Solving the AI productivity paradox

According to a new survey, 54% of sales leaders are intent on accelerating time to revenue this year, but an equal share (56%) say poor integration of workplace tools is a key barrier to that goal. 54% of sales leaders are intent on accelerating time to revenue this year, but an equal share (56%) say poor integration of workplace tools is a key barrier to that goal. Notably, AI plays a role in that growing disconnect. When asked how the technology fits into their go-to-market workflows, just 9% say AI is fully embedded into core workflows and regularly used to support decision-making. 

For businesses keen on using AI to accelerate time to revenue, fragmentation is the biggest blocker, creating a pesky AI productivity paradox, where the very tools intended to boost productivity end up widening the gap between revenue priorities and day-to-day sales execution. 

How can businesses close the gap? It takes a mindset shift, an investment in connected systems, and purposeful AI integration. 

Fragmented technology can’t close the revenue-execution gap

AI has the power to help businesses accelerate time to revenue, but it can’t work miracles on broken systems. If businesses keep trying to layer AI on top of already disconnected technology environments, then they’re likely to compound the very problems they’re trying to solve. 

Besides complicating AI integration, there are other ways siloed systems create drag. 

For one, fragmented technology pushes the burden of coordination onto people. Without systems that can talk to each other and share data, staff are left doing the connecting work. That usually takes the form of laborious reporting, where teams manually pull information from disparate systems, reformat it, and stitch it all back together to create a cohesive report.  While AI can help automate parts of this, it’s only as reliable as the data it can access. Without connected systems and proper governance, people are left manually checking if information is current and compliant, while inserting the confidential information AI wasn’t privy to. 

This tedious game of finding and fixing not only eats up valuable time, bogging down sales teams with dreary reporting and pulling them away from more strategic activities, but also drains their attention. Multiple systems, multiple logins, and multiple dashboards create constant context-switching that taxes focus and ultimately cuts into productivity. 

Plus, the more people have to toggle between platforms, the more opportunities there are for important details to slip through the cracks. Many hands in many systems also mean teams risk accidentally duplicating work, further causing confusion and inconsistency. 

Bottom line, an ecosystem of disconnected tools complicates rather than supports day-to-day sales execution, creating shaky ground on which businesses hope to deploy AI. 

Costs of fragmentation add up 

Fragmented technology makes it harder for businesses to meaningfully integrate AI to further revenue goals. Along the way, it also creates hidden costs. 

A chief concern is the productivity lost to hours spent manually fetching files, pulling report data or carrying out research. Besides slowing down operations, this overcomplicated reporting hinders cross-team communication and weakens decision-making. 

For example, when sales teams struggle to showcase their contributions, executive teams are left in the dark about performance. Without access to clear, timely reports from boots-on-the-ground salespeople, leaders risk becoming out of touch with daily realities. Over time, these visibility gaps can have lasting effects on long-term strategy, after all, if leaders can’t see what’s working and what’s not, they can’t know where to pivot and where to invest. 

With fragmentation making visibility murky, it’s no wonder AI integrations aren’t taking root. 

So far, half of revenue leaders (51%) struggle to demonstrate ROI from their current enablement platform, so simply layering AI on top of disconnected systems won’t improve execution. And the longer businesses leave fragmentation unaddressed, the more they lose productivity drains, start making poor decisions, and have unrealised AI potential. 

How to build connected revenue infrastructure

To bridge the divide between revenue priorities and day-to-day sales execution, businesses must commit to building a more integrated revenue technology infrastructure. 

That begins with changing the way leaders think about enablement. 

Already,  6 in 10 report seeing enablement as a core driver of go-to-market performance. But despite this recognition, many businesses still relegate enablement to mere support functions, like serving as a content library. That’s backwards thinking. If leaders want to close the revenue-execution gap, they need to start treating enablement as a strategic link. 

Next, to improve visibility and alignment, businesses should invest in connected systems that give both sales teams and executives greater clarity into performance. Systems that surface the right information at the right moment both simplify reporting and ensure leaders have real-time insights at their fingertips to inform strategy. 

Finally, integrate AI with purpose. 

With fewer than one-tenth of respondents successfully embedding AI into core workflows, it’s clear that attempting to push the technology everywhere all at once doesn’t work. Instead, leaders should favour a controlled rollout. 

Start by identifying high friction use cases where AI can make the biggest impact, such as automated logging, call summaries, and content recommendations. Then, scale integration gradually across workflows, using measurable success as guidance. 

AI has the potential to improve productivity, sharpen decision-making, and accelerate progress towards revenue goals. But without connected infrastructure, businesses risk falling into the AI productivity paradox: more tools, more problems. Instead, leaders should consider investing in systems that unite fragmented technology to connect revenue strategy with day-to-day execution and set the stage for successful AI integration.