Siloed No More: How AI-Driven Tech Stack Consolidation Boosts Revenue

By leveraging AI to optimize sales workflows and maximize revenue, organizations can go from an inefficient ‘bag of parts’ to AI-powered synergy.

Revenue-critical employees rely on technology to do their jobs, but outdated and disjointed tech stacks are hindering their organizations’ ability to consistently and repeatedly master revenue. Forty percent of surveyed sales leaders say their sellers are swivel-chairing between four to five (or more!) applications to perform their job. These siloed systems hurt efficiency and lead to revenue leak: Boston Consulting Group estimates that organizations lose over $2 trillion per year in missed revenue capture, sales waste, and lost enterprise value.

Many organizations have what I call a ‘bag of parts’ approach, wherein they string together multiple products without considering the end user’s actual workflow and whether or not it will make them more successful.

Compare marketing and sales, for example. Marketing is full of specialists (SEO, ABM, email marketing, etc.), so it makes sense to have specialist tools. Sales, however, has a much more generalist workflow. Sellers do lots of things to close a deal, from emails, calls and presentations to updating the CRM and forecasting, and these are typically not discrete workflows because they deal with the same information. When that information and data is spread across four or five different apps, sellers end up context-switching, getting interrupted, losing critical insights, and ultimately dropping the ball—which is very human—but results in lost time, productivity and revenue.

There is a critical need for consolidation and integration in the sales tech stack. Unsurprisingly, with consolidation and integration comes greater visibility and efficacy. It’s also where AI can perform the yeoman’s share of the work. AI tools built strategically with the seller workflow at the core not only allow revenue organizations to do more with less, but they have the ability to intelligently cull and analyze once disjointed data into a holistic view of buyer signals and seller actions while minimizing swivel-chairing between tools, creating purpose-built workflows, and delivering greater ROI back to the business.

AI as a Tech Stack Accelerator

With the popularity of tools like ChatGPT, DALL-E, and Midjourney, most of us are aware of AI’s power to assist with research, write text, and generate images within seconds. But the breadth of AI’s capabilities stretch far beyond that, and when combined with the existing tools in your tech stack, they act as an accelerant. AI-powered solutions can help you make the most of your CRM and other tools, embodying a true ‘better together’ approach. Below are a few things AI can accomplish to ensure your CRM and BI investments are fully realized.

1. Ingest information, automate tasks, and create useful summaries. Sellers spend a lot of time on admin work, from inputting data to drafting follow-up emails and action items. AI has the ability to automate many of these tasks. For example, AI can instantly generate call summaries, condense them into a brief paragraph, translate them into action items, and pass that information on to multiple places within the CRM.
2. Improve the quality of your data. One of the biggest CRM problems is that data is often inaccurate or inconsistent because it relies on sellers to input information. This where AI can step in to automate admin work, synthesize data from multiple sources, and provide a cleaner, single view of data. For example, AI can recognize when the phone number in someone’s email signature has changed and automatically update the CRM with their new number—and it can do this at scale.
3. Pull insights out of large data sets and make actionable recommendations. It’s easy to generate reports, but not as easy to generate actionable insights. If you can’t take an action based on information, it’s an anecdote, not an insight. Most technology provides anecdotes. Good AI, however, can synthesize a lot of data and produce accurate forecasts, identify deal risks, and make recommendations about next best actions. There are also tools that take it a step further by generating prioritized and customized to-do lists of the most immediate and impactful actions sellers should take to progress and win their open deals.

Criteria for an Effective AI Strategy

The benefits and transformative power of AI are well documented, but that doesn’t mean organizations should load every AI-powered solution they can find into their shopping carts. There’s a lot of tech out there that looks great but doesn’t perform well—or as my good friend Derek Grant once said: “sells well, but doesn’t success well.”

Generative text, for example, can be applied in dozens of ways. There are generative AI tools on the market that can scrub a prospect’s social media pages, uncover where they went to college and what concert they attended last weekend, and then forcefully wedge that information into an email. But that tactic doesn’t meaningfully impact deal outcomes. It’s not solving an existing seller problem—it’s just a novelty.

AI should help your business be more productive and make smarter decisions faster. You should be looking for solutions that are truly better together rather than one-off solutions that attempt to address one-off problems (but potentially create more problems with extra tools in the stack to log into, disparate or siloed data, etc.). The criteria for worthy AI tools is the same as any software, so before adding new AI to your tech stack, ensure it checks the following boxes:

1. It solves a real problem. AI is only important when it’s the best way to solve the customer’s problem. In most cases, that either means making data-driven, actionable recommendations, or automating low value and/or repetitive work.
2. It’s easy to use. Organizations often aren’t using the tools they have to their full ability. If you want to buy software that employees actually use, it needs to be easy. Research from Yale School of Management Professor Zoe Chance says the number one predictor of human behavior is ease. The easier something is, the more likely people are to do it. We need to apply that knowledge to our tech tooling to get the most of our investments.
3.  It integrates well with your other solutions. AI needs to play well with the other solutions that you use, meaning both workflows and data are integrated so users can complete end-to-end workflows. If it doesn’t integrate well, it likely won’t be used because it’s not easy.
4. Governance aligns across platforms. When it comes to selling, most organizations monitor who gets to see what information around a deal. If governance isn’t consistent across all your tools, you don’t really have governance at all.

When these criteria aren’t met when purchasing AI solutions—or any solutions, for that matter—organizations end up with a bag of parts.

The Bottom Line

Disjointed and bloated tech stacks are hindering sellers and their organizations from mastering revenue. Tech stack consolidation, particularly with a strategic focus on AI integration, holds the key to addressing many of these challenges. AI’s ability to streamline workflows, enhance data quality, provide actionable insights, and facilitate seamless integration with existing tools makes it a vital component for long-term revenue generation. However, organizations must ensure, as they do with any new product, that new AI tools are solving a problem and not just there for the sake of empty innovation.

In the ever-evolving landscape of technology and sales, prioritizing an AI strategy aligned with this criterion is essential for organizations to unlock their full potential and stay competitive in the pursuit of mastering revenue.