Workflow Transformation 

Respondents widely report that AI has streamlined key aspects of their workflows. This includes lead scoring, call prep/summaries, drafting, and CRM hygiene.

Every single team has a directive to evaluate and trial AI tools to reduce team burden. For example, we’re evaluating point solutions to help with call/meeting summaries, tech spec creation by non-engineers, meeting context-awareness to help document product gaps, and reducing our time to demo (and time to close) significantly.

Christian Eberle, Head of Solutions Consulting at Gladly

However, a minority say AI hasn’t improved much. This suggests that adoption success is context-dependent, often hinged on onboarding and how well the AI tool is integrated into the existing stack.

For many teams, AI has transformed daily operations primarily through Automation and Insight Generation. Teams are gaining more insights into vast amounts of knowledge and data at their fingertips.

But for others, the promised gains just haven’t come to fruition yet. So they’re wondering what the point is. It doesn’t matter if this is the fault of the AI system or just poor onboarding or training. If you can’t get your people to happily adopt and use a new system, it becomes a waste of time and money. 

Girouard stated that “AIs like 1up has saved up quite a bit of time in our security questionnaires, RFI, and RFP processes. It’s been extremely helpful in onboarding.”

AI + Internal Tools 

Here’s what folks had to say about how deeply AI tooling is being connected to internal platforms:

• Not connected: 40%
• Yes, integrated: 24%
• Planning to integrate: 20%
• In the process of integrating: 16%

While a majority are moving toward integrations, 40% are still working in silos. Tool fragmentation and weak APIs remain blockers.

AI has a long way to go before it’s fully embedded in business tech stacks.

Our take on this is that there isn’t much to lose by not integrating. That has been true of SaaS since day 1. It’s always been easy to just keep doing what you’re doing, even if failure to integrate software presents issues like data duplication and redundant workflows.

A lot of this comes down to how disconnected company data lakes are from new AI systems. The bridge between those systems is still missing in many cases, and that creates real adoption hurdles. It may be the AI tools that get a bad rap for “not being useful.” 

To get the most out of this technology, sales teams need to prioritize integration and training. They’ll have to work toward adoption by showcasing the value to their employees to increase interest. Investing in middleware, robust APIs, and collaboration across teams can help bridge these gaps. 

AI in Your Product 

Across all industries, here’s what Sales Teams say about AI’s role in their own products:

• Using but evaluating: 32%
• Still considering: 28%
• Not considering: 28%
• Using AI as core: 12%

While only a small segment views AI as “core,” the majority are actively evaluating or piloting. Sales feedback loops (internal testing, prospect responses) are now informing product roadmaps.

It’s clear that many companies are in the “testing and learning” phase, gathering internal data and customer feedback to shape future development. Sales teams play a critical role here, as customer insights feed into product strategy. 

This approach helps de-risk AI investments. It ensures alignment with the needs of real users and more importantly – paying customers. As more organizations recognize the competitive edge AI can offer, we’re likely to see that 12% Using AI as Core ramp up.

Trust in AI Outputs 

Here’s what hundreds of respondents say about trusting AI outputs (it doesn’t look very good):

• Trust but verify: 41%
• Mostly trust: 32%
• Do not trust: 25%
• Fully trust: 2%

Trust remains tentative, with most users verifying before acting. Sales requires accuracy, and teams are building human-in-the-loop workflows to calibrate quality.

Holly Girouard of FusionAuth states that “We do a pass on every AI response since we see that every AI has a tendency to hallucinate, so we always go through to make sure the information is accurate and revise answers.”

Pradeep Nayar from WalkMe goes further and says he does not trust AI, saying:

Due to the legal and compliance implications of RFX responses, every AI-generated answer is thoroughly reviewed. We’re also subject to internal audits, so ensuring accuracy and completeness is critical. AI is used to support—not replace—expert review.

Pradeep Nayar, Global Solution Advisory @ WalkMe

We could have guessed that full trust is going to be extremely rare as it should be. It is a bit surprising that 25% of sales professionals don’t trust AI at all.

The mostly cautious optimism we’ve seen is likely what has led to the hybrid workflows most teams are seeing now. Reps use AI for call summaries, email drafts, and opportunity scoring with ease. But final decisions, especially in client-facing moments, continue to be human-led. 

