The state of AI. Report
by The Growth Syndicate

AI has moved from experimental to operational in B2B marketing faster than most organizations were prepared for. If you lead a marketing function, own a P&L, or manage portfolio companies, you're already making decisions about where AI fits into your operations and where it doesn't belong.

The question is no longer whether to adopt AI, since for the vast majority of organizations this choice is no longer on the table. The priority now is to understanding how to execute when everyone has access to the same capabilities.

What we've learned over the past year, implementing AI across our own operations and working with dozens of companies at different stages, is that the gap between adoption and value is wider than it should be.

Most teams have integrated AI into their workflows. When you ask what's actually improved, they point to time saved on first drafts, not revenue growth or customer acquisition.

Marketing leaders are increasing AI budgets in the same quarter they're being asked to prove ROI on current tools. Practitioners use ChatGPT daily but rate their overall AI capability as low.

The way we see it, this adoption is driven by a fear of falling behind and belief in potential, not by systematic capability building or clear measurement frameworks.

Speed improvements are real, but speed alone doesn't create competitive advantage. What matters is where that speed compounds into better decisions, stronger positioning, or more efficient go-to-market motion.

We firmly believe that the conversation about AI in marketing has been stuck between two useless extremes.

On the one hand, vendors promise transformation and 10x productivity gains, conveniently leaving out the infrastructure, training, and process changes required to get there. On the other hand, influencers promote sophisticated future use cases while teams struggle with basic implementation.

None of these perspectives helps you make better decisions.

We wanted to provide evidence instead of opinions. Over the past year, we've implemented AI across our own operations and client work, not as experiments but as production systems delivering actual outcomes.

We've surveyed 110 B2B marketing leaders spanning Series A startups to PE-backed companies doing $500M+ in revenue, capturing data on adoption patterns, capability levels, and business impact.

We interviewed expert practitioners who've integrated AI into real operations under real constraints, extracting patterns about what separates teams that scale from teams that stay stuck.

We studied academic research on AI competencies in B2B marketing to ground our observations in broader evidence.

This report pulls all of that together.

AI has moved from experimental to operational in B2B marketing faster than most organizations were prepared for. If you lead a marketing function, own a P&L, or manage portfolio companies, you're already making decisions about where AI fits into your operations and where it doesn't belong.

The question is no longer whether to adopt AI, since for the vast majority of organizations this choice is no longer on the table. The priority now is to understanding how to execute when everyone has access to the same capabilities.

What we've learned over the past year, implementing AI across our own operations and working with dozens of companies at different stages, is that the gap between adoption and value is wider than it should be.

Most teams have integrated AI into their workflows. When you ask what's actually improved, they point to time saved on first drafts, not revenue growth or customer acquisition.

Marketing leaders are increasing AI budgets in the same quarter they're being asked to prove ROI on current tools. Practitioners use ChatGPT daily but rate their overall AI capability as low.

The way we see it, this adoption is driven by a fear of falling behind and belief in potential, not by systematic capability building or clear measurement frameworks.

Speed improvements are real, but speed alone doesn't create competitive advantage. What matters is where that speed compounds into better decisions, stronger positioning, or more efficient go-to-market motion.

We firmly believe that the conversation about AI in marketing has been stuck between two useless extremes.

On the one hand, vendors promise transformation and 10x productivity gains, conveniently leaving out the infrastructure, training, and process changes required to get there. On the other hand, influencers promote sophisticated future use cases while teams struggle with basic implementation.

None of these perspectives helps you make better decisions.

We wanted to provide evidence instead of opinions. Over the past year, we've implemented AI across our own operations and client work, not as experiments but as production systems delivering actual outcomes.

We've surveyed 110 B2B marketing leaders spanning Series A startups to PE-backed companies doing $500M+ in revenue, capturing data on adoption patterns, capability levels, and business impact.

We interviewed expert practitioners who've integrated AI into real operations under real constraints, extracting patterns about what separates teams that scale from teams that stay stuck.

We studied academic research on AI competencies in B2B marketing to ground our observations in broader evidence.

This report pulls all of that together.

AI has moved from experimental to operational in B2B marketing faster than most organizations were prepared for. If you lead a marketing function, own a P&L, or manage portfolio companies, you're already making decisions about where AI fits into your operations and where it doesn't belong.

The question is no longer whether to adopt AI, since for the vast majority of organizations this choice is no longer on the table. The priority now is to understanding how to execute when everyone has access to the same capabilities.

What we've learned over the past year, implementing AI across our own operations and working with dozens of companies at different stages, is that the gap between adoption and value is wider than it should be.

Most teams have integrated AI into their workflows. When you ask what's actually improved, they point to time saved on first drafts, not revenue growth or customer acquisition.

Marketing leaders are increasing AI budgets in the same quarter they're being asked to prove ROI on current tools. Practitioners use ChatGPT daily but rate their overall AI capability as low.

The way we see it, this adoption is driven by a fear of falling behind and belief in potential, not by systematic capability building or clear measurement frameworks.

