In 2024, AI emerged as a priority investment for technology marketing organizations and along with the increased investment, expectations have risen that AI will dramatically impact how marketers operate. Executives in many of the largest (and most invested) companies mandated uptake of homegrown AI tools, and in smaller enterprises, new ecosystems of AI technologies emerged as teams and individuals actively experimented.
Enthusiasm is high. According to 2024 research from Microsoft and LinkedIn, 78% of knowledge workers are bringing their own AI tools into the workplace. Strong incentives exist for individuals to demonstrate fluency in AI, proficiency with AI tools, and the ability to apply AI to their work.
However, there’s a “but.” Despite meaningful progress in driving ad hoc and experimental usage of AI, only a small—barely double-digit—percentage of marketing organizations are seeing increases in efficiency and performance from those efforts. Speaking generally, the return on investment just hasn’t materialized. The promise of increased efficiency, speed and scale of content production, personalized customer and partner journeys, and new kinds of insights remains unrealized for most.
More than a year into the journey, progress has stalled for the vast majority of marketing organizations. Why? And more importantly, how can leaders generate—or regain—momentum?
The simple truth is, it’s difficult. AI technology introduces a large number of strategic, operational, and tactical uncertainties with material potential impact. Marketing organizations have stopped short of scaling AI use cases in large part because the cost and consequences of mistakes are high. It takes a concerted effort to build widespread understanding of AI across the organization, and as ad hoc use cases emerge, they raise challenging questions demanding structured, scalable answers.
Among the key questions are:
Very little established practice exists in the industry to guide leaders tackling these questions. Even where best practices exist, for example in quality assurance, the nuances of AI challenge norms, for example by producing an effectively limitless volume of work the quality of which is far from assured.
To overcome these challenges, enterprise marketing leaders need to lean on their companies’ heritage of technology innovation, tapping into the discipline of moving fast while managing risk.
After nearly two decades working with the world’s largest technology companies, we at Bridge Partners are still waiting for a marketing leader to ask us to move slowly and err on the side of caution. That particular pizza party jar sits empty on a forgotten, dusty shelf in an eternal wait for someone to drop in a dollar bill.
No. Everyone aspires to move fast, break things, embrace risk. That’s the heroic, entrepreneurial archetype of tech innovation. At enterprise scale, however, the aspiration to move fast collides with the realities of operating complex businesses with huge amounts of money at stake. It doesn’t easily work as a tenet applied broadly to enterprise marketing operations. And when it comes to AI, the uncertainties we outlined earlier introduce unfamiliar and unprecedented risks.
But there’s a way.
Best-in-class technology marketing organizations embed an innovator’s mindset in their cultures, not by encouraging reckless decision-making but by unlocking decision speed through discipline.
For the 10% who’ve successfully begun to scale high-impact use cases, speed is an outcome of smallness and simplicity. They address uncertainty by programmatically making every decision as small as possible. Pilot programs are intentionally isolated—not secretive but quarantined from the broader enterprise to minimize dependencies and complexity—and governed by a series of gateways. The decision-making at every gateway is formally managed using data rigor commensurate with the consequences of a wrong decision. Low-consequence decisions need data but less certainty. High-consequence decisions need to be strongly supported by factual rigor and pressure-tested by high-judgement leaders.
The uncertainty associated with AI technology has, for the most part, slowed adoption. At Bridge Partners we’ve helped enterprise marketers successfully accelerate AI adoption and begin to unlock and scale tremendous value, with the promise of more to come.
The Decision Speed Mindset is a prerequisite to success in the face of uncertainty. But that’s only one part of the formula.
At Bridge Partners our advisory engagements focus on achieving practical results fast. Our “fast” is defined as weeks not months. We couple our strategic consulting capability with services born and bred in the technology industry to help our clients scale up and reliably execute in a hands-on way. That’s our formula: Be ready on day one. Get to the answer faster, execute faster, scale faster. See it through.
Our Accelerator Method is simple and (ideally) familiar. It leverages well-established strategy frameworks and agile methodologies that have stood the test of time when implementing new technologies, translating the product innovation wisdom in our clients’ DNA into applications in central, field, and partner marketing.
The process for driving AI uptake and finding value looks like this:
The Decision Speed Mindset is a cultural trait that enables successful application of the Accelerator Method. Combining the two creates the conditions that have enabled our most advanced clients to overcome the uncertainties associated with AI technology. Entrepreneurial marketers in innovative technology enterprises are moving forward with urgency not only to generate positive returns on the past year’s surge of investment in AI but to innovate with AI, creating new kinds of competitive advantage.
As Goldman Sachs points out in their decidedly mixed assessment of the upside of AI investment, the path to value is clearest where tasks are “purely mental” and use of technology is inherent to the business. Hence the gap in AI adoption between technology industries and industry at large:
Goldman’s mile-high view obscures the fact that a similar gap exists within the technology industry between the few who’ve successfully scaled high-value use cases and the many who are stalled at the point of ad hoc experimentation.