Trust tends to grow over time, particularly when AI proves itself accurate and helpful in context. To build confidence, we’re seeing teams focusing on transparency, audit trails, and feedback loops that help AI tools learn from being corrected.

Christian Eberle of Gladly notes an important point: 

The more data we’ve pointed AI at, the more comfortable we are with the result. RAG-based AI is the most basic requirement in order for us to trust results from a tool, and when used in combo with robust prompt libraries, can provide exceptional results.

Christian Eberle, Head of Solutions Consulting at Gladly

Dedicated AI Budgets 

Across different industries, here’s how AI budget is broken down:

• No budget at all: 40%
• Part of larger innovation budget: 22%
• Ad-hoc spend: 22%
• Yes, dedicated: 16%

Most teams are still funding AI opportunistically rather than strategically. This hinders long-term planning and slows rollout of deeper integrations.

Eberle states that leadership has moved away from “try it, but as low cost as possible” to now “we have to use it, find the right value and we’ll make the cost work if needed.”

This data suggests that many organizations are still in the experimentation phase. They’re hesitant to commit until they see real opportunities for ROI. Without a dedicated budget, AI efforts risk being piecemeal. They’re driven by short-term needs rather than a cohesive vision for transformation across workflows, products, and customer engagement.

AI and Procurement Processes 

Here’s what Sales Pros had to say, across all sorts of industries, about their AI procurement process:

• Same as other tools: 30%
• Not sure: 26%
• Depends: 24%
• Yes, distinct procurement: 20%

Procurement remains a gray area. New workflows are emerging for AI risk assessment, security checks, and ROI calculations.

This lack of consistency reveals how new and fast-moving the AI space still is for so many people. So many companies haven’t sat down to formalize how they vet, approve, or even scale AI tools. 

At the same time, forward-looking teams are adopting clear AI procurement tracks. The point is this: as AI spending increases, businesses will need to standardize frameworks for how to evaluate new vendors. In a recent study, we analyzed thousands of questionnaires to find the Top 20 questions being asked of AI vendors.

Buyers are not just looking at features and price, but at compliance, governance, strict data privacy, and alignment with their country laws.

That shift brings its own frictions, like:

• Bespoke AI risk assessments for each vendor
• Extra layers of questionnaires and internal review cycles
• Increased involvement from legal teams and a rise in contract redlines

Want to standardize on questions to ask AI vendors? Download this template for the most common AI questionnaire responses.

Presales Autonomy 

We asked more than 100 presales professionals, here’s what they had to say about their teams’ decision-making power when it comes to AI:

• No autonomy: 50%
• Partial autonomy: 42%
• Full autonomy: 8%

At least among presales teams, experimentation is slow. Teams want to move fast. But they often hit procurement walls.

Many teams have partial autonomy, meaning that they still have to run their tools through IT or some other approval process.

Executives should understand that partial autonomy isn’t enough to support rapid testing of AI tools that could improve demos, discovery calls, or technical evaluations. Forcing teams to navigate lengthy approval chains, justify spending, and compete with other departments for budget is a non-starter. 

It slows momentum and discourages experimentation. Yet presales sits at the intersection of product and customer needs, which makes it a prime candidate for gains that can be made with AI. 

Empowering these teams with more decision-making power could unlock faster adoption and increase customer engagement.

However, Nayar makes a good point about the chaos of purchasing decisions:

Allowing individual teams to procure tools independently leads to maverick buying and software sprawl. All software purchases should go through a structured evaluation involving relevant departments—presales may drive the business case, but IT ensures technical fit, Security conducts risk assessments, and Procurement manages commercial terms.

Pradeep Nayar, Global Solution Advisory @ WalkMe

Who Owns the Budget?

Only 28% said sales leadership owns the presales budget, with a small amount saying GTM leadership owned the presales budget as well. The rest were either unsure or pointed to shared/unclear ownership. This creates confusion and slows decision-making.

When budget ownership is unclear, accountability will inevitably suffer. Presales teams often find themselves stuck between sales, marketing, and operations. Of course, each has different priorities and procurement processes. This ambiguity then delays purchasing decisions, with the added bonus of making it harder to advocate for the tools’ presales actually need to succeed.

The solution? 

The primary goal here has to be establishing a clear budget owner when it comes to AI tools. 

Shared ownership can work if it’s supported by strong collaboration and clear decision rights. But more often, it leads to watered-down initiatives and unclear roadmaps. Without a single point of ownership, it’s difficult to plan strategically, track ROI, or build long-term capabilities.

