Today’s AI-empowered B2B buyers use large language models (LLMs) to configure solutions and evaluate providers. In response, commercial leaders should adapt sales strategies to this new buyer journey while using AI to improve sales planning and execution. In this series, learn how leading organizations use interdependent levers to adapt their go-to-market models for an AI-first world.
While you’re perfecting your sales pitch, your buyers are asking artificial intelligence (AI) agents which vendor to choose. AI and LLMs compress their research and evaluation processes, providing summaries and comparisons of multiple vendors at once. Buyers can compare more vendors in less time, meaning oftentimes they’ve already made many more of their purchasing decisions before contacting your sales team.
Most commercial leaders aren’t ready for AI’s impact on how they sell. Adapting to this environment requires an AI-powered operating model that accommodates modern buyers’ AI-driven processes, while simultaneously incorporating AI-powered tools and infrastructures within the sales organization to enable more efficient and effective deals.
While many companies struggle to adapt, the sellers pulling ahead aren’t just aiming to save time. They begin with the question, “How do we improve the buying experience so sales grow faster?”
This can be achieved with an operating model shift that provides sales leaders with seven interdependent, future-ready levers of control:
To understand the importance of these levers, we’ll first explore why this operating model shift is necessary. We’ll then show how successfully deploying the seven levers can help your organization meet AI-empowered buyers with speed, relevance and confidence.
The value at stake
Organizations are clearly seeing and feeling these shifts. How are they responding? In late 2025, we surveyed commercial leaders across multiple industries to find out.²
We analyzed performance and found that while plenty of respondents reported high growth or low cost of sales, relatively few report both.
Sales organizations with the highest growth-to-cost ratio had several characteristics in common:
Efficient systems and processes
Their efficient systems and processes help sales reps consistently exceed quota. As a result, each rep generates 81% more revenue than their counterparts at low-performing organizations.
Centralized revenue operations
By centralizing revenue operations (RevOps) responsibilities and infrastructure, their leaders have better visibility for smarter strategic decisions.
AI-powered tools
They’re moving past pilots and putting AI-powered tools in the hands of sellers—and compared to low-performing organizations, they're 8x as likely to be realizing gains from AI already.
These shared characteristics aren’t coincidental. AI-powered tools support more efficient systems and processes, allowing sales reps to spend less time on administrative tasks and more time on revenue-generating activities. AI is the common foundation that encourages high growth and low cost of sales.
We found that companies fall across a broad continuum of AI adoption, with most still in the early stages:
are piloting, running isolated experiments and measuring time saved
are scaling, expanding proven use cases across teams
are realizing measurable gains, connecting AI investments to outcomes
The practices and results of companies with the highest growth-to-cost ratio are adaptable at any scale, in any industry. So why aren’t more organizations succeeding? Often, companies try to fit AI tools into existing processes: AI assistants to draft emails, call summarization, simple knowledge chatbots. These individual tools can offer value, but true transformation stalls because workflows aren’t redesigned, data isn’t ready, and leaders are unable to measure meaningful metrics like revenue impact.
New levers to accelerate performance
Effective sales organization transformation depends on coordinated change that solves today’s challenges and prepares for tomorrow’s. It’s not about building an entirely new commercial engine. It’s about embedding new levers of control.
We’ve identified seven key levers that can help meet the realities and opportunities of the agentic era. Individually, these levers harness the potential of agentic AI to improve speed, agility and scalability as your operating model shifts. Together, they can continuously help elevate the performance of your people, extend the value of your tech stack, and streamline the efficiency of your processes.
Over the coming months, we will expand this perspective with a dedicated chapter for each of the seven levers. In the meantime, this overview highlights the core dimensions of a successful operating model shift.
To achieve sales transformation:
Create a decision engine that turns fragmented data into intelligence, so you can use that information to make better decisions at every point in the revenue pipeline.
Empower smaller, more specialized teams to sell across digital, partner and human channels. Use AI to handle the volume and humans to provide judgment and build trust.
Rebuild end-to-end commercial processes so AI drives the selling motion and humans step in where judgment and trust are required.
Integrate AI-enabled planning that adapts continuously to real signals and connects core components such as capacity, territories and forecasting to improve decision-making.
Create an architecture around anchor platforms you already own with modular and extensible solutions that can help avoid vendor lock-in as technology evolves.
Redefine the workforce with clear human and AI role definitions, backed by redesigned workflows, escalation paths and training.
Use revenue operations (RevOps) to drive alignment across data, GTM planning, execution, enablement and reporting.
In this framework, each lever builds on the one before. Better intelligence produces better decisions, which generate better outcomes, which feed better data, which trains better models. Over time, this compounds into advantages that competitors can’t replicate by simply purchasing the same technologies.
The window to lead is still open. Organizations that take action now can gain a competitive advantage and see real outcomes—both now and into the future.
The evidence
The framework we’ve described isn’t hypothetical. Real companies are already using it to drive results.
CASE STUDY 1
BETTER PROSPECTS FOR A LEADING RETAIL BRAND
A leading retail brand sought to expand its B2B sales by identifying and targeting new small- and medium-sized business (SMB) customers based on ideal customer profiles.
Deloitte supported creation of look-alike models leveraging existing high-value customer data to identify new, comparable SMB prospects. Each prospect record was enriched with first- and third-party data.
Ultimately, the client expanded its prospect universe by 10x and was able to prioritize those with the highest predicted value.to-market.
CASE STUDY 2
ENHANCED EFFICIENCY AND COST SAVINGS FOR A GLOBAL TECH COMPANY
A global technology company’s sales organization was drowning in administrative tasks. Reps were spending 70% of their time hunting for information instead of selling, juggling fragmented tools and struggling to personalize outreach at scale.
Deloitte deployed an AI sales agent that gave 1,500+ reps a single, intelligent workspace, including context-aware insights tailored to each seller's territory, AI-generated templates on demand and embedded learning in the flow of work.
The results speak for themselves: 25,000+ tasks completed, 20,000+ hours of seller time reclaimed, and $5M+ cost avoidance redeployed in sales operations spend—all within nine months.
Looking ahead
These seven levers are designed to produce continuous improvement over time. Every deal outcome trains the intelligence layer and sharpens agentic workflows. Regular feedback loops improve planning precision, and as time goes on, organizations can make better decisions with the same resources. Committing to this model now can create a compounding competitive advantage that will benefit your organization now and into the future.
SOURCES
1. Loganix, “73% of B2B Buyers Use AI Tools in Purchase Research, Multi-Source Analysis Finds”, PR Newswire, 2 April 2026.
2. Statistics cited in the discussion of Deloitte-sponsored research are based on a blind survey commissioned by Deloitte Digital and conducted by Gerson Lehrman Group in November and December 2025.
The 453 respondents met the following criteria:
