AI is a growing presence in our professional lives — and most of us will probably have used it for jobs like summarising meeting notes, creating emails, or generating briefs. But, while AI’s ability to fulfil ad hoc menial tasks and experiments is incredibly useful, this sort of one-off, sporadic usage only creates quick fixes, not lasting changes.
Used correctly, AI is increasingly setting itself apart as a key strategic differentiator, with expansive potential. But the key isn’t years of know-how, or even clear, forthright ambition — it’s structure. Without an effective AI marketing framework, organisations run the risk of deploying AI without truly understanding its purpose or value.
This is where AI readiness assessment frameworks are essential. With a structured approach, enterprises can systematically prioritise workflows, score ROI, and sequence their AI automation plans ways that deliver sustained impact.
Sonia Charles, senior solutions consultant for data & insights at Adobe, and Gaetano Polizzi, senior solutions consultant and content supply chain & GenStudio expert at Adobe, offer unique insight into this topic. Together, they provide a practical, enterprise-ready perspective on building a future-proof AI strategy, grounded in clarity, governance, and measurable business value.
Developing an AI readiness framework for enterprise.
The successful adoption of automated AI in marketing relies on a robust framework, not a one-off deployment. This establishes a shared foundation for all colleagues and users to follow, which in turn aligns with business goals, capabilities, and governance.
For Sonia, many organisations rush to adopt AI applications in marketing without first defining a key question — why? “It has to be tied to an organisational-level goal,” she explains. “The framework has to be based on the output you’re looking for. Why are we bringing AI in? Are we trying to increase efficiency? Improve asset management? Speed something up?”
Without knowing this, AI adoption is purely reactive — based on immediate need rather than long-term gain. What’s more, this could mean we may deploy tools without fully understanding them, using them inconsistently and with flimsy outcomes. A readiness framework anchors AI to the here and now, offering concrete business outcomes and the ability to set expectations.
But beyond goals, the framework must define how a business can roll out AI marketing automation across teams, and how users should interact with it. As Sonia puts it, "A framework should be based on the output. It needs to define how this is going to roll out, including rules of engagement." These clear guidelines are particularly important for decentralised teams. “You may have different teams responsible for different datasets. There needs to be guidance around how that data is going to be used.”
Then, there’s training. As AI literacy can vary widely across an organisation, consistent adoption depends on consistent training and understanding. “There’s always confusion about AI because everyone defines it differently… It’s important to establish the vocabulary, but also which types of AI are actually being rolled out. AI is a family, and there are many different kinds within it.”
Strong frameworks also incorporate governance from the beginning. Resistance and fear are common, particularly around job displacement and data usage. “It’s important to establish what AI will be doing and what humans will still be doing,” Sonia says. “Position it as something that makes people’s lives easier, not something that replaces them.”
At Adobe, this philosophy is often described as, “AI-driven, human-led, with humans always in the loop … Nothing happens until a human clicks a button. There is always an element of human oversight before any outcome is produced.”
The fastest path to AI value: Experiment, adapt, deliver.
Prioritising workflows: Identifying high-impact, AI-amenable opportunities.
As we touched on above, one of the most common mistakes businesses make is trying to apply AI before clearly identifying the problem it’s meant to solve. But when it comes to picking out a problem that AI could fix, where should we begin?
Sonia Charles explains, "It often starts with the problematic workflow." She suggests using user stories to identify these pain points, noting that asset management tools represent a typical flow that AI can augment. However, she cautions, "If you haven't properly identified the issue, it can cause confusion with different people getting different outcomes."
This approach applies both externally and internally. Whether it’s a customer journey in an app or an employee experience on an intranet, the goal is to pinpoint the disconnect. “Once you’ve identified the issue, you can say: this is exactly what we need the AI for.”
Gaetano Polizzi emphasises the importance of internal due diligence: "AI isn't always the answer; look at your processes first." He provides an example from the financial services industry (FSI) where the rise of digitally native customers, like Gen Zs, necessitates a clear understanding of customer needs. Solutions like Adobe Journey Optimizer can leverage AI to enhance customer experiences in these critical workflows.
Asset management is another useful example. “Some organisations have hundreds of thousands of assets… It’s difficult for people across offices to find the right content for campaign launches. That’s a very specific workflow issue where AI can help.”