Speed improvements are real, but speed alone doesn't create competitive advantage. What matters is where that speed compounds into better decisions, stronger positioning, or more efficient go-to-market motion.

We firmly believe that the conversation about AI in marketing has been stuck between two useless extremes.

On the one hand, vendors promise transformation and 10x productivity gains, conveniently leaving out the infrastructure, training, and process changes required to get there. On the other hand, influencers promote sophisticated future use cases while teams struggle with basic implementation.

None of these perspectives helps you make better decisions.

We wanted to provide evidence instead of opinions. Over the past year, we've implemented AI across our own operations and client work, not as experiments but as production systems delivering actual outcomes.

We've surveyed 110 B2B marketing leaders spanning Series A startups to PE-backed companies doing $500M+ in revenue, capturing data on adoption patterns, capability levels, and business impact.

We interviewed expert practitioners who've integrated AI into real operations under real constraints, extracting patterns about what separates teams that scale from teams that stay stuck.

We studied academic research on AI competencies in B2B marketing to ground our observations in broader evidence.

This report pulls all of that together.

AI has moved from experimental to operational in B2B marketing faster than most organizations were prepared for. If you lead a marketing function, own a P&L, or manage portfolio companies, you're already making decisions about where AI fits into your operations and where it doesn't belong.

The question is no longer whether to adopt AI, since for the vast majority of organizations this choice is no longer on the table. The priority now is to understanding how to execute when everyone has access to the same capabilities.

What we've learned over the past year, implementing AI across our own operations and working with dozens of companies at different stages, is that the gap between adoption and value is wider than it should be.

Most teams have integrated AI into their workflows. When you ask what's actually improved, they point to time saved on first drafts, not revenue growth or customer acquisition.

Marketing leaders are increasing AI budgets in the same quarter they're being asked to prove ROI on current tools. Practitioners use ChatGPT daily but rate their overall AI capability as low.

The way we see it, this adoption is driven by a fear of falling behind and belief in potential, not by systematic capability building or clear measurement frameworks.

Speed improvements are real, but speed alone doesn't create competitive advantage. What matters is where that speed compounds into better decisions, stronger positioning, or more efficient go-to-market motion.

We firmly believe that the conversation about AI in marketing has been stuck between two useless extremes.

On the one hand, vendors promise transformation and 10x productivity gains, conveniently leaving out the infrastructure, training, and process changes required to get there. On the other hand, influencers promote sophisticated future use cases while teams struggle with basic implementation.

None of these perspectives helps you make better decisions.

We wanted to provide evidence instead of opinions. Over the past year, we've implemented AI across our own operations and client work, not as experiments but as production systems delivering actual outcomes.

We've surveyed 110 B2B marketing leaders spanning Series A startups to PE-backed companies doing $500M+ in revenue, capturing data on adoption patterns, capability levels, and business impact.

We interviewed expert practitioners who've integrated AI into real operations under real constraints, extracting patterns about what separates teams that scale from teams that stay stuck.

We studied academic research on AI competencies in B2B marketing to ground our observations in broader evidence.

This report pulls all of that together.

AI has moved from experimental to operational in B2B marketing faster than most organizations were prepared for. If you lead a marketing function, own a P&L, or manage portfolio companies, you're already making decisions about where AI fits into your operations and where it doesn't belong.

The question is no longer whether to adopt AI, since for the vast majority of organizations this choice is no longer on the table. The priority now is to understanding how to execute when everyone has access to the same capabilities.

What we've learned over the past year, implementing AI across our own operations and working with dozens of companies at different stages, is that the gap between adoption and value is wider than it should be.

Most teams have integrated AI into their workflows. When you ask what's actually improved, they point to time saved on first drafts, not revenue growth or customer acquisition.

Marketing leaders are increasing AI budgets in the same quarter they're being asked to prove ROI on current tools. Practitioners use ChatGPT daily but rate their overall AI capability as low.

The way we see it, this adoption is driven by a fear of falling behind and belief in potential, not by systematic capability building or clear measurement frameworks.

Speed improvements are real, but speed alone doesn't create competitive advantage. What matters is where that speed compounds into better decisions, stronger positioning, or more efficient go-to-market motion.

We firmly believe that the conversation about AI in marketing has been stuck between two useless extremes.

On the one hand, vendors promise transformation and 10x productivity gains, conveniently leaving out the infrastructure, training, and process changes required to get there. On the other hand, influencers promote sophisticated future use cases while teams struggle with basic implementation.

None of these perspectives helps you make better decisions.

We wanted to provide evidence instead of opinions. Over the past year, we've implemented AI across our own operations and client work, not as experiments but as production systems delivering actual outcomes.

We've surveyed 110 B2B marketing leaders spanning Series A startups to PE-backed companies doing $500M+ in revenue, capturing data on adoption patterns, capability levels, and business impact.

We interviewed expert practitioners who've integrated AI into real operations under real constraints, extracting patterns about what separates teams that scale from teams that stay stuck.

We studied academic research on AI competencies in B2B marketing to ground our observations in broader evidence.

This report pulls all of that together.