Artificial intelligence is changing sales in a more fundamental way than most technology cycles before it.
For decades, new sales technology made people better at doing the work. CRM systems stored information. Sales-engagement platforms organized activity. Automation connected predefined actions. Analytics helped teams make better decisions.
AI introduces something different.
It increasingly allows parts of commercial work itself to move from human execution to digital execution.
That changes the question for sales leaders. Instead of asking only, “How can our salespeople use AI?”, companies increasingly need to ask:
What work should remain human, what work can become digital, and how should the two operate together?
What AI in sales means in 2026
AI in sales generally refers to the use of artificial intelligence to improve or execute commercial activities such as research, prospecting, account prioritization, buyer identification, communication, forecasting, qualification and sales administration.
Until recently, most applications were assistive.
A salesperson could use AI to summarize an account, prepare for a meeting, draft an email or analyze a conversation. The technology improved productivity, but the salesperson remained responsible for executing the process.
That boundary is beginning to move.
Agentic AI systems can increasingly interpret information, work toward defined objectives, use external tools, complete multi-step activities and operate within organizational guardrails.
This means AI is progressing from helping salespeople perform work toward performing defined parts of commercial work itself.
The distinction is important because AI adoption in sales is already widespread.
Salesforce’s 2026 State of Sales research, based on more than 4,000 sales professionals, found that 87% of sales organizations already use some form of AI. More than half of sellers surveyed — 54% — reported having used AI agents.
The question is therefore no longer whether AI will enter the sales organization.
It already has.
The more important question is:
What happens when AI moves beyond assistance and starts executing work?
The AI adoption paradox
High adoption numbers can create the impression that sales organizations have already transformed.
The research suggests otherwise.
McKinsey’s 2026 research into B2B sales found that while AI experimentation is widespread, fewer than 10% of organizations have successfully scaled AI within any individual function.
That creates an interesting contradiction.
Companies have more access to AI than ever before, yet many are struggling to translate adoption into structural commercial advantage.
Microsoft’s 2026 Work Trend Index points to a similar issue. Based on analysis of Microsoft 365 productivity signals and research involving 20,000 AI users across ten countries, Microsoft found that organizational factors such as management practices, culture and talent structures were critical in determining whether AI produced meaningful impact.
The implication is straightforward:
Using AI is not the same as integrating AI. And integrating AI is not the same as redesigning work around AI.
A salesperson asking AI to draft an email represents adoption.
A digital system researching a prospect represents execution.
A coordinated system continuously identifying accounts, mapping buyers, initiating engagement, managing follow-up and helping qualify demand represents something more significant.
It represents a change in the sales operating model.
Our interpretation of the research is that AI in sales is moving from an adoption problem toward an operating-model problem.
The next competitive advantage will not come simply from having access to AI.
Almost everyone will.
It will come from designing commercial work around it more effectively.
Buyers may be changing faster than sellers
One of the most interesting shifts is happening on the other side of the transaction.
Deloitte’s 2026 B2B commerce research, based on more than 1,000 US buyers and suppliers, found that nearly 40% of B2B buyers already use agentic AI during the purchasing process.
They are using it for activities including product evaluation, contract review and price benchmarking.
Supplier adoption is currently lower.
Deloitte found that 24% of suppliers were already using agents in their sales processes, while another 67% planned to do so.
Based on those reported figures, that suggests an approximately 15-percentage-point gap between buyer and supplier adoption of agentic AI.
That changes the urgency around AI in sales.
The issue is not simply that competitors may use AI before you do.
Your customers are beginning to change how they buy.
Deloitte found another revealing disconnect.
Seventy-two percent of suppliers believed their sales processes were mostly or highly automated.
Only 47% of buyers agreed.
That represents a 25-percentage-point perception gap between how suppliers believe their sales process operates and how buyers actually experience it.
This suggests that many organizations may be digitizing their internal sales activity without meaningfully improving the external buying experience.
A company can automate CRM updates, connect systems and create automated sequences while customers still experience irrelevant communication, slow responses and fragmented interactions.
Technology implementation and commercial transformation are not the same thing.
Why AI is different from previous sales technology
Every major generation of sales technology has changed how commercial organizations work.
But most previous systems had one thing in common:
The human remained the primary executor.
CRM digitized information.
Sales automation digitized predefined rules.
When one event occurred, another action could automatically follow.
These technologies improved scale, visibility and coordination, but the human still performed most of the reasoning required to turn an objective into an outcome.
