Why Your AI Strategy Isn’t Delivering ROI and How to Fix It | MarTech

Everyone is talking about how they use AI. Salespeople summarize conversations, marketers write emails, product teams brainstorm new features. It feels like the way work is done is changing. But if you ask how AI fits into their daily workday, most will say, “I copy my notes into ChatGPT, get a response, and paste it back into my document.”

That’s not transformation – that’s translation. The digital equivalent of printing an email just to read it. The winners will not be the best prompters. They will be the ones who design systems that think along with them.

Your AI ROI strategy isn’t working. This is the reason

Research by McKinsey shows this almost eight in ten companies report the use of generative AI – but just as many report no significant impact on the bottom line. Take a deep breath and let it sink in: what you’re doing today isn’t working.

The potential for genAI in GTM teams is huge, but the chatbot shortcut is delaying that value. It replaces system design with surface-level questions and answers, delaying real transformation. There are two core issues in the AI-ROI conversation:

• CEOs assume that the value of AI lies in efficiency through workforce reduction.
• GTM teams chase the wrong tools or fail to reimagine the right applications that deliver ROI.

CMOs often lean on the efficiency mandate, but effectiveness is what drives value. Your CEO needs both. Here’s how to substantiate that approach:

• Better insights (effectiveness): AI shortens the distance between knowledge and decision-making, allowing teams to switch faster, recognize patterns earlier and seize opportunities that others miss.
• Process optimization (efficiency): AI enforces winning workflows, eliminates variation and reduces error rates, improving conversion and freeing up strategic capacity.

CEO translation: AI ROI is not just about labor savings. These are systems that are faster, smarter and structurally geared to growth. Simplify your message into two levers: efficiency and effectiveness.

Dig deeper: Marketers report increasing ROI as genAI moves from pilot to practice

The duality of AI strategies

Efficiency optimizes the process. Effectiveness scales intelligence. Chatbots don’t do that either. They are useful for ad-hoc question and answer sessions, but are separate from the GTM implementation. Instead of fragmenting teams with ad hoc chatbots or digital twins that seem impressive but deliver little cost savings or performance, focus on tangible ROI: cost savings through efficiency and performance increases through effectiveness.

• The right idea: Automate operational workloads to reduce manual effort and improve precision, speed and consistency in execution.
• The right tool: GenAI (not chatbots) configured with business rules and rich knowledge that streamline processes and improve decision quality.

GenAI as process optimization: efficiency strategy

Look for ways to automate operational tasks to improve speed and productivity. How many reports does it take an operations person a full day to compile each week? How many campaign reports does your team create each month? These are necessary tasks, but they take a huge amount of time – and if someone is sick or on vacation, progress stalls.

As a CMO, I once demanded this discipline. It was state of the art. Now it is outdated. This is where AI needs to come to work. Operational roles focused on routine processes should be the CMO’s first target. It may sound abrupt, but these roles will be virtually eliminated within two to three years. If you have not developed projects in this area, you are already behind.

As I write this, I’m also building a 30-step AI workflow that reverse-engineers a company’s GTM strategy from the website – mapping audiences, capabilities, messaging, and more. It runs on approximately 1,000 lines of Python and over 200 lines of JavaScript, using NLP, NER, and entity clustering to extract, validate, and prioritize insights. That is possible today.

How did I get here? I followed one piece of advice: use ChatGPT to build a functional specification and then implement it piece by piece into a low-code automation tool. The chatbot’s broad, general knowledge is perfect for this use case.

CEO translation: Reduces operational costs, shortens decision-making time and increases confidence in data-driven decisions.

GenAI as a knowledge infrastructure: effectiveness strategy

You start with AI for speed, but then something goes wrong (e.g. a hallucination, a wrong answer or a generic suggestion that ignores your business reality). That is normal, but it is also a signal.

This is the moment when you realize that fast is not the goal. You just need. You need context. And you need it all to scale accurately. That’s when you pivot: from assistant to strategist, from speed to veracity.

How does this work in practice? Think about how often teams recreate the same series, campaign, or message thread because the latest version is buried in someone’s folder. Sales uses one insight, marketing another, product teams another. And all too often, we reach for the easy button, pitching the wrong ideas to a potential client or anchoring a new piece of content on outdated assumptions.

