AI ROI Depends on Redesigning Go-to-Market Jobs, Workflows and Talent Models, Not Adding More Technology.
Across business and financial services, AI investment climbed 90% between 2024 and 2025. Yet revenue per rep fell 5% during that same period, indicating that more spending did not produce more output. For CEOs, CROs and heads of commercial excellence in professional and tech-enabled services; HR and human capital; and commercial and industrial sectors, that gap is the real story of AI in the go-to-market environment right now. It also points to a problem that no additional tool will fix on its own.
“Seller productivity is actually flat to down year over year,” said Mike Burnett, partner at Alexander Group. “There’s a ton of activity, a ton of interest and a ton of dollars being poured into AI tools. But we’re seeing organizations really struggle with pulling through the value.”
This disconnect especially raises a bigger question for leaders facing the pressure to drive growth without proportionally increasing headcount: Where should AI create value inside the go-to-market organization?
When AI Spend Rises, But Productivity Doesn’t
When it comes to deploying AI use cases within their salesforce, most organizations are stuck in pilot mode. Despite rolling out the tools, the roles, workflows and expectations around those tools haven’t changed. Therefore, any AI returns haven’t materialized either. Most companies are treating AI as a technology initiative rather than a broader go-to-market initiative, with leaders start with the tools instead of the workflow, the data environment and the job design that would let the tools free up time.
This misaligned approach compounds. When AI does free up seller bandwidth, few organizations have decided in advance where that bandwidth should go. Should reps make more calls and outreach? Conduct more research and account planning? Reengage with existing customers? Sell a broader portfolio?
Without a clear answer, the time savings disappear back into the day. There’s also a talent gap: As administrative work falls away, sellers need to operate more as deal strategists—and managers need to shift from reporting and oversight into coaching and change management. The extra capacity will never turn into revenue without deliberate training.
None of this means that the human role in selling is shrinking. Everyone has acknowledged that the human element to selling and go-to-market isn’t going away. Leading organizations are the ones enabling their teams with AI, not those trying to replace them with it.
The Organization, Not the Technology, Is the Constraint
Alexander Group’s 2026 AI & Go-to-Market Job Evolution research quantifies what happens when organizations redesign the job around AI instead of layering AI onto the job. Companies with AI-enabled roles report 33% more engaged selling time with customers, and top-performing AI-enabled sellers post 9 percentage points higher quota attainment than top-performing traditional sellers. Among reps that use a broad set of AI tools in their daily work (AI power users), that translates into 18% more customer calls, 19% more accounts covered and 23% larger deals. All of these gains simultaneously show up across sales process, customer reach and product and service positioning.
This transformation is only beginning. Survey respondents expect more than half of activities across every major customer-facing role to be AI-led by 2028, ranging from 53% for generalist sales representatives to 68% for lead generation roles. With administrative and process-driven work moving into the hands of AI, human effort and value-add will move toward judgment, influence, coaching and relationship management. Organizations will have to undergo a fundamental redesign of go-to-market jobs, from demand strategists and deal orchestrators to customer outcome advocates and human-AI coaches. In practice, organizations will need to redefine how performance is measured, how capacity is allocated and what skills differentiate top performers.
Proof From the Field: How Leaders are Rewriting GTM Roles
The research includes several examples of what these job redesigns look like in reality.
- A B2B financial data and analytics company embedded AI into manager coaching and deal-oversight workflows. After doing this, the organization saw 40% more seller meetings and 9 percentage points higher quota attainment for AI-enabled managers, along with managers exercising more influence on deals they weren’t personally attending.
- A separate enterprise information services and technology company invested about $200,000 a year to embed AI across its “identify, land and retain” motion. In just eight months, this investment saw a return of 15 times while keeping seller headcount flat.
- A $16 billion tech-enabled services company automated lead qualification and routing, cutting a two-to-three-day manual process to seconds. Conversion rates rose 60% over about one year, and AI-enabled lead gen reps spent 47% more time on live buyer engagement while posting 9 percentage points higher quota attainment.
- A $20 billion technology company launched an AI agent to generate monthly customer utilization reports, significantly reducing the process timeline from five to seven days a month to just around 30 minutes. Customer success managers spent 65% less time on performance monitoring and redirected that energy to spend 31% more time on value realization and account strategy.
- A $200 million enterprise software and services company used AI to aggregate account signals such as job postings and leadership changes, cutting prospecting time from hours to minutes. Sellers spent 26% less time on prospecting and turned that into 33% more time on persuasion. Additionally weekly customer call volume rose 43%.
- A business technology provider facing limited sales engineer capacity deployed a generative AI tool across its products and business units, which cut technical preparation time 20%, lifted win rates 15% and grew average deal size by $50,000. Since this deployment, the organization also piloted an AI-powered avatar that joins customer meetings to answer technical questions in real time.
Collectively, these examples demonstrate how AI can help organizations support more growth without proportionally increasing go-to-market investment.
Three Decisions Leaders Should Be Making Now
There are three workstreams that should run in parallel and leaders should reassess these workstreams quarterly.
- Reevaluate processes. Audit the workflow end to end and decide what AI should own outright versus where human judgment stays. This is a joint effort between revenue operations and IT.
- Reset expectations. Quantify the new productivity ceiling for each role, and rebuild territories, account loads and lead flow to match it. This sits with revenue operations and sales leadership.
- Revamp the talent profile. Rebuild competency models around the skills AI cannot replicate, make AI fluency a requirement in hiring and promotion, and build upskilling into every automation rollout. This is a shared responsibility across revenue operations, sales leadership and HR.
“Don’t fall in love with the idea of just buying more technology. AI investment is not going to drive the growth alone. You have to think of that in tandem with really redesigning all the processes, talent profiles, jobs and workflows.” — Dave Eddleman, principal at Alexander Group
As AI becomes capable of doing more of the work, leadership teams will have to decide where people create the most value. That very decision may shape the next decade of go-to-market performance for business and financial services organizations.
