By 2028, traditional B2B marketing and sales tactics — from demand generation to outbound prospecting — will be nearly unrecognizable or extinct. Buyers are rapidly adopting AI to bypass vendor-led processes, making GTM teams shift from persuasion-based strategies to proof-driven models. The time to adapt is now. Here’s what that means for your strategy — and your career.
A hard truth for GTM professionals
After many years in technology marketing, I love to write about GTM. But this is an exception. As a devout humanist, I prioritize people and try my best to prioritize facts. It gets tricky when the latter doesn’t align with the former.
In short, this article is “tough love.” It’s unpleasant for everyone. It’s filled with uncomfortable truths and highlights trends you won’t enjoy reading about — and I don’t particularly enjoy writing about.
That said, it is based on substantial research and statistical analysis. It will be understandable if you want to reject its conclusions. Please don’t. Now is not the time to ignore what’s happening or reject clear trends and what they are leading us to.
The rise of the AI-driven buyer
For decades, B2B GTM strategies relied on persuasion-based marketing and sales motions — lead generation, content marketing, consultative sales and vendor-led buyer enablement. But by 2028, most of these disciplines will be unrecognizable — or obsolete.
The reason? AI is what historians often refer to as a super technology, meaning that it works both ends of the field. At one end, are marketers excited about how AI can shape marketing content and delivery. At the other end are buyers who are just as excited about AI’s ability to get them out from under the torrent of marketing and sales activities.
This argument is based on what all in GTM assert — the importance of looking at the world from the outside in.
B2B buyers are beginning to aggressively use AI to avoid GTM tactics they see as outdated, inefficient, and self-serving and that substantially increase the time it takes to buy stuff. They don’t want to share data or have it mined by vendors, and they don’t want to be the target of pervasive, automated marketing.
AI-powered search, knowledge aggregation and bot-led procurement are systematically eliminating the need for vendor-led education, sales prospecting and performance marketing.
This isn’t speculation or fearmongering. It’s happening now, and the trends are accelerating. Vendor marketing has gone from leading the dance in many ways to being led by the buyer.
Dig deeper: It’s time for B2B marketing to understand its GTM role
From vendor-driven GTM to buyer-controlled AI
B2B companies have relied on marketing to capture attention and create demand, sales to convert leads into opportunities and customer success to drive retention and expansion. However, AI-driven buyers no longer need vendors to guide them.
Buyers now use AI-powered “buyer bots” to pre-screen vendors, analyze product-market fit, validate competitive claims and even ballpark the pricing that buyers should offer vendors — all before engaging with a sales team. Instead of relying on vendor-generated content, buyers train AI assistants to pull insights from peer networks, case studies and industry benchmarks.
This is an existential threat to persuasion-based GTM models, and a significant shift in the balance of power between buyers and sellers.
Which GTM sub-disciplines will disappear by 2028?
The following GTM functions will shrink dramatically or vanish entirely as AI shifts control from sellers to buyers.
1. Marketing functions that will disappear
Demand generation (as we know it)
- AI-powered buyers will bypass gated content by using AI tools to research solutions.
- Traditional MQL-based lead nurturing will be irrelevant, as AI handles vendor discovery and shortlisting.
- Vendor personalization and customization will be replaced by buyer-side information assessment, significantly negating the need for the data that vendor teams have historically prioritized.
- What replaces it? AI-driven, on-demand buyer education (without vendor control).
B2B influencer marketing
- Buyers will use AI to detect bias in influencer content, stripping away the impact of paid sponsorships.
- AI will favor peer-driven recommendations over influencer endorsements.
- What replaces it? AI-driven peer knowledge-sharing networks.
SEO and content arbitrage
- AI-powered search will summarize content instead of directing traffic to vendor websites, which will steadily reduce over time to LLMs fronted by a URL. Traditional web creative that appeals to people will be superseded by the ability to elevate and prioritize high-trust content for bots.
- Traditional SEO tactics will be useless as AI filters out marketing fluff.
- What replaces it? AI-powered buyer bots performing buyer-side personalization and vendor-content customization, including third-party product analysis and peer-validated research.
Outbound performance marketing (Targeted ads)
- AI-driven ad blockers and filters will neutralize targeted ads and retargeting.
- Buyers will find vendors organically through AI-assisted search, not by clicking ads.
- What replaces it? Algorithmic recommendation engines surfacing the most relevant solutions.
2. Sales functions that will disappear
SDRs/BDRs (Cold outreach and lead qualification)
- AI will pre-screen vendors before they can reach a human, making outbound sales obsolete.
- AI procurement assistants will auto-filter and engage vendors, eliminating the need for SDRs.
- What replaces it? AI-driven vendor qualification and buyer-side procurement AI assistants.
