Revenue-critical employees depend on technology to do their jobs, but outdated and disjointed tech stacks are hindering their organizations’ ability to consistently and repeatedly master revenue.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 4 to 5 (or more!) applications to perform their job. These siloed systems hurt efficiency and result in revenue leak: Boston Consulting Group estimates that organizations lose over $2 trillion per 12 months 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 tip user’s actual workflow and whether or not it can make them more successful.

Compare marketing and sales, for instance. Marketing is stuffed with specialists (search engine optimisation, ABM, email marketing, etc.), so it is smart to have specialist tools. Sales, nonetheless, has a way more generalist workflow. Sellers do a number of things to shut a deal, from emails, calls and presentations to updating the CRM and forecasting, and these are typically not discrete workflows because they take care of the identical information. When that information and data is spread across 4 or five different apps, sellers find yourself context-switching, getting interrupted, losing critical insights, and ultimately dropping the ball—which may be very human—but ends in lost time, productivity and revenue.

There may be a critical need for consolidation and integration within 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 vendor workflow on the core not only allow revenue organizations to do more with less, but they’ve the flexibility to intelligently cull and analyze once disjointed data right 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 recognition of tools like ChatGPT, DALL-E, and Midjourney, most of us are aware of AI’s power to help with research, write text, and generate images inside seconds. However the breadth of AI’s capabilities stretch far beyond that, and when combined with the present tools in your tech stack, they act as an accelerant. AI-powered solutions can assist you benefit from your CRM and other tools, embodying a real ‘higher together’ approach. Below are a couple of things AI can accomplish to make sure your CRM and BI investments are fully realized.

1. Ingest information, automate tasks, and create useful summaries. Sellers spend a variety of time on admin work, from inputting data to drafting follow-up emails and motion items. AI has the flexibility to automate lots of these tasks. For instance, AI can immediately generate call summaries, condense them right into a transient paragraph, translate them into motion items, and pass that information on to multiple places throughout the CRM.
2. Improve the standard of your data. Considered one of the most important CRM problems is that data is commonly inaccurate or inconsistent since it relies on sellers to input information. This where AI can step in to automate admin work, synthesize data from multiple sources, and supply a cleaner, single view of information. For instance, AI can recognize when the phone number in someone’s email signature has modified and robotically update the CRM with their latest number—and it may do that at scale.
3. Pull insights out of huge data sets and make actionable recommendations. It’s easy to generate reports, but not as easy to generate actionable insights. When you can’t take an motion based on information, it’s an anecdote, not an insight. Most technology provides anecdotes. Good AI, nonetheless, can synthesize a variety of data and produce accurate forecasts, discover deal risks, and make recommendations about next best actions. There are also tools that take it a step further by generating prioritized and customised to-do lists of essentially the most immediate and impactful actions sellers should take to progress and win their open deals.

Criteria for an Effective AI Strategy

The advantages and transformative power of AI are well documented, but that doesn’t mean organizations should load every AI-powered solution they will find into their shopping carts. There’s a variety of tech on the market that appears 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 instance, could be applied in dozens of how. There are generative AI tools available on the market that may scrub a prospect’s social media pages, uncover where they went to varsity and what concert they attended last weekend, after which 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 only a novelty.

AI should help your online business be more productive and make smarter decisions faster. You ought to be on the lookout for solutions which are truly higher together fairly than one-off solutions that attempt to deal with one-off problems (but potentially create more problems with extra tools within the stack to log into, disparate or siloed data, etc.). The factors for worthy AI tools is identical as any software, so before adding latest AI to your tech stack, ensure it checks the next boxes:

1. It solves an actual problem. AI is barely essential when it’s one of the best solution to solve the client’s problem. Normally, that either means making data-driven, actionable recommendations, or automating low value and/or repetitive work.
2. It’s easy to make use of. Organizations often aren’t using the tools they need to their full ability. If you need to buy software that employees actually use, it must be easy. Research from Yale School of Management Professor Zoe Likelihood says the primary predictor of human behavior is ease. The simpler something is, the more likely individuals are to do it. We want to use that knowledge to our tech tooling to get essentially the most of our investments.
3.  It integrates well together with your other solutions. AI must play well with the opposite solutions that you simply use, meaning each 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 since it’s demanding.
4. Governance aligns across platforms. With regards to selling, most organizations monitor who gets to see what information around a deal. If governance isn’t consistent across all of your tools, you don’t really have governance in any respect.

When these criteria aren’t met when purchasing AI solutions—or any solutions, for that matter—organizations find yourself 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 give attention to AI integration, holds the important thing to addressing lots of these challenges. AI’s ability to streamline workflows, enhance data quality, provide actionable insights, and facilitate seamless integration with existing tools makes it an important component for long-term revenue generation. Nonetheless, organizations must ensure, as they do with any latest product, that latest AI tools are solving an issue and not only there for the sake of empty innovation.

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