The Future of AI Shopping
VML's new Unlock whitepaper reveals how agentic AI like Amazon Rufus is redefining digital shopping and lays out the steps to win in conversational commerce
AI Build or AI Buy
Our Unlock whitepaper unpacks the build-vs-partner dilemma of in-house AI development versus outsourcing to vendors
In today’s fast-changing world, AI in marketing is transforming how businesses operate – unlocking productivity and creating a competitive edge.
But the question remains – should your organization build its own AI marketing solutions, or should you partner with experts to develop them?
In this Unlock whitepaper, “Unlock AI in Marketing: Partner or Build Your AI Marketing Strategy – And How to Start," Alan Davies, Head of Client Business Transformation at WPP and Global Consulting Lead at VML, explores the critical decision of build vs. partner and provides actionable insights for a plan that fits your business needs.
Download "Unlock AI in Marketing: Partner or Build Your AI Marketing Strategy – And How to Start"
Why AI in Marketing Matters
AI is a game-changer for enterprises looking to innovate, differentiate and thrive in a competitive landscape. Here's why navigating the complexities of AI marketing technology requires a strategic approach:
- Rapid Innovation: Marketing evolves at lightning speed, with new channels, formats and technologies emerging daily.
- Differentiation Over Standardization: Unlike other business processes, marketing thrives on innovation and breaking the mold.
- Data-Driven Decisions: AI unlocks the power of your data, enabling smarter, faster and more effective marketing strategies.
Build vs. Partner: What’s the Right Choice?
Building in-house AI solutions is time-consuming and costly. Partnering with AI marketing experts allows businesses to focus on innovation and outcomes without the burden of maintaining fast-evolving technology.
According to VML’s “Unlocking the Power of Digital Transformation” report, 81% of businesses and organizations attempting to keep up with the rapid pace of change in a digital world rely on an average of three external partners to support their transformation goals – inevitably ending up with an ecosystem of technologies.
We’re entering a new, highly volatile world of marketing services evolution and the organizations that succeed will be open to innovation, orientate their businesses to create conditions for success, and make the most of the data they already have available.
Plan for Success
If you’re ready to harness the power of AI in marketing, our new Unlock whitepaper provides a step-by-step plan to get you started, including how to:
- Define Your AI Governance Strategy: Draft a one-page policy that defines objectives, acceptable use, decision rights and vendor portability.
- Prepare Your Data for AI Model Training: Catalog priority marketing data sources, and set quality, access, privacy and retention boundaries.
- Select a Partner and Launch a Pilot: Choose an agency/tech partner, and agree on a 60-day pilot focused on a single, high-impact use case.
Winning Strategy
Developing in-house AI platforms may seem appealing, but it often leads to higher costs, slower innovation, and missed opportunities. Partnering with experts like VML allows you to leverage cutting-edge AI solutions without the burden of development, focus on data organization and strategic outcomes rather than technology ownership, and stay agile and ready to adapt to the future of marketing technology AI.
Let's Talk: Future-Proof Your Marketing
If you're ready to unlock the full potential of AI in marketing, let's discuss how VML can help you build a winning AI marketing strategy.
Answers to your AI marketing questions
Rather than building platforms from scratch or trying to lock down rigid in-house software, companies should partner with agencies and suppliers.
Why avoid building from scratch? Developing software in-house is time-consuming, costly, and difficult to maintain in a rapidly changing environment. The agency advantage: According to VML, 81% of businesses rely on external partners to support their digital transformation. Marketing agencies are best positioned to take the lead on AI innovation because they constantly operate at the bleeding edge, design tools around evolving human workflows, and can amortize software development costs across multiple clients.
To protect intellectual property while leasing or co-developing AI solutions, businesses must separate the underlying technology platform from the intelligence (the data and models):
The Platform: Serves as the user interface and workflow tool, which is best managed and updated by partner agencies.
The Ownable IP: Lies in the trained AI models and the proprietary brand data used to train them. By maintaining a portable training dataset schema and structuring their data properly, brands preserve their ownable IP as a transferable asset—ensuring they can easily switch agency partners or platforms without losing their core data value.
Traditional enterprise software (such as accounting or ERP systems) relies on standardized, highly predictable processes designed for maximum operational efficiency and stability.
In contrast, marketing requires continuous differentiation, experimentation, and rapid adaptation to fast-changing channels and formats. Because the marketing tech landscape is fragmented and AI technology is evolving exponentially, waiting for a single, dominant, mature platform to emerge will cause companies to fall behind. Success in AI marketing requires agility and active collaboration rather than standardized lockdown.
To set up winning conditions for AI adoption, organizations should focus less on owning systems and more on preparing their internal ecosystem:
People & Governance: Define clear AI policies, decision rights, and guardrails for bias, privacy, brand safety, and human-in-the-loop oversight.
Data Readiness: Catalog priority marketing data sources and establish strict privacy, access, and schema standards for effective model training.
Partnership Focus: Collaborate early with agency partners, focusing on high-impact outcomes and real-world experiments rather than platform ownership.