Early-stage discovery and demos
- Buyers will use AI to self-configure product trials, bypassing human-led demos.
- AI-driven vendor comparisons will replace sales discovery calls.
- What replaces it? AI-powered interactive proof-of-concept models in all but the most complex purchases.
Solution selling and consultative sales
- AI will provide automated vendor comparisons and risk assessments, making sales-led consultation unnecessary.
- Buyers will pre-model solution fit without vendor interaction.
- What replaces it? AI-driven decision frameworks tailored to buyer needs.
3. Customer success functions that will disappear
Manual onboarding and training
- AI-driven adaptive learning systems will eliminate the need for CSM-led training.
- Customers will onboard themselves using AI-powered product walkthroughs.
- What replaces it? AI-assisted self-service training.
Basic customer support (Tier 1 and 2)
- AI copilots will self-diagnose and resolve common issues, reducing human support demand.
- AI-driven knowledge assistants will handle most customer inquiries.
- What replaces it? AI-powered proactive troubleshooting and automated resolution engines.
What roles will thrive in the new AI-driven GTM model?
While many GTM roles will shrink or disappear, some disciplines will become more critical than ever:
AI-driven GTM strategy and market intelligence
- Companies will need experts in how AI-driven buyers make decisions.
- The best GTM teams will optimize revenue motions based on AI-driven buyer behavior.
Causal-driven marketing and sales execution
- AI will demand causal proof of GTM impact. Correlation-based tactics will fail.
- Marketing and sales must tie their efforts directly to business outcomes.
Community and peer-validated selling
- Buyers will trust verified peer insights over vendor-driven messaging. However, the definition of “peer” really matters — executive leaders have very different perspectives from line employees.
- AI will curate buyer-led communities where users educate themselves.
Customer experience and business reputation
- High-trust, high-confidence content will be the key to selling opportunities — and AI will decide who gets access.
- Powerful and immersive experiences will dominate physical channels, which will be important “surround sound” for AI-powered but still human-centric decision-making.
Risk-based pricing and fiduciary GTM models
- AI will detect misaligned vendor incentives, forcing companies to adopt performance-based pricing.
- Vendors must offer fiduciary-style commitments and value-backed guarantees.
Dig deeper: AI is transforming GTM teams into fiduciary powerhouses
Proof-based GTM will replace persuasion-based GTM
The most significant shift ahead is the move from persuasion to proof. Within organizations causal AI will increasingly govern the relationship between GTM teams and the business itself.
Over the next three years, the “zone of ambiguity” surrounding GTM’s impact will shrink dramatically. Shareholders will demand concrete steps to control risk and increase readiness for upside opportunities — forcing teams to justify their investments with clear, causal evidence of business outcomes.
By 2028, B2B buyers will completely control the vendor selection, rendering most traditional marketing and sales tactics obsolete. The shift from persuasion-based GTM to proof-based GTM will force companies to align with AI-driven buyer expectations — or risk irrelevance.
If your GTM strategy still relies on lead capture, outbound prospecting, or persuasion-based selling, you’re already behind. The future belongs to companies that prove their value, earn trust through AI-driven transparency and structure their GTM around causal impact.
The time to pivot is now, both as organizations and in our careers.
Massive restructuring of B2B GTM is coming fast
The anticipated shifts in GTM will have far-reaching impacts. Estimates based on expert conversations and research using various AI tools suggest the following:
- B2B marketing jobs will decline by approximately 55% as AI eliminates demand generation, influencer marketing, web and outbound performance marketing.
- B2B sales jobs face the steepest decline, with an estimated 90% of current roles. Only the “best of the best” in sales teams will remain, essentially working as “super SME” closers.
- The B2B GTM organization will unify under a chief commercial officer and the CMO role will significantly decrease. The commercial officer will not be a re-skinned CMO but a leader with more business and financial acumen.
- B2B customer success jobs will shrink by about 50% as AI automates onboarding, training and tier-1/2 support.
GTM strategy, reputation, customer experience, market intelligence and causal analytics jobs will grow by about 25% to 30% by 2028, highlighting the seismic shift from persuasion-based GTM to proof-based GTM where only strategic, AI-literate roles thrive. I regret to say there is very little evidence of a similar offset for sales professionals.
If you’re like the people I asked to preview this article, you may hate what I’ve written and/or want to scream at the ceiling and/or have a stiff drink. I don’t blame you. We live in interesting times and the reality is that the next several years will be an exercise in 52-card pickup.
For all the AI talk, remember that the essential thing in this world is you and me and how we stand together amid the tsunamis of change that very few of us can hope to survive by ourselves.
AI is a multiplier of human genius. It is not the author of it.
You are.
We are.
