Organizations pile up AI pilots and ideas but struggle to turn them into measurable buyer and business outcomes. The problem is prioritization: choose fewer, higher-value AI projects and shore up the knowledge and governance foundations that let them scale.
Stop approving every AI idea. Prioritize use cases by business impact, customer value, feasibility, and strategic fit.
Build the knowledge foundation first: consistent product information, governed content, and clear ownership enable buyer-facing AI to scale.
Use decision intelligence to guide which pilots to scale and which to stop before they consume budget and attention.
The problem is no longer finding opportunities for AI — it's choosing among them. As the number of potential use cases continues to grow, many B2B organizations are failing to make conscious decisions about where AI can create differentiated value and where it adds little more than complexity. That may sound surprising at a time when investment continues to rise and AI capabilities improve almost weekly.
Which should be stopped before they consume more time, budget, and attention? The hardest AI decision is no longer what to build — it's what to ignore. Unlike many internal productivity applications, digital commerce AI operates close to the customer.
Every executive meeting generates another idea. Yet the reality inside many organizations looks remarkably similar: growing portfolios of pilots, competing priorities, fragmented ownership, and persistent uncertainty about where AI will create measurable business impact, customer value, and revenue growth. Activity without scale and experimentation without transformation.
While most organizations are deploying predictive, generative, and agentic AI, few can directly connect those investments to revenue, customer outcomes, or profitability. As AI becomes cheaper and easier to build, the quality of the problem being solved becomes the primary determinant of value. Teams that measure succe
Everyone has AI. Few know what to do with it.
The Boring Truth About AI Everyone and their grandmother is talking about AI, and it’s just not interesting anymore. Let’s face it, once a technology topic becomes kitchen table conversation fodder, tech pros grow weary of discussing it. Yet, every vendor has a “game-changing” solution you should buy, every staffing fi
Powerful models cannot overcome fragmented information, unclear goals, or workflows that employees do not trust.
Since Everyone Has Access To Increasingly Capable AI, Where Is Your Differentiation? When it comes to AI, B2B marketing leaders often focus on chatbots, prompts, content creation and agentic use cases, and model selection. Those topics are understandably relevant and where most of my AI client conversations begin. But
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Key Findings Search and summarization lead AI usage . According to G2’s analysis of 600+ verified Audience Intelligence reviews, these were the most mentioned AI capabilities that account for 58% of all described AI use for the category. Campaign performance is the outcome buyers attribute most to audience intelligence
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