Tackling The Fragmented Nature Of Multiple GenAI Tools

Tackling The Fragmented Nature Of Multiple GenAI Tools

The year of generative AI (GenAI) is coming to a close, and with already experimenting with the technology, it’s clear that GenAI is here to stay. Its impact on productivity has the potential to add to the global economy on an annual basis, and as we’ve seen from the myriad GenAI tools that seem to crop up every day, its business applications are seemingly boundless.

But buyer beware; we’ve reached a pivotal moment where any productivity or revenue gains from GenAI may be at risk. Think about it this way—since GenAI became widely known and available in 2022, multiple business functions, including marketing, digital and e-commerce teams, have rapidly adopted an assortment of moderately priced GenAI tools to supplement their work.

I don’t doubt these individual teams have seen increased productivity—and, in some cases, business results—from these implementations. But these disparate GenAI tools, APIs and plug-ins are now likely confusing systems, particularly your martech stack.

The Fragmented Approach

From CMS and orchestration solutions to chatbots and e-commerce platforms, GenAI tools are churning out content based on a patchwork of disconnected LLMs and training models. The result? Businesses have a fragmented approach to GenAI implementation.

The Dangers A fragmented approach to GenAI implementation prevents businesses from leveraging data related to the input, output and results associated with each model and application. As these models are trained, any learnings, triumphs or pitfalls stay siloed.

Different teams don’t know how others are performing or whether or not their campaigns are successful and why. With this approach, businesses are sending a cacophony of mixed messages in various brand voices to customers across channels. To your customers, this can be confusing and off-putting, potentially causing a loss of loyalty.

One message—generated by disparate GenAI tools, optimizing for different objectives—could come across as inconsistent to customers. Results for different channels could run the risk of not appearing cohesive to readers.

Preventing GenAI Fragmentation With A Centralized “Brain”

Just as CRM systems brought order to email chaos, we now need a centralized hub—a “brain”—to seamlessly unify data, translate learnings, standardize outputs and proliferate outcomes across teams responsible for communications across the customer life cycle.

This brain should empower organizations to create a consistently intelligent and dynamic feedback loop for communications to ensure that testing and training results are applied universally. For example, a GenAI model that acts as a brain for all customer communications may be the biggest unlock to growth across a company’s martech stack.

Beyond improving productivity, such a strategic implementation connecting a single evolving model to various data sources and customer responses could drive substantial revenues.

These productivity and performance gains would stem from a GenAI brain learning which messages or language elements are performing across each communication and applying those insights to the next ones. Data and insights would travel from a cohesive brain to meaningful experiences and back to the brain, ensuring that each output is better than the last.

Getting It Together

Without a centralized source of truth for each disparate GenAI plug-in your organization uses, teams are at a higher risk of repeating A/B tests, confusing each other and customers with mixed voices and messages and ultimately missing out on both efficiency and effectiveness gains as they race to clean up these messes from siloed solutions.

To address this problem, marketing and digital leaders must approach GenAI as a layer rather than a feature that is available for various people to use—much like the capabilities within an email service provider, SMS platform, social platform, content delivery platform, orchestration tool, analytics tool and so on.

As we head into 2024, companies should consider whether they will build or buy a GenAI layer that can connect to their martech stack so that every message or image generated, as well as insight about its performance, benefits all users and touchpoints.

The leaders who manage the delivery of customer experiences across these functions (designers, copywriters, marketing operations, development, etc.) should ensure all their teams are properly onboarded to take full advantage of this evolving brain.

Furthermore, chief marketing officers and chief digital officers must assess their patchwork of GenAI tools and identify the best brain to ensure they’re working together cohesively, from data ingestion to feedback loop. Now that we have amassed our shiny objects, it’s time to get them organized.

Source: forbes