Bridging the AI Chasm

It was pretty much inevitable. Last week, I was at Mops-Apooloza, a marketing operations conference in Anaheim. AI was everywhere. Every conversation touched on it. Every breakout session talked about it. Yet, despite being an ever-present topic, the technology attracted surprisingly little hype. Instead, there was a down to earth discussion about what the technology can do, and the steps needed to make it happen. In that respect, AI is rapidly becoming just another tool in the marketing operations toolkit.

Practical Experience

Sure, marketing ops professionals recognise how important AI is to their businesses, and how it can significantly improve the customer experience. Yet, there were none of the excessive boasts or overblown doom-mongering that we're seeing in certain quarters. The AI talk at past events has always felt tacked on; as though speakers had nothing to say on the subject, but felt it had to be mentioned anyway. As a result, any discussion of AI within marketing operations circles has always felt highly theoretical. Not this time.

Marketing is at the frontline of AI adoption. It’s one of the few departments that can construct a viable business case for Generative AI, generally on the basis of redeploying agency budgets to AI content generation. As a result, marketing operations teams have been forced to experiment with the technology. Many enterprise marketers have implemented AI content workflows with decidedly mixed results, and marketing ops have been forced to deal with the outcomes. That means the Mops-Apooloza audience could talk confidently about the steps needed to implement the technology but also of the business challenges that it introduces.

Key Concerns

In particular, two key concerns were repeatedly raised at the event: data and governance. Data improvement is typically a major topic at marketing ops events anyway. It’s a core responsibility of the function. Yet, data is a well understood problem that has clear solutions, albeit ones that are difficult to implement successfully. AI adds another layer of complexity to the long-standing data quality challenges. It introduces new data cleanliness standards, and new processes for data maintenance.

Governance is a much less understood issue, but just as important. Hallucination is the key blocker to successful AI implementation. Security is an ever-present risk. While cost will become one in time. Oversight is critical in overcoming all these issues, but the question of how to implement human-in-the-loop processes was a major talking point. Addressing data quality is only one part of the challenge, because no AI will ever be 100% reliable. A probabilistic solution will always give unpredictable and occasionally inaccurate results. The need to mix AI with traditional automation workflows was well understood - even if the technology to do it is still very immature.

An Additive Technology

And that's the second big AI related takeaway from Mops-Apooloza. AI doesn’t replace anything. It doesn't replace SaaS. It doesn't replace rule based automation. It doesn't even replace your SDR team. Used wisely, it can improve all those things. It can make everyone more efficient. That’s critical because efficiency is king right now. It's the number one priority in the boardroom. It's what the CMO is talking about with CEO. Yet, the drive for improved productivity is a major opportunity for marketing operations to achieve recognition and deliver real value to the business.

Whether it's through streamlined processes, enriched data or optimised technology, Generative AI allows the business to focus on activities which improve the customer experience. A new wave of startups took the stage to promise 1:1 personalisation, even if the technology isn’t quite ready yet. Industry veterans are aiming to usurp the leading martech platforms, such as Marketo. Generative AI promises to transform B2B marketing. And yes, ops might even use AI to deliver those improvements. Assuming it's the right tool for the job.