AI begins to change that boundary.
Modern systems can increasingly interpret unstructured information, synthesize large amounts of data, determine appropriate actions, generate outputs and interact with external systems.
That creates an important distinction.
Previous generations of sales software primarily digitized the tools of work.
AI increasingly allows organizations to digitize parts of the work itself.
That is why AI has the potential to affect sales differently from previous technology cycles.
If software can execute parts of commercial work rather than simply help humans execute them, the economics of how companies allocate sales capacity begin to change.
From tasks to workflows, roles and teams
Most companies begin using AI at the task level.
Draft an email.
Summarize a meeting.
Research a prospect.
Analyze a conversation.
Score an opportunity.
These are useful applications. They can save time and improve individual productivity.
But the largest organizational impact occurs when AI moves beyond individual tasks.
McKinsey’s latest B2B sales research focuses increasingly on redesigning complete commercial workflows rather than deploying isolated AI use cases.
That suggests a progression we believe sales leaders should pay attention to:
Task → Workflow → Role → Team
At the task level, AI performs an individual activity.
At the workflow level, multiple activities are connected toward a commercial outcome.
At the role level, a meaningful group of activities previously assigned to a person becomes digitally executable.
At the team level, the organization changes how human and digital capacity are structured around those workflows.
This final transition is particularly important.
Once AI reaches the workflow and role level, the primary management question is no longer:
“Which AI tools should our salespeople use?”
It becomes:
“How should our sales organization be structured?”
The AD Labs Digital Work Model
At AD Labs, we use a simple model to understand this transition:
Assist → Execute → Orchestrate
At the first level, AI assists.
The salesperson remains responsible for execution, while AI supports activities such as research, analysis, preparation, writing, summarization and recommendations.
This is where many organizations started with generative AI.
At the second level, AI executes.
Instead of simply preparing information for someone else to act on, digital workers begin performing defined commercial activities themselves.
That can include:
- researching accounts;
- identifying relevant buyers;
- monitoring market signals;
- preparing personalized engagement;
- managing repetitive follow-up;
- processing responses;
- supporting qualification.
The human role begins moving from execution toward supervision.
At the third level, AI orchestrates.
Different digital capabilities operate together across an interconnected commercial workflow.
In B2B sales, that might mean moving through a process such as:
Understand the market → Find best-fit accounts → Identify buyers → Start relevant conversations → Qualify demand → Hand sales-ready opportunities to the commercial team.
At this level, humans increasingly provide strategy, supervision, judgment and accountability.
This is the foundation of what we call a Managed Digital Sales Team.
A digital sales team is not simply a collection of AI tools.
It is an operating model in which digital workers execute defined commercial work while experienced people provide the strategy, quality control and commercial judgment required to turn activity into outcomes.
A better way to measure AI transformation in sales
Companies frequently measure AI adoption through metrics such as licenses purchased, users activated, prompts submitted or automations created.
These numbers tell us whether AI is being used.
They do not necessarily tell us whether the work has changed.
We believe commercial leaders should begin asking a different question:
What proportion of our repeatable commercial work can be executed digitally at the required level of quality and control?
We call this the Digital Work Ratio.
Conceptually:
Digitally executed repeatable commercial work ÷ Total repeatable commercial work
The Digital Work Ratio is an AD Labs management framework, not an established industry benchmark.
And the objective is not to drive the ratio toward 100%.
Complex B2B sales depends heavily on judgment, trust, relationships and creativity. Those requirements do not disappear because AI becomes more capable.
Instead, the framework forces a more useful management discussion.
Rather than asking:
“How much AI are we using?”
ask:
“How much human capacity are we still allocating to work that no longer requires human execution?”
What sales work should become digital?
Not every commercial activity is equally suitable for digital execution.
The strongest candidates generally involve some combination of:
- repetition;
- high information volumes;
- continual monitoring;
- structured decision logic;
- research;
- preparation;
- data processing;
- consistent execution.
Account identification is one example.
A digital system can continuously evaluate large numbers of organizations against defined commercial criteria without requiring a salesperson to manually research every company.
Buyer mapping is another.
Information from multiple sources can be processed to identify relevant roles, decision-makers and organizational relationships.
Similar opportunities exist across market research, trigger monitoring, repetitive follow-up, information processing, conversation classification, qualification support and sales administration.
These activities matter.
But as digital systems become more capable, the marginal value of assigning people to manually execute every step becomes harder to justify.
Human comparative advantage remains much stronger elsewhere.