That’s the effectiveness gap – when organizational insight is siled and uneven across critical GTM teams. It only gets worse as complexity increases. Multiple products, industries, personas, and geographies mean that the permutations of what “good” looks like are nearly infinite.

This is where genAI can really shine, although few teams have yet to realize this. An LLM’s knowledge is generic and broad, but your GTM strategy is narrow and deep. To unlock its effectiveness, you must replace the generic knowledge of the LLM with your own GTM strategy.

Just as radiologists use AI trained on millions of medical images – not generic chatbots – to detect tumors and abnormalities, GTM leaders need AI trained on their strategy, messaging and customer insights to create real effectiveness.

AI as IP for your GTM strategy

Your GTM knowledge isn’t just content, it’s intellectual property. As AI commercializes the value of broad knowledge, expert-trained AI models become your IP moat – your key lever to drive value and ROI. Treat this knowledge as a product: managed, maintained, versioned, and deployed across your organization.

When you do that, AI becomes a real-time co-pilot – not a chatbot, but a knowledge engine that adapts to strategic applications and bridges the gap between the needs of customers and prospects and the GTM elements they need to understand. This looks more like library science than computer science: a gap between technical expertise and business knowledge.

For over two years, I have built expert-trained LLMs that transform organizational knowledge into institutional assets for sales enablement and content creation. These models often span more than 20,000 rows of code and replace the broad, generic understanding of an LLM with the narrow, deep intelligence of a company’s GTM strategy. The result: no fast engineering, no hallucinations. Teams simply talk to the LLM as they would a colleague.

Dig deeper: the hard truth about what AI will do to GTM

Build your knowledge infrastructure

This isn’t ChatGPT reading a PDF. It is the infrastructure layer of your GTM. Proprietary knowledge is contextualized and surfaced when your teams need it. You can start now. Gather your key GTM strategy elements and place them in a shared location.

Common assets include:

• Objectives
  • What do you want from your GTM investments?
• Messaging and positioning
  • Value proposition communication: Clear articulation of the unique value that your solution delivers to target groups.
  • Market positioning: How you position yourself against competitors, emphasizing unique features and approaches.
• Capabilities and differentiation
  • Highlight product capabilities: Focus on important functionalities that stand out.
  • Competitive Analysis: Comparative insights that show where your solutions excel.
• Personas and their challenges
  • Needs-based solutions: Address pain points and connect needs with value and capabilities.
  • Value propositions: Proven ways to express solutions for each persona.
  • Segment-specific targeting: Tailor messages to the unique challenges of each role.
  • Life cycle phases: Identify how needs evolve throughout the customer journey.
• Examples of successful content
  • Format and writing style: Voice and tone that reflect your brand.
  • Context: Understanding the audience and applying your strategy in the real world.

The most effective approach is to put this content in a vector store (semantic database) and point your LLM to that source. I use the OpenAI Assistant infrastructure, which will transition to the Responses API in mid-2026.

Even a simple installation using File Search creates a knowledge source that is 30-40% stronger than a generic chatbot. Although your goal should be a 90% improvement, perfection should not slow down progress. Higher quality data improves adoption and trust, although this requires active knowledge management.

Your objectives in this exercise are:

• The right idea: Codify expertise and make it usable everywhere.
• The right tool: Own GTM knowledge embedded in AI systems.
• The correct outcome: Faster strategy cycles, sharper personalization, higher conversion, and a scalable execution engine.

Looking ahead, this knowledge infrastructure will become standard in commercial B2B applications in three to five years. It is a portable tool that connects workflows, CRM and MAP systems and is accessible to everyone. If you haven’t started down this path yet, you’re already falling behind.

CEO translation: This is not about reducing workforce; it’s about higher signal quality, faster insight cycles and deeper GTM deployment.

The AI ​​loop: process and knowledge come together

Process drives speed. Knowledge ensures relevance. Together they redefine what is possible in GTM. Whether you start with process or knowledge, the two inevitably come together.

Automate the basics: formatting, reporting, data hygiene. Let strategic gaps guide knowledge investments, and let those investments drive more advanced automation. Strong processes drive better insights, and smarter insights drive smarter automation.

Dig Deeper: How AI Flipped the Funnel and Made GTM Tactics Obsolete