as-usual with someone who will share the knowledge, and hold you accountable. (I have limited seats so hurry!)
I do project work as well. Use the coaching link and we can discuss.
Why Me?
I often turn down projects that don’t align with my expertise to maintain the quality of my work.
I’ve been trained by the best in Outcome-Driven Innovation. Part of that training involved how to understand what the future should look like. As a result, I’ve taken what I’ve learned and begun innovating so I can get you to the outcomes you’re seeking faster, better, and even more predictably. Anyone preaching innovation should be doing the same; regardless of how disruptive it’ll be.
How am I doing this?
I’ve developed a complete toolset that accelerates qualitative research to mere hours instead of the weeks or months it used to take. It’s been fine-tuned over the past 2+ years and it’s second-to-none (including to humans). That means we can have far more certainty that we’ve properly framed your research before you invest in a basket of road apples. They don’t taste good, even with whipped cream on top.
I’m also working on a completely new concept for prioritizing market dynamics that predict customer needs (and success) without requiring time-consuming and costly surveys with low quality participants. This is far more powerful and cost effective than the point-in-time surveys that I know you don’t want to do!
I believe that an innovation consultant should eat their own dog food. Therefore, we must always strive to:
Get more of the job done for our clients
Get the job done better for our clients
Get the job done faster for our clients
Get the job done with with fewer features for our clients
Get the job done in a completely different and novel way for our clients
Get the job done in a less costly manner for our clients
But more importantly, I strive to deliver high quality and high availability. That's why I also have to be choosy.
All the links you need are a few paragraphs up. Or set up some time to talk … that link is down below. 👇🏻
Mike Boysen - www.pjtbd.com
Why fail fast when you can succeed the first time?
📆 Book an appointment: https://pjtbd.com/book-mike
Update your small business tech stack: https://pjtbd.com/tech-stack