It is tempting to think that the AI revolution will follow the pattern of the digital transformation. From about 2010, marketing experienced profound changes driven by new, digital technological capabilities.  Marketing’s scope was expanding in amazing ways even though sometimes it felt as though the floor was disappearing beneath our feet.

During the peak of the digital transformation, digital tech was applied in haphazard ways across the organization; individual websites built by different groups, CRM systems for different product groups, data strategy organized by business unit, and analytics based on silo’d data.

It is natural to think that AI would follow that pattern – start with pilots and go on from there. It is logical to want to proceed with caution.   

However, if you are thinking that AI should follow in the deployment footprints of digital then you are not seeing for AI for what it really is. AI is not just an expansion of marketing capabilities – it is changing marketing itself in ways digital never did.

Stay with me so you understand the possible, wonderful trajectory of AI transformation in marketing.  

1. Marketing Had Two Versions. AI Isn’t the Third.

Marketing 1.0 was analog: TV planning, print insertion orders, reach and frequency as the measure of truth. Marketing 2.0 was digital: Google, Meta, and programmatic DSPs gave marketers new channels, new data, and new ways to buy attention. It’s tempting to call AI “marketing 3.0” — the next evolution in the sequence. That framing misses the import of what’s actually happening.

Analog marketing was dominated by mass media advertising; TV, magazines, newspapers and radio when, in the decades post-WWII, American families were building their version of the American dream. They needed a lot of stuff from homes to cars and everything in between which ignited an economic bonanza for American companies who made all the stuff. This was analog marketing’s defining eras when advertising did the heavy lifting to drive market share and sales growth. During these decades, (1970s – 2000s), the mechanics of analog marketing was simpler because it had far fewer moving parts. There were few mass media channels available. There no data lakes or first party data requirements. Websites, when they even existed, were simply digital collateral sites. Measurements were transparent albeit limited with a focus only on things that was known for sure – market share and sales gains. However, analog marketing was very expensive – making it out of reach for most companies.

Digital marketing which really took off starting in 2010, more or less, changed who could activate marketing, where marketing happened and how it was measured. Digital did not change the basic shape of the marketing operating model: humans set strategy, humans designed workflows, and systems executed against rules humans wrote. Importantly, digital technology vastly increased the number of companies that could afford advertising. The production costs were cheaper, getting into market was faster, and once social media really took off, it provided a compelling new opportunity for companies of all sizes to grow their businesses. Yet digital marketing had a dark side. It was complex, it had transparency issues, and it became clear that initial lower costs were eaten up by all tech needed to run digitally centric marketing.

Digital did not replace analog marketing but it became part of the expanding capability of marketing to drive businesses. The analog PLUS digital ecosystem took roughly ten, painful years for marketers to settle into a new consensus. It was hard on people. Organizations couldn’t simply switch systems since they had to run both at once. Dominance of TV buys and static insertion ran alongside programmatic media buying even as the infrastructure was being built up.

Slowly, but inevitably by 2019, digital media budgets overtook traditional media spend. This was when digital marketing matured. Two operating systems, two budgets linked yet one set of people driving how marketing evolved.

We now know the firms that bolted digital onto their existing analog relationships spent the following decades losing ground because disruption also arrived one function at a time — media buying first, then measurement, then creative, then CRM. This meant that an organization that adapted digital sequentially, required an endless game of playing catch too much of the time.

Despite the herky-jerky adoption curve of digital, one thing held steady – marketing is all about human motivation. People still buy for identity, trust, status, convenience, and fear of missing out — that hasn’t shifted and won’t. What had shifted when digital was introduced into the marketing ecosystem is how that motivation got engaged. Analog marketing engaged it through mass broadcast. Digital engaged it through targeting and measurement.

This is where people may not understand the crucial difference between AI and the other marketing analog and digital systems. AI is not designed to move people – it is designed to deliver intelligence to help people who create the motivation layer of marketing.

That is entirely different than anything that came before. That difference is not just in tooling or sequencing – it is a different layer within the marketing ecosystem. Companies and agencies who understand this are the ones to thrive over the next decade.

