Editorial
The Gist
- Own it. Building AI capabilities in-house unlocks faster execution and a strategic edge that outsourcing can’t deliver.
- Start with outcomes. The most effective teams align AI use cases with real business goals, not flashy tools or isolated experiments.
- Design for the future. Leaders should upskill talent, stay flexible and think several years into the future when mapping org structures.
AI is no longer a side experiment. It’s becoming a foundational layer of modern marketing, just as the internet, mobile and cloud did.
McKinsey’s 2025 Global AI Survey found that 92% of companies plan to increase their AI budgets over the next three years, and 78 % of companies are already using AI in at least one business function.
I speak with growth-focused marketing executives every day who are under pressure to prove impact and are seeking solutions. Here's what I tell them: my team predicts that in 18 months, AI-native marketing won’t be your competitive edge but rather, your entry fee. AI-powered workflows, insights and agents will become the baseline, and those that don’t learn to lead with it will be stuck playing catch-up.
Companies that keep renting capability from vendors will be left permanently dependent on platforms they don’t control. Brady Lewis, senior director of AI Innovation at Marketri, described companies that rely entirely on vendors as “digital sharecroppers.” (Editor's note: the author works for Marketri).
That’s a dangerous place to be when the technology is moving this fast.
Building AI capability in-house is the strategic leap that will decide who commands the next decade. The goal isn’t to chase trends or inflate headcount, but to weave intelligence into how your marketing gets done.
Let’s talk about what that means on a practical level.
The Strategic Case for Building In-House
When you build internally, you’re not waiting on someone else’s roadmap, so you can shape AI around your business and move faster. More importantly, building internally unlocks genuine differentiation because you design capabilities that your competitors can’t simply download.
Accenture’s “Reinventing Enterprise Operations with Gen AI” study found that firms with internally modernized, AI-led processes achieved 2.5 times higher revenue growth than peers relying on standard solutions.
When every player can purchase the same software, your advantage lives in what you invent, not what you license.
Start AI Journey With Your Bottlenecks, Build Strategically
Too many companies begin their AI journey with infrastructure: buying servers, hiring data scientists and signing software contracts. That’s backwards. Start instead by identifying the tasks that drain the most time, and picture what an optimized process could look like. Then rank each opportunity by impact and ease of implementation before you spend a dime on tech.
The most successful companies we work with follow a clear playbook:
- Map the business outcomes. What KPIs does AI need to improve?
- Audit your data assets. What unique insights do you have that others don’t?
- Analyze your capability gaps. What should you build versus buy?
- Roll out in phases. Start with the quick wins, not the hardest problems.
- Measure impact. Set baselines before implementation, not after.
Some of the highest-impact, lowest-barrier use cases we’ve seen include:
- Accelerating campaign briefs, outlines and content creation using generative tools.
- Automating lead scoring and prioritization based on behavioral signals.
- Extracting insights from customer feedback without manual coding or sorting.
- Scheduling, posting and analyzing social content through intelligent agents.
Start with what's slowing you down the most and then scale.
Related Article: AI in Marketing 2025: Smart Automation and Brave Brand Building
Keep AI Out of the IT Department
Another trap I see often is treating AI as a tech initiative. That’s a great way to waste time and miss the point. AI should live where business outcomes are measured, and, in most cases, that’s within marketing operations or a growth function. IT needs to be a strategic partner, not the driver.
Lewis put it this way: “Most AI initiatives fail because they’re actually IT projects disguised as marketing innovation. What you need instead are marketers who understand algorithms, not engineers who guess at marketing.”
One smart way to integrate AI properly across teams is to form an interdepartmental AI council. Ideally, it’s chaired by someone who’s AI-forward and curious, not just technical. The council should include stakeholders across marketing, sales, operations and IT who are collectively responsible for setting guidelines, evaluating tools, managing change, and upskilling the team.
In-House AI vs. Outsourced AI: Strategic Differences
Key trade-offs that marketing leaders should consider when choosing how to build AI capability.
| Factor | In-House AI Capability | Outsourced AI Solutions |
|---|---|---|
| Speed of Innovation | Faster, customized to internal priorities | Slower, dependent on vendor roadmap |
| Differentiation | Unique capabilities not available to competitors | Commoditized features available to all |
| Control | Full control over models, data, and direction | Limited control and flexibility |
| Talent Development | Upskills internal team and drives long-term value | Creates ongoing dependency on external providers |
| Cost Efficiency (Long-Term) | Higher up-front investment, lower long-term cost | Recurring costs, potential for vendor lock-in |
Build the Right AI Marketing Roles, Not Just More Roles
Building capability doesn’t mean building a big team. It means rethinking what roles you really need and considering which ones are flexible or fractional.
The titles I'm seeing emerge most often are:
- Marketing Technologist: the bridge between tooling and execution
- AI Specialist: the technical expert who implements AI solutions across marketing campaigns
- Prompt Engineer: the craftsperson who designs AI inputs to generate high-quality marketing content and insights
- Innovation Engineer: the strategist who knows how to move from pre-AI to AI-empowered
In-house teams should focus on upskilling, and any new full-time hires should be adaptable and cross-functional. You don’t need a PhD to use AI well. You need curiosity, process-oriented thinking, and a clear sense of how marketing drives value.
Related Article: The Unforeseen Consequences of Relying on AI in Marketing Strategies
The Payoff for AI in Marketing Is Real
When AI is built internally, the results speak for themselves: real-time analytics that inform decisions rather than just report them, personalized campaigns created in minutes instead of weeks, always-on competitive monitoring and intelligence, and consistent brand expression across every channel.
But none of that happens by accident. You need to build the systems and habits that sustain it.
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My Advice to Executives Ready to Make Moves
Start by taking stock of where you are today. Where is AI already being used? Where is it missing? Decide what success looks like so you can set a clear vision for the future. If you need to generate momentum, lean on partners who are already AI-forward and upskill your internal team as the work unfolds.
And finally, don’t be afraid to move fast, because the window for competitive advantage is closing quickly.
Core Questions About Building AI Capability In-House
Editor's note: Key questions surrounding how and why marketing leaders should build AI capability internally — and what strategic steps will set them apart.
Keep AI initiatives where outcomes live — in marketing, not IT. Cross-functional collaboration is key, but the driver must understand business value, not just technical capability. Successful orgs form AI councils across departments and build agile roles like AI specialists, prompt engineers and marketing technologists. The goal isn’t headcount — it’s capability and coordination.
Begin with your bottlenecks, not your tech wishlist. Focus on the most time-consuming marketing tasks — like content generation, lead scoring or campaign analysis — and build AI use cases that remove friction. Map these to clear business outcomes and roll out in phases. This ensures ROI from day one and avoids overbuilding without purpose.
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