Is Your Marketing AI Paying Off? How to Find Out (A Sneak Peek at NJDMC 2026)

Your team is using AI every day. It’s writing first drafts, summarizing research, resizing creative, and cleaning up campaign reports. Now the hard question: can you say what all of that costs and what it’s actually returning to the business?

If the honest answer is “not really,” you’re in good company. That gap is exactly what Denis Scott, Chief Marketing Officer of Larridin, an AI measurement platform backed by Andreessen Horowitz and Bloomberg Beta, is bringing to the stage at NJDMC 2026 in Holmdel, NJ, on October 15. His session, “Measuring AI ROI and Turning Adoption into Business Outcomes,”

It’s the natural second half to Dan Van Tran’s session on agentic AI earlier that day. If DVT is the gas pedal, showing what AI can now do, Denis is the gauge: how you’ll know whether it’s working and what it’s quietly costing you.

Below, you’ll get a preview of what Denis will cover, plus the step-by-step method we use at dblspc to help marketing teams measure AI ROI. You can start on it this week.

Why is marketing’s AI spend so hard to measure?

Larridin’s 2026 State of Enterprise AI Report surveyed 365 senior leaders at companies with 1,000 or more employees. It found that 92% of C-suite executives feel confident in AI’s impact. At the same time, 58.2% said unclear or fragmented ownership is their biggest barrier to measuring AI performance, and 62% do not have a complete inventory of the AI applications their people are using.

Confidence is running well ahead of real evidence.

Marketing usually finds itself in a tough spot here. While AI usually lands in a company as an IT line item, marketing is often the heaviest user in the building across content, research, personalization, creative, and campaign ops. On top of that, the spend gets scattered across approved team licenses, per-seat add-ons buried in existing software, and random personal credit cards.

The heaviest user and the least measured is not where you want to be when the CFO asks questions out loud.

Using AI vs. being good at AI: what’s the difference?

There is a massive difference between adoption and proficiency.

A VP might report a 90% adoption rate to leadership and walk out feeling great. Look closer, however, and most people are just using AI to fix grammar or tighten an email draft. Very few have built a workflow that runs more than once without being rebuilt from scratch, and almost nobody can tell you what that workflow costs per month in compute or internal labor.

Adoption is just the starting line. Value happens when AI gets baked into repeatable processes with predictable outputs that you can actually measure.

How to measure AI ROI in marketing: a five-step walkthrough

When clients ask us “Is our AI setup actually working?” this is the process we take them through. No fancy new software required. A spreadsheet and an honest afternoon will get you most of the way there.

What does this look like with real numbers?

Consider a 5-person marketing team publishing 8 blog posts a month.

• Before AI: They paid freelancers $400 per post plus $150 in internal editor review time = $4,400 per month.
• With AI (The On-Paper Math): They cut the freelancer, pay $300 in tool fees, and spend $2,400 in internal staff drafting/editing time = $2,700 per month (an apparent $1,700 savings).
• With AI (The Honest Reality): Extra fact-checking and heavy rewriting add another $1,200 in internal labor = $3,900 per month.

The team is still saving $500 a month, but the narrative shifts from “AI cut our content costs by almost 40%” to “AI saves us about 11%, and rework is where our time bleeds out.” That second version gives you a practical problem to solve, such as refining your brand prompts or updating brief guidelines.

What should you do about “shadow AI” on your team?

When unapproved AI tools pop up on expense reports, the knee-jerk reaction is to label it “shadow AI” and shut it down. However, outright bans usually kill genuine grassroots productivity that you simply cannot see or scale.

Instead, bring it into the light. Ask your team what tools they are testing and what friction points those tools solve. You will fill in your spend list and likely discover your best workflow ideas in the process.

How does AI change who you should hire?

As AI takes over middle-tier execution tasks, marketing leaders face a new challenge: how do junior marketers build critical thinking if the baseline execution work disappears?

When teams blindly accept AI outputs without scrutiny (what researchers call cognitive surrender), mistakes slip into published work. High-performing teams will not just look for people who can type prompts; they will hire for sharp editorial judgment, strategy, and deep domain expertise.

Let’s make your AI strategy defensible

Measuring AI is not a one-time audit; it is a discipline. Whether you need help auditing your current stack, setting up clear baselines, or training your team to move from basic adoption to true workflow proficiency, explore our AI Consulting, Automation & AI, and MarTech services. We will help you get real answers before your next budget review.

Who is Denis Scott?

Denis has built marketing functions from the ground up at some of the biggest names in tech and retail. He started his career in FP&A, which is a big part of why he’s known for tying marketing dollars to pipeline instead of vanity metrics. He was Gap Inc.’s first-ever online marketer, driving early e-commerce across Gap, Old Navy, and Banana Republic. He later led the consumer business at OpenTable, served as CMO of SurveyMonkey (where his team won a Gold ANA Reggie Award for B2B marketing), launched Fanatics’ Betting & Gaming brand from scratch as SVP of Integrated Marketing, and built the brand and growth engine at CharterUP as it landed the No. 2 spot on the Inc. 5000. He’s keynoted Google Marketing Next and Salesforce Dreamforce, was named a Brand Innovators 40 Under 40, and advises Hangtight, an AI-powered social planning app.

After two decades as the person spending the marketing budget, he recently switched sides of the table to help executives find out whether their AI investment is working. He’s also closer to home than his Bay Area address suggests: he played four years of varsity soccer at Georgetown, spent a college summer working at the Nordstrom in Short Hills, and played club ball out of Rutgers. And he’s said plainly that he’d rather talk about the problem than give a pitch, which is exactly why this session is a conversation and not a product demo.

Want the one number to bring to your next budget meeting? Get your seat for NJDMC 2026: Connected Experiences, October 15, Bell Works, Holmdel, NJ.