Summary and Rewrite: **The Gist** Taking ownership by building AI capabilities internally enables faster execution and provides a strategic advantage that outsourcing cannot match. **Start with Outcomes** High-performing teams focus on aligning AI use cases with tangible business objectives rather than experimenting with flashy tools or isolated pilots. **Design for the Future** Leaders should prioritize upskilling their workforce, maintain organizational flexibility, and plan several years ahead when structuring their teams. AI has moved beyond being a side experiment; it is now a foundational element of modern marketing, akin to the internet, mobile, and cloud technologies. According to McKinsey’s 2025 Global AI Survey, 92% of companies plan to increase AI budgets over the next three years, and 78% already use AI in at least one business area. Speaking with growth-focused marketing executives, I emphasize that in 18 months, being AI-native won’t be a competitive edge but a baseline requirement. AI-powered workflows, insights, and agents will set the standard, and companies not leading with AI will fall behind. Companies relying solely on vendors risk permanent dependence on platforms outside their control. Brady Lewis, Senior Director of AI Innovation at Marketri, calls such firms “digital sharecroppers. ” (Note: I work at Marketri. ) Given how quickly technology evolves, building AI capabilities internally is the critical strategic move that will determine who leads the next decade. The goal is not to chase trends or add headcount but to embed intelligence into marketing processes. **The Strategic Case for Building In-House** Developing AI capabilities internally frees you from external roadmaps, speeds execution, and creates unique, defensible advantages competitors cannot replicate. Accenture’s study found companies with AI-led, modernized operations outgrew peers by 2. 5 times in revenue. When everyone can buy the same software, your edge comes from what you invent, not what you license. **Start Your AI Journey with Bottlenecks, Then Build Strategically** Many companies jump into AI by investing in infrastructure—servers, data scientists, software—before identifying key pain points. Instead, begin by pinpointing time-consuming tasks and envisioning optimized solutions. Prioritize by impact and ease of implementation before spending on technology. Successful companies follow a playbook: - Define business outcomes and KPIs AI should improve - Audit unique data assets for competitive insights - Identify capability gaps to decide what to build versus buy - Roll out in phases, starting with quick wins - Measure impact with pre-implementation baselines High-impact, easy-to-adopt use cases include: - Accelerating campaign briefs and content creation with generative AI - Automating lead scoring via behavioral signals - Extracting customer feedback insights without manual effort - Scheduling and analyzing social media with intelligent agents Begin with what slows you down most, then scale. **Keep AI Out of the IT Department** Treating AI as merely an IT project often causes failure.

AI belongs where business outcomes are measured—typically marketing operations or growth functions—with IT as a strategic partner, not leader. Lewis notes, “Most AI initiatives fail because they’re IT projects disguised as marketing innovation. Marketers who understand algorithms—not engineers guessing at marketing—are critical. ” Forming an interdepartmental AI council chaired by an AI-forward leader and including marketing, sales, operations, and IT stakeholders helps set guidelines, evaluate tools, manage change, and upskill teams effectively. **In-House AI vs. Outsourced AI: Key Differences** | Factor | In-House AI Capability | Outsourced AI Solutions | |-----------------------|----------------------------------------|-------------------------------------------| | Speed of Innovation | Faster, aligned with internal priorities | Slower, dependent on vendor roadmap | | Differentiation | Unique capabilities competitors lack | Commoditized features available to all | | Control | Full control over models, data, direction | Limited control and flexibility | | Talent Development | Upskills internal teams for long-term value | Creates dependency on external providers | | Cost Efficiency (Long-Term) | Higher upfront, lower ongoing cost | Recurring costs, risk of vendor lock-in | **Build the Right AI Marketing Roles, Not Just More People** Building capability is about smart role design, not expanding headcount indiscriminately. Emerging roles include: - Marketing Technologist: bridges tools and execution - AI Specialist: implements AI across campaigns - Prompt Engineer: crafts AI inputs for quality output - Innovation Engineer: leads transition from traditional to AI-powered marketing Focus on upskilling current staff and hiring adaptable, cross-functional team members. Success with AI requires curiosity, process orientation, and understanding marketing’s value—not necessarily a PhD. **The Payoff for AI in Marketing Is Real** Internally developed AI delivers real-time analytics for decision-making, personalized campaigns in minutes, continuous competitive intelligence, and consistent branding across all channels. These benefits require deliberate system-building and cultural adoption. **Advice for Executives Ready to Act** Assess your current AI use and gaps, define success metrics, and create a clear vision. Partner with AI-forward organizations if needed and upskill your team along the way. Move swiftly—the window for competitive advantage is closing rapidly. **Core Questions for Building In-House AI Capability** Marketing leaders must address strategic questions about how and why to develop internal AI capabilities and identify the steps that will differentiate their organizations. --- This rewrite preserves the original's volume and detail while enhancing clarity and flow for practical understanding.