Security & Compliance as a Blocker 

Security and compliance are major considerations, and occasionally blockers, for AI rollout. That said, most teams view them as necessary, not optional.

Girouard emphasizes that she is “particularly cautious when using AI since I don’t want any AI training on our proprietary information.” 

Security and compliance concerns are often the final hurdle before AI tools can be deployed. Legal, IT, and security teams have to evaluate how data is handled, stored, and shared, especially when customer or proprietary information is involved. 

This scrutiny, while time-consuming, is essential to trust and compliance.

Most companies now recognize that AI adoption must include strong governance frameworks. Rather than viewing compliance as a blocker, forward-thinking teams treat it as a core design constraint. Building security into AI workflows from the start helps streamline approvals and reduce friction down the line.

When handled well, security protocols can give organizations the confidence they need to scale AI initiatives more aggressively.

Christian Eberle of Gladly emphasized that it doesn’t need to be a very long process to adopt AI tools. He advises companies to:

Start with the use-cases that require the least approval from security/compliance teams, they’re everywhere. Once you build trust and understanding about how AI can work for you and your org, lean into a few well-defined and well-scoped use-cases.

Christian Eberle, Head of Solutions Consulting at Gladly

He adds, “You will have earned the right level of knowledge, trust, and experience to quickly roll out the really impactful use-cases. And this isn’t a years long, or sometimes even months-long process – find ways to work and learn quickly. You can do this in a matter of weeks.”

Insights and Takeaways

These stats reflect where sales teams are aligned in their AI adoption and where they’re facing friction. Let’s zoom out to find the broader themes that connect tool usage, team behavior, and organizational dynamics. 

By understanding these patterns, sales leaders can better navigate the AI tech landscape. They can also make smarter decisions about where to invest, experiment, and scale.

What’s Holding Teams Back?

1. Lack of clear budget ownership: Many respondents weren’t sure who owns the budget for AI tools, especially in presales. This ambiguity can slow down purchasing decisions and create bottlenecks.
2. Fragmented tools with weak integrations: Teams are using multiple AI tools. But when they have to deal with poor integration of internal systems, they may suffer from inefficiencies and duplicated work, not to mention data silos.
3. Low autonomy in presales teams: Most presales professionals have little to no purchasing power. Their ability to test and implement tools is limited when it comes to improving productivity or accelerating deals.
4. Fear of compliance missteps: Security and regulatory concerns are obviously crucial. Unfortunately, a hyperfixation on security leaves companies hesitant to fully embrace AI, even when the benefits are clear.

What’s Working Well?

1. Content generation, personalization, and call prep: AI is great for helping sales teams generate personalized emails and pitch decks. It’s also ideal for helping craft talking points tailored to specific prospects. And it’s doing this quickly. That means reduced prep time before meetings or calls. 
2. Research and summarization: Tools like ChatGPT and CoPilot are widely used to draft follow-ups and summarize long emails. They can also distill call transcripts into actionable insights. This translates to a huge speed-up in communication. A tool like Perplexity is also helpful for researching prospects.
3. Workflow and knowledge automation: Internal knowledge is now being unified and made accessible via search. Knowledge automation platforms are improving answer generation and internal search, making it easier for reps to find accurate info without digging through docs or pinging teammates.
4. CRM hygiene and lead scoring (when data is clean): This section crosses over into the marketing side of the  GTM organization. AI assists in cleaning up CRM records, tagging contacts, and prioritizing leads. But it’s only effective when the underlying data is accurate and consistently maintained.

Recommendations and Looking Ahead

For Sales and RevOps Leaders:

To maximize the impact of AI tools, sales leaders should encourage prompt experimentation and develop prompt fluency across their teams. 

Also, assign clear ownership of each tool and offer training. That way, your team won’t lose interest in the tools as soon as they learn about them.

Empowered, well-trained teams are more likely to integrate AI meaningfully into their daily workflows.

Make sure leaders prioritize regular feedback loops. You want to understand what’s working, what’s not, and why. 

Create internal champions or “AI power users.” This gets your whole team on board and introduces the concept of practical use cases. 

Most importantly, ensure leaders align AI initiatives with measurable sales goals like win rates, deal velocity, or rep productivity. It will reinforce both relevance and ROI.