Discovery requires understanding ambiguity and context.
Complex solution design requires creativity and judgment.
Strategic account development depends on relationships and organizational awareness.
Negotiation requires an understanding of motivations and incentives.
Executive relationships depend on credibility and trust.
Closing complex B2B transactions often combines all of these.
McKinsey’s research similarly suggests that as AI handles more searching, synthesizing, drafting and administrative activity, sellers can redirect time toward relationship building, problem solving and judgment-intensive customer interactions.
That leads to an important distinction.
The objective of AI should not be to remove humans from sales.
It should be to remove humans from work where being human adds relatively little value.
The structure of the sales organization starts to change
For decades, increasing commercial capacity generally meant increasing commercial headcount.
If a company wanted to research more markets, identify more accounts, reach more buyers and generate more conversations, it usually needed more people.
That relationship between output and headcount helped shape the traditional sales organization.
AI begins weakening it.
When digital workers can perform more research, monitoring, processing and repetitive execution, companies can increase market coverage without increasing human headcount at the same rate.
The emerging sales organization therefore looks different.
Digital workers increasingly provide:
Scale. Research. Monitoring. Processing. Consistent execution.
Human commercial professionals increasingly concentrate on:
Strategy. Relationships. Discovery. Negotiation. Solution design. Closing.
The future sales organization is therefore unlikely to be human or AI.
It will be a deliberately designed combination of both.
And this has significant implications for the traditional SDR structure.
A typical SDR role combines account research, prospecting, contact identification, communication, follow-up, administration, qualification and conversations.
AI does not affect each of those activities equally.
Research, preparation and repetitive execution are increasingly suitable for digital systems.
Building trust, understanding nuanced requirements and navigating complex buying situations remain considerably more dependent on human capability.
As those different types of work separate, the economic rationale for assigning every stage of prospecting to human SDR headcount becomes progressively weaker.
The better question for sales leaders is therefore not:
“Will AI replace SDRs?”
It is:
“Which parts of the SDR function still require an SDR?”
What sales leaders should do now
The first step should not be buying another AI tool.
It should be understanding the work.
Map the existing commercial process from market selection through account research, buyer identification, engagement, qualification and handover.
Identify where time is being spent.
Identify where execution is repetitive.
Identify where quality varies.
And identify where human involvement genuinely creates value.
Then separate execution from judgment.
Which activities require experience, relationships, creativity or commercial judgment?
Which activities are performed manually largely because no better alternative existed before?
Next, look for complete workflows rather than isolated automation opportunities.
Using AI to draft one email may save several minutes.
Redesigning the workflow through which a company identifies accounts, maps buyers, initiates conversations and qualifies demand can change commercial capacity.
Human supervision should also be designed deliberately.
Digital workers need objectives, standards, escalation rules, approval mechanisms and accountability.
Without management, AI execution is simply automation.
Finally, measure commercial outcomes rather than AI activity.
Ask:
How much additional market coverage did we create?
How much seller capacity did we return?
How many relevant buyer conversations did we generate?
How many qualified opportunities reached human sellers?
What happened to meeting quality?
What happened to pipeline contribution?
What happened to conversion?
AI should ultimately be judged by the same standard as every other commercial investment.
Did it improve the economics and outcomes of the sales organization?
The next competitive advantage will not come from having more AI tools
Access to powerful AI will become increasingly common.
That means the technology itself is unlikely to remain a durable competitive advantage.
The difference will come from how organizations design work around it.
First, AI changes tasks.
Then workflows.
As workflows change, roles change.
And when enough roles change, the structure of the team changes with them.
The companies that gain the most from AI in sales will therefore not necessarily be those with the largest technology stacks or the most AI licenses.
They will be the organizations that become best at deciding:
What work should remain human?
What work should become digital?
And how should both operate together?
That is the transition from simply using AI in sales to building a digitally augmented sales organization.
And it is why the next phase of AI in sales will be much bigger than sales automation.
It will change how commercial work itself gets done.
Where could digital work increase your sales capacity?
For many sales organizations, the biggest opportunity is not replacing the entire existing process.
It is identifying where digital execution can remove unnecessary manual work, expand market coverage and give commercial professionals more time to focus on relationships, opportunities and closing.
The AD Labs Digital Sales Scan examines how your current business-development process operates — from market coverage and account research through buyer identification, conversations and qualification.
We identify where digital workers could increase commercial capacity, where human involvement creates the greatest value, and how both can operate together as one managed sales process.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!