Technology is the heart of revenue-critical teams’ roles in the modern business landscape. As tech has become more important to these employees’ day-to-day tasks, the number of platforms, tools, and software programs any given enterprise uses has grown significantly. The average employee now finds themselves drowning in disjointed tech tools and less-than-seamless processes.
Outdated and fragmented tech stacks are now hindering sellers’ successes more than supporting them. When information is siloed, sellers are unable to connect the dots they need to clinch deals and drive revenue consistently and repeatedly. Boston Consulting Group estimates that organizations lose an average of $2 trillion a year due to less-than-desirable go-to-market execution.
This reveals the damage that may be done when organizations take a “bag of parts” approach to their tech stack, wherein they invest in individual solutions without regard for the end-user experience of using all these platforms together. Believe it or not, it’s a route that’s incredibly common—even in today’s market, where integration and end-to-end insight are a top priority. In fact, a survey revealed that nearly half (40%) of sales leaders admit that their teams regularly jump between four to five (and sometimes more) applications throughout the day.
There’s no question that the sales tech stack is ripe for consolidation, especially as emerging AI tools grow increasingly adept at intelligently culling and analyzing disparate records into a holistic view of buyer signals and seller actions. AI tools built with seller workflows at the core not only allow revenue organizations to do more with less but minimize swivel-chairing, create purpose-built workflows, automate routines, and deliver greater ROI back to the business. Even better, these tools can help simplify the integration process, eliminating obstacles that commonly stand in the way of digital transformation.
Using AI to Accelerate Integration
As generative AI (genAI) has gained popularity across sectors—and even for individual users—the technology’s utility for research, composition, and image generation has become increasingly clear. However, tools like ChatGPT and Gemini only scratch the surface of what AI can do. Specialized AI models trained on sales-specific data sets and designed for the unique challenges sellers face can go far beyond helping with an email subject line or providing a summary of a company’s history.
With a company’s sales data at their figurative fingertips and access to market insights and intent signals, they act as an accelerant to growth. They can increase the value of client relationship management (CRM) and other enterprise platforms. Rather than just acting as a system of record, AI-enabled revenue orchestration platforms prove, once and for all, that the whole is much greater than the sum of the parts. These systems bring together insights from seller engagement, third party signals, conversational AI, deal management, and more to paint a clearer picture of what needs to happen next. Then, they make it happen with AI that prioritizes, automates, and even acts on the right steps to advance a deal.
Using AI to automate administrative tasks, data input, and analysis to free up sellers’ time to focus on more meaningful and strategic endeavors. As an additional benefit, well-built and trained automations can also help companies improve their records’ accuracy and precision by eliminating human errors, taking more precise measurements, reconciling duplicative records, and allowing all of these activities to happen in real time. The sum of these capabilities is the ability to transition traditional reporting and analysis from anecdotal observations into actionable insights that can be implemented as they are uncovered.
However, this only works when a business takes a thoughtful approach to adoption, building its stack with strategically chosen solutions and toward a specific outcome. And it’s here that many sales organizations begin to lose the thread.
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Building a Balanced AI Ecosystem
Because of AI’s many apparent benefits, organizations have been eager to adopt and deploy the emerging tool. Thanks to today’s oversaturated market, though, that process has too often been guided more by trends than tactics.
Take generative text (like ChatGPT), for example. It’s an impressive tool with countless applications in sales and elsewhere. Some vendors are using it to aid sellers’ research by scrubbing social media to compile comprehensive backgrounds on prospects and inform outreach messaging. It’s cool—to be sure. But does it add value? Not really. It’s a solution to a problem that sellers don’t actually have. And, considering the chair-swiveling many sales teams experience, investments in tools like this can actually be more harmful than helpful. It’s just one more platform to add to the day.
The key to unifying the sales tech stack is to outline a detailed, outcome-led investment and integration roadmap. Before adding any solution to the toolkit, sales leaders must be able to answer “yes” to all of the following questions:
- Does it solve a real problem?
This must be the first stop. The social-media-scrubbing AI described above is unlikely to help sell industrial equipment. It might, though, help someone identify influencers for a marketing campaign. The key is figuring out where your company stands. - Is solving that problem a priority?
If it does solve a problem you have, the next thing to ask is whether finding a solution is important right now. AI integration takes time, and you will have to pick and choose what to address. Speaking to employees to see what affects them most can help you prioritize solutions to ensure you’re getting the most value along the way. - Does it complement the rest of our tech stack?
Now, you can consider how it fits into what you already have—or intend to have. You should consider whether you already have a tool that could accomplish this same thing, whether there are other options that include this feature and another desirable capability, and whether it has the kind of interoperability you need to add it to your system. Often, it’s better to wait for the right fit than to settle for the first option. - Does it help us make smarter decisions?
The reason it’s better to wait is because of this final question. When tech stacks are disjointed, it can impede the insights they offer—and every AI investment should really be an investment in your overall analytics capabilities. If a solution does all of the above but doesn’t support continuous improvement, it may not be worth the resources that go into implementation.
The Bottom Line
Fragmented and overcomplicated tech stacks are preventing sellers and their organizations from optimizing revenue. Streamlining stacks with a strategic emphasis on AI integration can solve many of these issues. AI’s capabilities in streamlining workflows, improving data quality, providing actionable insights, and ensuring seamless integration with current tools make it crucial for sustained revenue growth. As they do so, though, organizations must ensure that AI tools address real problems—and have the flexibility and resources they need to continue to do so in the future.
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