For IT and Security:

On the technical and operational side, you need to build secure, scalable APIs to internal data sources. Without reliable access to CRM, sales enablement, and analytics platforms, AI tools can’t do their jobs.

Create AI-specific procurement frameworks. It will streamline evaluation and approval processes. This helps organizations move faster while staying compliant with security and governance standards.

It’s also critical you integrate your AI tools into existing systems. Plan carefully, standardize data formats, create data pipelines, and ensure real-time syncing. 

Without these integrations, your AI won’t be as effective because your data and insights will be outdated. 

Of course, you want to establish clear governance protocols around AI usage. This will maintain the lifecycle from pilot testing to full-scale deployment. These protocols should address data ownership, decision-making processes, and ethical considerations. 

We have to make sure AI is used responsibly and that it aligns with the organization’s long-term goals. 

That’s how you’ll capitalize on the full potential of AI tools. 

In short, AI has moved from novelty to necessity in the sales world, but not without friction. 

Trust, integration, and clarity around budgeting remain major hurdles. Still, the appetite is strong, and teams are actively building stacks that suit their specific workflows.

To stay competitive, sales organizations should:

• Embrace a culture of experimentation
• Formalize trust frameworks
• Invest in tools that truly integrate into daily life

Looking ahead to 2026, we expect:

• Increased consolidation of tools
• Emergence of AI-native sales platforms
• Rising demand for explainability and compliance tooling

The future of AI in sales is about so much more than automation. The truth is it’s about augmentation with accountability.

Glossary

Survey Methodology: Conducted in Q2 2025 across 500+ respondents

Question List: Available upon request

AI & Machine Learning Terms

• AI (Artificial Intelligence): Computer systems performing tasks typically requiring human intelligence, such as language understanding, prediction, or decision-making.
• AI-generated outputs: Content or decisions produced by AI systems, such as emails, summaries, or recommendations.
• Machine learning models: Algorithms that improve automatically through data and experience, often powering AI tools.
• Prompt engineering: The process of crafting effective prompts to optimize AI outputs.
• Hallucinations: AI-generated content that is factually incorrect or nonsensical.
• Human-in-the-loop: A system where humans supervise or intervene in AI decision-making to ensure accuracy or relevance.

AI Tools & Platforms

• ChatGPT: An AI-powered conversational tool by OpenAI, used for content generation, summarizing, and more.
• CoPilot: A productivity-focused AI assistant (e.g., Microsoft’s GitHub Copilot).
• Gemini: A Google-developed generative AI model/platform.
• Salesforce:  A leading CRM platform, integrating AI for automation and analytics.
• Level AI: A tool often used for customer interactions, analytics, and voice intelligence.

Software & Systems

• CRM (Customer Relationship Management): Software used to manage customer interactions, sales pipelines, and data.
• Productivity tools: Digital tools that improve work efficiency, often integrated with AI features.
• Internal platforms: Proprietary tools or systems used within a company, sometimes connected to AI solutions.
• Middleware: Software that connects different applications or systems, helping with integration and communication.

Workflows & Automation

• Automation: Using technology to perform tasks with minimal human intervention, such as lead scoring or email drafting.
• Onboarding: The process of training users and implementing tools within an organization.
• AI tool integration: The act of connecting AI tools with other software for a seamless workflow.
• Call prep: Preparing for sales or customer calls, often assisted by AI insights or summaries.
• Lead scoring: A method of ranking potential customers based on their likelihood to convert, sometimes powered by AI.

Data & Infrastructure

• Data silos: Isolated data systems that hinder collaboration and visibility across departments.
• Security risks: Potential vulnerabilities in data protection, often increased by poor integration or excess tools.
• APIs (Application Programming Interfaces): A set of protocols allowing software components to communicate.
• Audit trails: Records of system activity used for monitoring, compliance, or debugging.

Sales & Business Processes

• Sales enablement: Strategies and tools that help sales teams sell more effectively.
• Presales: The technical evaluation and demo stage in a sales process, often enhanced with AI tools.
• Tech stack / AI stack-building: A collection of technologies used together, especially multiple AI tools for different functions.
• Budget ownership: The person or team responsible for financial decisions, such as procuring new tools.
• Procurement processes: Procedures for evaluating and purchasing software, including AI tools.

Acknowledgements: Thanks to the analysts, survey participants, and expert contributors who helped shape this report.