We’ve observed, from the vantage point of our exposure to most of the best in class global technology enterprises, a relatively small set of emerging use cases developing at the leading edge. As uptake increases, we expect to see a virtuous cycle of investment sparking further innovation. And a manageable set of high-impact use cases is already producing results.
Every business is different, but the three use cases that follow offer a good starting point for pilot programs. Finding maximum impact for your particular business requires a complex decision-making process, but until you learn better, there’s probably no need to start from an entirely blank slate.
According to HubSpot’s AI Insights for Marketers 2024, 43% of marketers identified content creation as the primary use case for AI in marketing. Generative AI technologies and tools like Adobe Firefly and others offer tangible benefits and content output in many formats that are impossible to dismiss as mere parlor tricks. These tools are powerful and undeniably, obviously… super cool. It’s already clear the production process has changed forever.
So, you might ask, why isn’t everyone already doing it?
The answers to that question are nuanced and variable. There’s the so-far-unsolved practical challenge of assuring quality at scale. And, from a strategic perspective, there’s the table-stakes challenge: When the volume of available content surpasses the volume of customers’ available attention, creating even more low-cost content ceases to produce business benefit, despite making your cost-per-unit metric look great. Mediocrity at scale isn’t a win.
Applications of AI in content marketing hold more promise than simply increasing production efficiency. All phases of the content marketing process are potentially enhanced and in some cases transformed. Our Accelerator Method’s structured approach to rapid testing is the best way to find scalable (and sustainable) returns.
Content Discovery: Leverage AI to understand your audience, their needs, and the competitive landscape. Use cases include:AI has the potential to eliminate barriers of content scale in personalization, introduce new levels of logic in segmentation, and even use predictive intelligence to optimize customer and partner experiences.
In fact, 89% of decision-makers believe personalization is invaluable to their business’ success in the next three years, according to Twilio Segment’s The State of Personalization 2024.
Personalization is hard. To succeed you need tremendous understanding of customer behavior and the ability to act on it quickly with a blend of creativity and automation. Understanding the point of diminishing returns in segmentation is an analytics challenge, and communicating personalized messages that are cool rather than creepy requires scalable brand strategy and strong creative judgement. But the promise of personalization is clear, and AI technologies might prove to be the unlock marketers have been seeking.
Simplification and smallness are crucial principles to apply. Here are a few starting points:
Generative AI gets a large share of the airtime in the conversation about AI in marketing. But the most advanced organizations are actively exploring AI’s impact on business intelligence. Most simply, AI can rapidly synthesize many data sources and generate reports that would take human analysts much longer, freeing up time to focus on strategy and creative solutions. And AI can help with that part of the process too—applying predictive analytics to foresee consumer and partner behaviors enabling recommendations and thought starters that can only come into focus at massive scale. Many possibilities exist:
AI’s enhancement of business intelligence impacts all areas of marketing and sales. In fact, according to Salesforce in its 2024 State of Sales report, 79% of sales teams say AI has improved the quality of their data, enabling them to make better-informed decisions.
In the spirit of simplification, we’ll leave you with a final thought. In general, AI offers two kinds of value. Efficiency is the starting point (the new table stakes), but innovation is the fuller realization of AI’s true promise (the way to win). Innovation is much harder to do, and it’s even harder because you don’t have the luxury of failing to capitalize on AI’s efficiency benefits. Put another way, efficiency is required but insufficient.
Setting objectives requiring not just efficiency but also innovation, and organizing to achieve them with urgency and disciplined intention, greatly increases the likelihood of innovation occurring in your organization. That’s consultant-speak, in case it wasn’t clear, for aim high, think big, start small, and move fast.
At Bridge Partners we’ve had the privilege to work with the world’s largest, most innovative technology and cloud organizations in support of their AI strategies. We have a unique window into the organizational qualities that separate the best from the rest, and we tailor our services to help customer and partner marketers find success with approaches proven on a global scale.
Our Marketing AI Accelerator service is a case in point, applying the combination of Decision Speed Mindset and Accelerator Method to the challenge of AI, enabling complex organizations to swiftly validate new applications of AI technologies with high levels of discipline. If you’d like to learn more, please contact us.
| Paul Shirer | Ryan Turner | Kyrsa Dixon |
| Principal for AI Services, brings deep expertise in leveraging AI to transform business operations. | Sr. Director for Marketing Solutions, specializes in driving data-driven marketing strategies that deliver measurable results. | Sr. Director for Partner GTM, leads initiatives that strengthen partner go-to-market strategies and expand market reach. |
The authors thank Rebecca Jones, Matt Albert, Matt Hansink, and Marisa Lather for their contributions