The post The hard truth about what AI will do to GTM appeared first on MarTech.
# The Impact of AI on Go-to-Market (GTM) Strategies: Challenges and Opportunities
Artificial Intelligence (AI) is transforming the way businesses approach their Go-to-Market (GTM) strategies. From customer segmentation and personalized marketing to sales automation and predictive analytics, AI is reshaping how companies bring products and services to market. While AI presents numerous opportunities to enhance efficiency and effectiveness, it also introduces challenges that businesses must navigate carefully.
## **Opportunities of AI in GTM Strategies**
### **1. Enhanced Customer Insights and Segmentation**
AI-powered analytics enable businesses to process vast amounts of customer data, identifying patterns and behaviors that would be difficult to detect manually. Machine learning algorithms analyze historical data to segment customers based on demographics, purchasing behavior, and engagement levels, allowing for more precise targeting.
**Example:** AI-driven tools like Salesforce Einstein and HubSpot AI can analyze customer interactions and predict which leads are most likely to convert, helping sales teams prioritize their efforts.
### **2. Hyper-Personalization in Marketing**
AI enables hyper-personalized marketing campaigns by tailoring content, recommendations, and messaging to individual customers. Natural Language Processing (NLP) and AI-driven recommendation engines allow businesses to deliver highly relevant content across multiple channels.
**Example:** E-commerce platforms like Amazon and Netflix use AI to recommend products and content based on user preferences, increasing engagement and conversion rates.
### **3. Predictive Sales and Demand Forecasting**
AI enhances sales forecasting by analyzing historical data, market trends, and external factors to predict demand fluctuations. This allows businesses to optimize inventory management, pricing strategies, and resource allocation.
**Example:** AI-powered tools like Clari and Gong analyze sales conversations and customer interactions to provide predictive insights, helping sales teams close deals more effectively.
### **4. Automation of Sales and Customer Support**
AI-driven chatbots and virtual assistants streamline customer interactions by handling routine inquiries, qualifying leads, and providing real-time support. This reduces response times and allows sales teams to focus on high-value tasks.
**Example:** AI-powered chatbots like Drift and Intercom engage with website visitors, answer common questions, and guide potential customers through the sales funnel.
### **5. Competitive Intelligence and Market Analysis**
AI can monitor competitors’ activities, industry trends, and customer sentiment in real time. By analyzing social media, news, and market data, businesses can make informed decisions about pricing, positioning, and product development.
**Example:** AI-driven tools like Crayon and Brandwatch provide competitive intelligence by tracking competitor movements and analyzing market trends.
## **Challenges of AI in GTM Strategies**
### **1. Data Privacy and Ethical Concerns**
AI relies on vast amounts of customer data, raising concerns about data privacy and compliance with regulations like GDPR and CCPA. Businesses must ensure that AI-driven GTM strategies adhere to ethical guidelines and data protection laws.
**Solution:** Implement robust data governance policies, ensure transparency in AI decision-making, and obtain customer consent for data usage.
### **2. Integration with Existing Systems**
Many organizations struggle to integrate AI tools with their existing CRM, marketing automation, and sales platforms. Legacy systems may not be compatible with AI-driven solutions, leading to inefficiencies.
**Solution:** Invest in AI-compatible platforms and work with IT teams to ensure seamless integration with existing workflows.
### **3. High Implementation Costs**
Deploying AI-powered GTM strategies requires significant investment in technology, infrastructure, and talent. Small and mid-sized businesses may face budget constraints when adopting AI solutions.
**Solution:** Start with scalable AI solutions, leverage cloud-based AI platforms, and focus on high-impact use cases to maximize ROI.
### **4. Dependence on Data Quality**
AI models are only as good as the data they are trained on. Inaccurate, incomplete, or biased data can lead to flawed insights and poor decision-making.
**Solution:** Regularly clean and update data, implement data validation processes, and use diverse datasets to minimize bias.
### **5. Resistance to Change**
AI adoption often faces resistance from employees who fear job displacement or struggle to adapt to new technologies. Successful AI implementation requires a cultural shift and proper training.
**Solution:** Provide AI training programs, emphasize AI as an enabler rather than a replacement, and involve employees in the AI adoption process.
## **Conclusion**
AI is revolutionizing Go-to-Market strategies by enhancing customer insights, personalizing marketing efforts, automating sales processes, and providing predictive analytics. However, businesses must address challenges such as data privacy, integration complexities, and resistance to change to fully leverage AI’s potential. By adopting a strategic approach to AI implementation, companies can gain a competitive edge and drive sustainable growth in an increasingly AI-driven marketplace.
As AI continues to evolve, organizations that embrace its capabilities while mitigating risks will be best positioned to succeed
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