As enterprise buyers evolve, so must the teams that support them. This report outlines how forward-thinking presales leaders are using AI to drive efficiency, personalization, and scale without compromising expertise.

Executive Summary

B2B buyers have changed the game. Five years ago, over half of buyers wanted to meet sales reps face-to-face; today, that number has plummeted to just 35%​. Customers are increasingly favoring digital research and remote interactions over traditional in-person meetings. In fact, at any given stage of a complex buying journey, roughly one-third of buyers prefer in-person engagement, one-third prefer remote communication (e.g. via video or phone), and one-third opt for self-serve PLG digital channels​. This “rule of thirds” in buyer preference holds across industries and deal sizes, underscoring a new presales reality: technical sellers get less direct face time and must add value in smarter, more streamlined ways.

For presales and sales engineering leaders, the mandate is clear. In an era when enterprise deals involve more touch points and data than ever, AI has emerged as the force multiplier for presales teams. Early adopters are already reaping the benefits. According to a 2024 McKinsey survey of nearly 4,000 B2B leaders, 19% of B2B sales organizations have implemented generative AI use cases in their sales process, with another 23% actively experimenting. Notably, two-thirds of organizations using generative AI in sales report it to be “very” or “extremely” beneficial​. 

Data-driven teams that blend AI with personalization are 1.7× more likely to increase their market share than those that do not​. The takeaway is compelling: AI in presales isn’t hype, it’s driving tangible impact in efficiency, deal velocity, and revenue growth.

This executive report examines how AI is revolutionizing the presales function. We explore the evolving presales landscape, data-backed strategies for deploying AI effectively, and key differences in how startups versus large enterprises approach AI in presales. We also shine light on often overlooked challenges technical sales leaders face when infusing AI into their organizations. The goal is to provide presales executives at global B2B companies from agile high-growth SaaS firms to established Fortune 500 enterprises with concise, actionable insights to inform their AI strategy. 

The future of presales is being written now: those who leverage AI to elevate their teams’ productivity and expertise will gain a formidable competitive edge, while those who lag risk being left behind.

By the Numbers: What’s Changed in Presales

Enterprise buying has become more complex and buyer-driven, fundamentally shifting the role of presales. Customers today progress substantially deeper into their purchasing journey before ever engaging a sales representative. By the time a technical sales team is looped in, buyers have often self-educated via websites, whitepapers, peer reviews, and product trials. 

Presales leaders must now deliver expert guidance and personalization in a fraction of the time and touches they once had to establish trust.

Consider these realities shaping presales today:

• Buyers demand omni-channel engagement: B2B customers use an average of 10 interaction channels in a typical purchase journey (up from 5 channels in 2016)​. They expect to seamlessly pivot between digital self-service, virtual meetings, and the occasional in-person discussion. This omni-channel behavior means presales engineers might interface with a buyer via a product demo, webinar, follow up with technical documentation over email, and support a proof-of-concept not to mention potential on-site visits. The presales function must be adept at delivering value across these channels and keeping messaging consistent. Notably, if the experience falters (e.g. data from one channel isn’t carried into the next), over half of customers will readily switch vendors. High-quality, connected engagement is now a baseline expectation.
  
  

• Less direct access, more decision makers: With only ~35% of buyers favoring in-person sales interactions, presales teams get fewer face-to-face opportunities to build relationships. At the same time, enterprise buying groups have grown larger and more cross-functional, often 6 to 10 stakeholders (from IT, security, finance, etc.) all weighing in on a major tech purchase. This puts pressure on presales to convey tailored value propositions to each stakeholder and to do so efficiently. When direct interaction is limited, every touch must count. Presales engineers need instant access to relevant insights (e.g. industry-specific case studies, ROI data, technical FAQs) to address diverse concerns and prove value to all parties.
  
  

• High stakes technical validation: Especially for complex B2B solutions (e.g. cybersecurity software, analytics platforms), the presales stage is where deals are won or lost. Demos, technical deep-dives, and trial programs are under intense scrutiny as customers evaluate not just what a product does, but how it will work in their environment solving their pain-points. Any misstep from  a sluggish demo, an unanswered question about integration, an inability to quantify business impact can and will sow doubt in the buyers mind. Modern presales teams must execute flawlessly and respond to detailed requirements with speed and precision. This demands better data utilization (to anticipate needs and personalize presentations) and often more automation behind the scenes to deliver polished outputs under tight timelines.
  
