McKinsey Q&A: AI Anxiety in the Marketing Industry

AI is changing marketing – and not everyone is confident on what that means for their futures. 

According to research from McKinsey, 87% of marketers say they are excited about the possibilities AI creates, while  57% report feeling anxious about what AI means for their roles.

Discussing this anxiety – as well as how marketing leaders can best redesign their workflows with AI – is Jennifer Stanley, a McKinsey Partner and Head of the company’s Growth, Marketing and Sales Practice across the UK. 

Jennifer, who works predominantly with B2B companies on sales acceleration, account-based marketing and go-to-market transformations, exclusively tells Marketing Chief the ways she sees AI transforming the marketing industry.

UK marketing teams are balancing job security fears and mounting pressure to show financial returns through AI – creating high levels of AI anxiety. How can marketing leaders help turn this anxiety into business value?

Marketers are clearly enthusiastic about what AI could make possible, but many are also uncertain about what it will mean for their roles. In the UK specifically, 82% of marketers are excited about the possibilities of AI at work, while 64% feel anxious about what AI means for their role.

What’s particularly interesting is that anxiety is only one part of the picture. Marketers also see AI as an opportunity for growth and efficiency, but believe the C-suite is more focused mainly on productivity. We also see a sizeable gap between adoption and proof of value creation. Nearly 60% of marketers use AI multiple times a week, yet fewer than 10% have started capturing measurable value across end-to-end workflows. 

Marketing leaders have an important role in supporting their teams as AI changes how marketing work gets done, while keeping the focus on where it can create business value. That means setting a clear vision around growth and productivity, while building the skills people will need as roles and ways of working evolve.

Upskilling needs to be continuous and practical, with hands-on support that helps teams apply AI to the specific challenges they are working on. CMOs need to lead by example, too.  

They should build their own AI capabilities and embed new technologies into their own workflows. Our research found that employees’ trust in their organisation to support them as AI changes work is a significant predictor of reported enterprise value from AI.

How important is transparency in senior marketing leaders when helping teams feel empowered by AI?

Transparency is critical, particularly when there is still uncertainty about how AI will affect people’s work. Leaders need to recognise, internalise and directly acknowledge the concerns people have about AI and communicate a credible vision for how it can amplify human capability and support greater creativity, engagement with work.

A key part of that is building stronger bridges between an organisation’s AI ambitions and the people who will put them into practice. There are already examples of this playing out in practice. A global CPG company encouraged teams to build AI agents around a clear hypothesis and value case, allowing ideas to be tested from the bottom up before decisions are made about what should scale.  

Showing people what is already possible with AI can help make that message more credible. Recognising early wins is one way to do that, alongside putting targeted support, like access to targeted training, in place as people’s work changes.

How can leaders redesign their marketing workflows to better embed AI?

Leaders should look beyond individual AI use cases and think about how they can embed AI more broadly across their commercial domains. That allows them to scale their capabilities faster, while companies that remain in pilot mode risk falling further behind.

We’re seeing this particularly clearly in B2B. Our latest B2B Pulse found that market leaders are twice as likely as laggards to have adopted gen AI, at 44% compared with 22%.

The difference is also in the depth of implementation. Leaders are embedding AI directly into core commercial workflows rather than confining it to isolated pilots. And value creation is greater when applied systematically in workflows.

As an example, a financial services firm rewired its end to end prosecting workflow in tandem with account management workflows, leading to +3-15 points higher revenue per account manager (versus targeting AI at only a lead generation pilot).

There is a reinforcing effect as adoption grows. Greater usage accelerates implementation across functions. When that implementation is tied to measurable efficiency and revenue outcomes, it strengthens executive confidence and justifies further investment. That investment can then expand AI capability and impact.

What top piece of advice would you give a marketing leader looking to implement AI in their workforce?

My top piece of advice would be to start with the type, scope and scale of value you want AI to create. It’s easy to focus on how much AI is being used, but the more important measure is whether it is delivering a business outcome.

I’d look for an area where AI can materially improve value (new revenue), volume (new customers, new sales), or velocity (faster pipeline conversions) and start or expand there. In parallel, have the process ready to identify and track the impact so you can see whether it is actually reaching the bottom line. 

You also have to think about what you do with the value you create. For example, if in pursuit of faster pipeline conversions, AI saves a creative demand gen marketer 20% of their time on a task, but that time isn’t put to use elsewhere, you haven’t really captured the benefit. That capacity can be reinvested into other high priority work.

Trust also needs to be built as people start working differently with AI. Part of that is making sure incentives evolve alongside people’s capabilities and removing anything that discourages new ways of working. I’d also suggest building measurement in from the beginning of rolling out any new technology. You want to capture performance data in real time and create feedback loops so that what you learn from one interaction improves the next – and informs future choices on where to invest in AI.