2. AI Doesn’t Coexist With What Came Before — It Is the Intelligence Layer that Drives Them both.

While it is reasonable to think that the addition of AI is just another iteration of marketing, it is more accurate to think that AI is as game changing as Internet was in its day. Internet did not just change marketing – though it did – it became enablement infrastructure that allowed for greater efficiency, greater productivity, and greater return on effort’s investment across an organization.

In other words, the Internet was the paradigm change that changed everything. AI is similar.

AI is not adding a channel to the marketing model though it can. It is not adding tools to marketing’s toolkit though it does. Nor can AI be thought of as a new capability to be bolted onto existing systems and processes though it can. 

AI is reinventing how business happens – across the board. For marketers, the disruption runs deep because the transformation transcends adding a tool here or a new workflow there.

What makes this shift structurally different from the digital marketing transformation is simultaneity. Strategy, creative, media, measurement, and operations are all being reshaped at the same time because AI is changing the intelligence and logic that connects data, decisions, and execution. It is changing the intelligence logic underneath every channel at the same time — how decisions get made, how creative gets produced, how audiences get identified, how performance gets read.

Once we think of AI as the “intelligence infrastructure” of an organization, we see there’s no adapting one function this year and the next function later in the year. Intelligence can only be built up organically and holistically.

Some organizations are a model for what this looks like in practice. Coca-Cola’s investment in a dedicated generative AI platform wasn’t a creative experiment — it was a rebuild of content infrastructure from brand-asset ingestion through global localization. Unilever has been restructuring its data infrastructure and agency relationships explicitly around AI-native workflows. AI adoption in marketing correlates with process change, data readiness, and organizational reconfiguration — not with which tools a company has procured. You cannot buy your way into this transition.

You build your way in, or you don’t make the transition at all.

Dimension	AI as a tool	AI as Intelligence Infrastructure
Content Creation	This is like using AI to create a PDF – flat and not productive to drive revenue directly.	Using AI to gather performance and engagement data to create a deep set of articles that can drive engagement and sales.
Analytics	By channel, by cohort group and by ad.	Operating ROI intelligence layer underneath every channel, every expenditure and every user journey.
Where disruption hit	Sequentially: media, then measurement, then CRM.	Simultaneously: strategy, creative, media, measurement, ops.
Primary failure mode	Bolting AI onto digital workflows.	Creating AI infrastructure to reshape the why AND how of marketing – analog and digital.
The core job	Build revenue pipelines.	Rebuild infrastructure as AI-first; redesign the operating logic.
Who controls the disruption	AI companies, channel owners: Google, Meta, DSPs	Internal capability: intelligence data architecture, governance, design

3. What AI As Infrastructure Changes: People, Process, Technology

People

AI can power humans in a holistic fashion — not a function at a time. With AI, new, genuinely productive insights can be achieved that drive a coordinated set of behaviors. With intelligence going horizontal in an organization, it can deliver a unifying approach to functions like content generation, segmentation, real-time bid optimization, reporting, measurement. Areas that previously were thought of to be in the human-led domain like strategic direction, creative judgment, ethical oversight, and cross-functional orchestration become intelligence powered.

The intelligence provides people the optimum environment for profitable decision-making, repeatedly and at scale.

Better yet, intelligence as infrastructure has a profound impact on what is knowable versus unknowable. Previously, without AI, the footprint of what was unknowable in marketing was huge. For instance, in analog marketing, very little was knowable. Digital technology could answer many more questions but it had a trust issue – could the data from digital platforms be trusted. More was known but a lot was still unknowable.

As AI gets better and better, realm of marketing unknowables will get smaller and smaller as the realm of certainty dominates marketing.

That is the real value of AI as infrastructure.  It will take time, training, and trust but in the process, an organization can create profound improvements to their marketing outcomes possibly even more dramatically than when companies launched websites during the early years of the Internet.

Now, we have AI as infrastructure – accessible intelligence for the modern organization. While it requires humans to take a leap of intelligence faith, it is that leap that will distinguish high achieving organizations from the rest.

