AI vs ROI: Proving the Value of AI

AI content is everywhere. Everyone complains about it. It doesn't even save time, yet people are still producing ever greater amounts of AI generated content. People may be using it more, but as the technology has matured, generative AI has become far less popular among consumers. It's not just data centres. Complaints about the technology itself are far more common than they were twelve months ago. Nevertheless, corporate AI uptake has accelerated anyway. However, increased usage is no longer being matched by higher AI spending.

Code Constraints

After a brief tokenmaxxing boom, AI budgets are being throttled. Recent spend data from Ramp showed that AI budgets have been static over the last three months. Yet usage is still increasing; meaning that token efficiency has become a key metric in every model release. AI development tools have become a mature technology integrated into most software engineering workflows. That maturity leads to questions about cost. Every major tech firm has announced strict new usage guidelines on the AI coding tools used by their dev teams. No one is proposing to take AI tools away from developers though.

Perhaps the biggest impact of vibe coding has occurred outside of software teams. Despite some over optimistic initial predictions, Claude Code has not turned every knowledge worker into an app developer. It has significantly boosted the capabilities of technical teams with limited coding skills in data and operations roles. Enabling engineers with a computer science background to better integrate custom code into the systems they already use.

A similar change is occurring among non-technical AI users. Skills and scheduled tasks have turned everyone into an AI builder. Every business team has an AI-assisted reporting or content production workflow that is creating minor efficiency gains and unlocking new insights for the business. These low level automations are driving agentic adoption throughout the enterprise, even if they aren't quite the productivity revolution that boardrooms and economists have been predicting.

Time vs Value

A recent analysis in the Financial Times attempted to model the relationship between employees and revenue in an attempt to find AI-generated productivity gains. It reported no measurable productivity gains outside of the technology sector. Even that trend predates the AI because it is the result of big tech job cuts, which started prior to the release of ChatGPT in 2022. In effect, people are using a lot more AI, but that investment isn't saving costs or generating new revenue. Hence, the ongoing debate about the lack of ROI.

It's not just because teams are spending more time reviewing AI output, although that is a factor. As any author will testify, editing copy has always taken longer than producing the first draft. Much of the additional free time is being spent on backlog projects that knowledge workers previously didn't have the time to deliver. Yet such projects are low priority precisely because there is no short term impact on revenue. They might improve service quality in the long term, but even that is not guaranteed.

Beyond Content

Phave, the brand new AI native marketing automation platform from Marketo co-founder Jon Miller, provides the perfect example of this. One of the customer testimonials boasts about how Phave allowed them to produce ten times more campaigns than in the past. The question about whether the market can actually support all that extra content is left unasked. Deeper personalisation does not always lead to a higher click-through rate. Long nurture campaigns perform no better than shorter ones. Both lessons have been proven numerous times over the years.

Infinite content and ultra-granular audience targeting do not create successful marketing campaigns on their own. The content needs to resonate, and the audience needs to be a relevant buyer fit. AI provides the tools to deliver better customer experiences, but only after it has completely disrupted the discovery pipeline. It's a difficult time to be ramping up the content marketing engine. B2B marketing is in a state of flux. Simply pushing more content at the same audience is no longer good enough, especially when that audience wants to see less AI content anyway. 

Measuring Customer Impact

For AI to deliver an ROI to marketing, tokens need to be spent on projects that deliver measurable impact on revenue. Analytics teams can use AI to speed up the delivery of reporting to the business. Yet those extra reports need to deliver insights which can improve campaigns. Campaign teams can use AI to select the right message for each account. Yet those accounts need to have some level of baseline interest in order to convert. AI can scrape the web for detailed company profiles. Yet the data returned by web scraping needs to be useful to either marketing or sales. 

In recent years, marketing teams have carried out plenty of AI experiments. They've deployed new AI tools. They've launched new production workflows that incorporate AI. None of those things have affected the bottom line. Now, the AI bubble is under pressure. CFOs are now asking for evidence of AI impact, not just AI usage. More is no longer better. Linking technology expenditure to business outcomes is difficult, but marketing teams have plenty of practice. That same rigour should be extended to AI expenditure too. AI is improving the customer experience, and it is accelerating the sales process. Time to prove it. 

Eloqua Goes Agentic

Oracle have vast ambitions for AI agents. Their Fusion cloud platform includes a powerful agent builder and an expansive agent marketplace. It also now includes Eloqua support. The upcoming 26D release will enable agents built in Oracle Fusion to create campaigns, build segments and manage assets. The release also introduces some welcome UX changes to campaign canvas, including better spacing and the removal of size limits.

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