How to Increase Sales Productivity in 2027: 5 Ways to Redesign Seller Work for AI

Executive Summary

Sellers still spend most of their week on work that is not selling, and adding AI tools on top of old workflows has not fixed it. Gartner’s May 2026 research shows the teams pulling ahead are redesigning seller roles: AI handles research, messaging, signal monitoring, and next best actions, while sellers focus on the conversations where judgment and trust decide the deal. Organizations that give sellers AI-enabled next best actions are 2.6x more likely to achieve commercial growth. This guide covers five ways to make that shift, starting with the customer data every AI recommendation depends on.

The average seller spends just 40% of the week selling, according to Salesforce’s State of Sales 2026. The other 60% goes to meetings, prospecting, quotes, planning, training, and manual data entry. Gen Z reps fare worse, selling 35% of the time and losing about two hours a week to data entry alone.

AI is changing that equation fast. Gartner predicts that by 2027, 95% of sellers’ research workflows will begin with AI, up from less than 20% in 2024. However, more AI does not automatically mean more output. Gartner also expects AI agents to outnumber sellers 10 to 1 by 2028, while fewer than 40% of sellers will say agents improved their productivity.

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What is Sales Productivity?

Sales productivity is the revenue a sales team produces relative to the time, people, and cost it takes. It combines efficiency (how well time and resources are used) and effectiveness (how well that effort turns into pipeline and closed deals). A team can be busy and efficient yet ineffective if the effort goes to the wrong accounts, and effective yet inefficient if every deal takes twice the work it should.

In 2026 the definition includes AI. Gartner advises sales leaders to shift from traditional productivity measures to metrics that capture both human and AI contributions (Gartner). Productivity is now a property of the whole system of sellers and agents working together.

The Reality of Sales Productivity Today

Your bottom line will take a hit if reps don’t feel productive. Let’s first look at the problems plaguing sales teams today to find solutions.

1. Admin still crowds out selling

Data entry, meeting prep, and CRM updates take the time that should go to buyers. Salesforce’s numbers above show the problem has barely moved in years.

2. AI is layered on top of old workflows

According to Greg Hessong, Senior Director Analyst at Gartner, the most effective sales organizations are redesigning seller workflows so AI supports execution, recommendations, and orchestration, rather than bolting AI onto existing ways of working. When sellers are asked to do everything they did before plus manage new tools, productivity drops.

3. Customer context is scattered

Interactions live across email, calendars, meeting recorders, engagement tools, and Slack. Salesforce found 51% of sales leaders using AI say disconnected systems are slowing their AI initiatives. AI recommendations built on partial context waste seller time instead of saving it.

4. Deals stay single-threaded

Gartner’s 2025 buyer research found buying groups with low dysfunction were 13x more likely to report high-quality deals, and buyers who spent more time with supplier reps reported the least dysfunction. Sellers who cannot see the full buying group cannot manage it.

5 Ways to Increase Sales Productivity in 2027

Here are 5 ways to increase sales productivity in 2027:

1. Redesign seller roles around what AI and humans each do best

Start by deciding which work belongs to agents and which belongs to sellers. AI is well suited to account research, personalized messaging, signal monitoring, and next best actions, while sellers remain differentiated in empathy, judgment, contextual understanding, and value framing. Buyers agree. In a 2025 survey of 645 B2B buyers, buyers were 39 percentage points more likely to say a rep understood their needs than GenAI, and 32 points more likely to say a rep made them confident in the purchase.

Map a typical rep week, tag each task as agent-led, human-led, or shared, and rebuild the role so the time agents free up goes to discovery, multi-threading, and helping buyers build the business case.

2. Remove admin at the source

The fastest productivity gain is eliminating work entirely rather than speeding it up. CRM data entry is the clearest example. Automatic activity capture writes every email, meeting, calendar event, and call into Salesforce, matched to the right contact, account, and opportunity, so reps never log anything. It also gives every AI agent downstream a complete record to work from.

This is the layer Nektar provides, with zero rep behavior change. It captures the complete customer interaction history and context your AI agents need to work.

3. Give sellers next best actions grounded in complete data

Sales organizations that provide sellers with AI-enabled next best actions are 2.6x more likely to achieve commercial growth. The quality of those recommendations depends on what the AI can see. If half the buying group is missing from the CRM, or last week’s executive meeting was never logged, the next best action will be wrong.

Build a centralized context layer connecting enterprise data, systems, and seller judgment before scaling agents. In practice that means complete activity history, every engaged stakeholder on the opportunity with a buyer role, and engagement trends the AI can read.

4. Coach on leading indicators and upskill on AI

Coaching remains one of the highest-leverage manager activities, and AI raises its ceiling. Organizations that prioritize upskilling sellers on AI are 2.4x more likely to achieve strong revenue growth, and it is predicted that AI-driven enablement will deliver 40% faster sales stage velocity than traditional methods by 2029.

Pair AI skills training with coaching on leading indicators that show where a deal or a rep is heading before results arrive: first meetings booked, deals advancing from discovery to evaluation, senior buyers engaged, and stakeholders who have gone quiet. Chainguard’s leadership reviewed exactly these metrics every Monday from automatically captured data, which let managers spot struggling reps within weeks instead of a quarter.

5. Measure productivity for sellers and agents together

Traditional metrics such as quota attainment, win rate, and pipeline coverage still matter, but they lag. Add measures that show whether the human and AI system is working:

Share of seller time spent with buyers
Share of customer interactions captured in the CRM without rep effort
Engaged buyer roles per open opportunity
Adoption and acceptance rate of AI next best actions
Stage-to-stage conversion and cycle time
Pipeline influenced or created by agents

Review them monthly, and treat a falling share of buyer time or a low acceptance rate on AI recommendations as early signs that the workflow, or the data behind it, needs attention.

Give sellers and agents the same picture

Increasing sales productivity comes down to a simple division of labor: agents take the research, admin, and monitoring, and sellers take the moments buyers value most. Both depend on the same thing, a CRM that reflects what actually happened with each account. Salesforce is the system of record. Nektar keeps that record complete, correctly mapped, and continuously self-healing, so sellers stop doing data entry and every AI recommendation starts from the full picture.

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