Without this specificity, AI can make problems worse. “If you haven’t clearly identified the issue, you bring in a ‘cool AI tool’ to solve something you don’t fully understand… People start using it in different ways, getting different outcomes. The tech-savvy users do well, others get lost, and frustration builds.”
AI readiness, then, is not about speed, but precision. If you do the groundwork, you unlock value. If you don’t, you risk amplifying inefficiencies rather than resolving them.
Scoring and quantifying ROI for enterprise AI initiatives.
Measuring AI marketing readiness requires more than tracking immediate cost reduction. The first step is defining what success means for your business when looking at ROI and other non-profit metrics.
“You need to establish that upfront.” Sonia Charles explains. “What does success look like? What are the metrics?” These might include reducing time spent searching for content, shortening campaign launch cycles, or improving operational efficiency.
Concrete KPIs matter. “If campaigns usually take two weeks to launch, and, with AI, you can do it in three days, that’s measurable,” the consultant says. “Otherwise, you can’t tie success back to it.”
However, not all value is immediately financial. Brand perception, visibility, and confidence also matter. “Some metrics can’t be quantified in cost,” Gaetano Polizzi notes. “They’re about brand awareness, brand visibility, or how accurately your content is represented.”
Charles also notes that "Internal employee satisfaction can be a metric — how happy employees are, "citing the use of AI tools for internal chat box questions. She adds that "content governance as an AI value outcome" contributes to brand perception, demonstrating that "[it’s] not always [about] money, it can be about brand." “If AI improves how people access information or reduces friction in their day, that’s real value — even if it doesn’t show up as savings straight away.”
Strategic sequencing of automation: From pilot to enterprise-wide scale.
AI readiness is rarely a single-step process. “It depends,” Sonia explains. “It depends on the size of the organisation and their level of engagement.”
Gaetano Polizzi highlights the value of providing a "trial/playground so they can experiment with the solutions," but stresses it's an "education piece with the client," fostering a partnership rather than a vendor relationship.
Some enterprises begin with co-innovation or pilot programmes, working closely with partners to test and refine use cases. Others start with trial environments or “playgrounds” to explore capabilities safely. In all cases, education comes first. “This is never just clicking a button and getting a solution… it’s about onboarding, understanding the capabilities, and then scaling.”
Cross-functional ownership is critical. “Don’t make this just a tech initiative,” Sonia Charles advises. “Bring marketing in, bring sales in. Identify internal champions across teams so adoption doesn’t stall.” "Identify core teams and make it cross-functional on implementation – someone from every team who you start with before it rolls."
Building an adaptive AI governance for continuous improvement.
AI readiness is not a one-time achievement, but an evolving concern. Gaetano Polizzi underscores the dynamic nature of Artificial Intelligence: "AI is in a constant state of flux; [you] have to be ready to adapt to that, particularly in highly regulated industries." This means the framework itself "has to be agile. [You’ve] got to be able to change and adapt." He adds that upskilling is also critical as it "mitigates potential human risk."
As a result, AI readiness frameworks and assessments must be agile. Some large enterprises are now creating dedicated AI governance units. “We’re seeing organisations establish internal authorities to manage AI usage, compliance, and guidelines,” Sonia explains. “That level of focus didn’t exist a few years ago.”
For Sonia, continuous learning is inseparable from governance. “You can’t roll AI out and stop learning… if you don’t keep up, you risk breaking policies or missing important changes.”
Upskilling on AI platforms and tools requires motivation. She stipulates that “Training takes time… employees need to feel it’s worth their while — that these skills will benefit them beyond their current role.”
Sonia concludes by reminding us that, while AI tools like those within AEP AI Services are powerful, "[they] are not human — they cannot detect the things humans can. We need to keep upskilled." This continuous evolution and augmentation, though disruptive, accelerates progress, requiring a constant learning curve and a balance of innovation and adaptation.
The goal is cultural as much as technical. “AI is an augmentation, not a reduction,” Gaetano notes. “It accelerates what people already do, but humans still bring judgment, nuance, and context.”
Successful AI adoption doesn’t come from chasing trends — it comes from readiness. Enterprises that take the time to define goals, prioritise workflows, measure value holistically, and scale responsibly are the ones that turn AI into a lasting advantage.
With the right frameworks in place, AI becomes not just a tool, but a capability — one that evolves alongside the organisation and amplifies human expertise rather than replacing it.
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