  

In this environment, traditional presales approaches are straining. Manual effort can’t keep up with the volume of information and level of tailoring buyers expect. Leading organizations recognize that AI and automation are not simply efficiency tools, but essential enablers for presales to thrive in the digital era. 

By offloading low-value work (non-selling activities) and augmenting human expertise with data-driven recommendations, AI allows presales professionals to focus on what matters most: understanding customer problems and crafting the right solutions. 

As we turn to AI’s role, it’s clear that embracing technology is no longer optional,  it’s mission-critical for presales excellence.

Where AI Delivers Quantifiable Value

AI is often described as the new electricity in sales, an enabling force behind a smarter, more connected, and faster presales process. But what specifically can AI do for presales teams? In practice, successful use of AI spans a few key areas:

• Automating Repetitive Tasks: A significant portion of presales work involves repetitive, time-consuming tasks, updating CRM records, pulling product specs, drafting responses to common RFP questions, scheduling demo meetings, etc. These administrative duties, while necessary, can consume valuable hours that sales engineers could better spend on high-touch client interactions. AI-powered automation is changing the equation. Intelligent assistants now handle meeting scheduling, data entry, and even first-pass proposal drafts, and business documentation generation. For example, Opine’s AI-driven presales platform can auto-generate customized POC based on client inputs, freeing the presales team to refine and personalize rather than start from scratch. By automating rote workflows, presales teams reclaim precious time to strategize on deal-winning tactics. The result is not only productivity gain but also reduced burnout on teams who previously spent evenings on paperwork. 

  
  

• Data-Driven Insights and Predictive Analytics: Today’s sales cycles throw off a wealth of data,  from CRM opportunity fields and past deals to buyer intent signals (webinar attendance, whitepaper downloads) and product usage during trials. AI excels at crunching these large data sets to surface patterns and recommendations not obvious to humans. Predictive analytics can score leads and opportunities, helping presales prioritize where to invest effort. For instance, an AI model might flag that a prospect’s usage of certain product features in a trial correlates with a 20% higher win rate, prompting the presales team to allocate extra technical resources to that account. AI can also forecast deal outcomes and revenue with greater accuracy by analyzing analogous historical deals​. These insights help presales managers make data-backed decisions on where to focus their top talent or when to deploy an executive briefing to save a wavering deal. In essence, AI augments the presales playbook with an analytical “sixth sense”  guiding teams to act on the right opportunity at the right time based on hard data rather than gut feel.
  
  

• Personalization at Scale: B2B buyers have grown accustomed to Amazon- or Netflix-like personalization in their professional lives. They expect vendors to know their industry, anticipate their pain points, and tailor every interaction accordingly. Delivering this level of personalization is challenging when presales teams might be supporting dozens or hundreds of opportunities concurrently. Studies show that highly personalized sales motions can increase deal close rates and shorten sales cycles​. By using AI to deliver “segment of one” experiences at scale, presales teams build trust and credibility with buyers, showing that they truly understand and can solve the customer’s unique challenges.
  
  

• Enhanced Collaboration and Knowledge Sharing: In large enterprises, valuable presales knowledge is often scattered, tribal expertise living in senior solution architects’ heads, technical Q&A buried in past email threads, or demo scripts varying by region across different video tools from Zoom to Gong. AI can help unify and democratize this knowledge. Modern presales platforms use AI to index vast amounts of unstructured data and make it searchable in seconds. Imagine a junior sales engineer preparing for a customer call being able to query a system for “Has our company solved problem X before in healthcare?” and instantly get relevant examples and recommended approaches. AI-enabled knowledge bases serve as a virtual coach or copilot, bridging skill gaps within the team. They foster seamless collaboration by ensuring everyone has access to the latest and greatest content and answers. Furthermore, AI can facilitate handoffs between sales, presales, and post-sales by automatically capturing meeting notes and action items. By breaking down information silos, presales teams operate more cohesively and present a unified front to the customer.
  