Process

AI transformation as intelligence infrastructure touches everyone; data architecture, IT, security, governance (legal and compliance), and workflow redesign across every function – across the company. It sounds daunting and to be honest – it is. Yet, just like someone cannot be “half pregnant,” AI as intelligence infrastructure needs to be thought of in the totality of marketing functions.

Happily, our experience with the digital evolution taught us two important lessons that can be applied to AI:   

• This time we are smarter about what to expect in the infrastructure build phase. For example, after about seven years into the digital revolution – it became clear that a fragmented approach was technological suicide. It cost a lot of time and money to undo those seven years of disjointed network chaos. This time, we can avoid that mistake.  
• AI is hitting the knee of the curve in its exponential growth trend (ala Ray Kurzweil). The digital transformation took about 15 years because the rate of technology progress was paced in years. AI is blowing all those development cycles out of the water so that what took five years in the digital revolution, is likely to take five days with AI. By collapsing the time frame, AI frees companies to adapt to AI in a much shorter amount of time thus reducing costs, confusion, the human proclivity to resist change. Now adopting AI at a holistic, company level is really possible in ways the digital transformation never was.
  

Technology

Today’s conventional thinking when it comes to AI adoption is to pick a function as a pilot with real production data before it touches core infrastructure. Then, the team evaluates people’s operating experience, and ask what broke in production and what it cost to fix.

That approach made total sense in a digital centric world and I believed it too. Now I realize AI is not a digital expansion “thing” – it is the foundation of everything the company does. Staying with the building metaphor for a moment, it is like building a home but putting a concrete foundation only in a few rooms scattered across the house. The structural flaws are obvious. Same with AI. Unless it is built as the infrastructure level across the entire organization, the company building will come crashing down.

This is why we need a change of thinking that understands the importance of building up the AI infrastructure into layers of AI brilliant intelligence; data, workflows, integrations, and analytics. The advice of operationalizing one function at a time is misplaced. If a company follows typical AI build thinking using a fragmented approach, the company will end up looking like the early days of Internet when a company would have multiple, unrelated websites, multiple versions of CRM systems or silo’d data that do not talk to each other. In short, the company has a chaotic mess on its hands.

If AI is harnessed to its optimum effect – holistically – then the reward delivers nothing less than extraordinary – a truly functional single source of data truth for the entire organization. This unfulfilled dream of marketers to have a unified data source of truth is now within reach but it can only happen if AI is built up as infrastructure. That is the prize that makes the effort worthwhile.    

Seven Directives – One Goal – Intelligence As Infrastructure.

The analog-to-digital transition taught us a lot. The experience can be applied directly to the decisions in front of marketing leaders now.

1. Rebuild infrastructure to be AI-first — not retrofitted.
2. Retrain people to be AI-first — not tool-trained.
3. Understand the full cost before you commit: downstream data, governance, integration.
4. Do not buy what you do not understand, or what sounds too perfect.
5. Find a practitioner — someone who has run these systems live.
6. Partner internally and cross-functionally. AI transformation is not a marketing-owned project or sales. It is a cohesion level that drives it all.
7. Be absolutist about data quality: pristine, first-party, real-time — or do not deploy until that is addressed. Before deploying AI against marketing data, the honest version of a readiness check asks whether enterprise-wide datasets can be normalized into a single schema:

• First-party data collected with explicit consent and auditable provenance
• Pipelines accessible in real time, not batch
• Governance policies embedded at the pipeline layer, not the application layer
• A monitoring framework in place to detect model drift and output degradation
• Quality audits conducted within the last 90 days

A “no” on any of those means the AI system will degrade toward the data, not elevate it. Run that check before the next vendor conversation — not after the next deployment.

The analog-to-digital shift redrew the map. AI is redrawing the terrain in no way we have seen before because it is creating the foundation other marketing systems are built on. In this respect, AI is touching every function at once which is markedly different than how the digital transformation played out. The organizations that treat AI as a tool – will get a tool. The companies that treat AI as infrastructure will thrive in the next decade.

A special note to agencies.

There are multilayer implications for agencies. One, is to get your own AI house in order based on the concepts here. Then, there is a real opportunity to help your clients evolve in ways that delivers incredible outcomes for clients. The potential is only limited by imagination, discipline, and a sense of the wonder at what is now possible.