  
  

Insight: Early adopters of AI in sales are seeing measurable gains. 67% of sales organizations leveraging generative AI report it as “very” or “extremely” beneficial so far​, and organizations that pair AI-driven insights with personalized customer experiences are 1.7× more likely to boost market share than peers. The evidence is clear,  thoughtfully implemented AI strategies translate to presales success.

In summary, AI empowers presales teams to do better and more with less,  automating low-value tasks, surfacing rich customer insights, delivering deep personalization, and enabling team-wide intelligence. Importantly, the most successful use cases treat AI as an augmenting partner to human sellers, not a replacement. The expertise, creativity, and relationship-building skills of presales professionals remain irreplaceable. AI simply amplifies those human strengths by handling scale and complexity at a level impossible to achieve manually. As one Harvard Business Review analysis noted, the winning formula is using AI for what it does best (speed, data analysis, automation) while presales engineers do what they do best, engage customers and solve problems and build trust. With this synergy, presales teams become significantly more predictive, proactive, and personalized, elevating their role from support function to true strategic partner in revenue generation.

How Startups and Enterprises Use AI in Presales Differently

While the case for AI in presales is compelling across the board, the way organizations implement these technologies can differ markedly between agile startups and large global enterprises. Business context, from team size to legacy process, influences the AI adoption playbook. Understanding these differences provides valuable insight into how AI strategies can be tailored for maximum impact.

Startups and Scale-Ups: Younger companies and high-growth scale-ups tend to have smaller presales teams and fewer established processes, but they often boast a culture that’s tech-forward and adaptable. For these firms, AI is a way to punch above their weight class. Startups typically leverage off-the-shelf AI tools and cloud platforms to get quick wins. 

For example, a 200-employee SaaS startup might deploy an AI-driven chatbot on its website to handle initial technical inquiries, effectively acting as a virtual sales engineer for basic questions 24/7. They might also use automated demo environments to allow prospects to explore the product on their own (PLG), since the startup’s limited presales staff cannot personally attend every demo request. The advantage startups have is agility, they can experiment with the latest AI features (from GPT-powered writing assistants to lightweight predictive analytics) without the drag of legacy systems. Many incorporate AI early in their growth to scale customer engagement without scaling headcount. It’s not uncommon for a startup’s single sales engineer to manage a volume of deals that would typically require a team, by relying on AI to handle initial scoping, data gathering, and even generating first-draft technical collateral. The flip side is that startups often have less proprietary data to train AI models and fewer internal resources for customization. They rely on vendor-provided AI (for instance, using a platform like Opine out-of-the-box) and focus on use cases that deliver immediate efficiency due to their limited capacity. 

In short, startups treat AI as a force multiplier to accelerate growth and offset resource constraints, they prioritize tools that are fast to implement, easy to integrate with their CRM, and require minimal IT support. The goal is to quickly drive up presales productivity and customer coverage while keeping the operation lean.

Large Enterprises: Established B2B enterprises, think global software, networking, or cybersecurity companies with hundreds of sales engineers, approach AI from a different angle. These organizations have scale, data, and existing processes to consider. For them, AI is a strategic investment to optimize and unify complex presales operations rather than a quick fix. 

Enterprises often start by piloting AI in a specific part of the presales process (e.g. an AI assistant to support RFP answering, or machine learning to prioritize leads in one region) and rigorously measuring impact before wider rollout. They are more likely to integrate AI capabilities into their current CRM, CPQ (configure-price-quote), or sales enablement systems, ensuring compliance with security and data governance standards. 

A large enterprise presales team might spend significant effort curating and cleaning historical sales data to feed a predictive model that recommends the best expert or resources for each new opportunity, a project that requires cross-functional coordination between IT, sales ops, and analytics teams. Unlike startups, big companies usually have vast troves of customer and deal data that can fuel highly tailored AI insights, but leveraging that advantage requires overcoming data silos and legacy system fragmentation. 

Enterprises also think about change management: a new AI tool must be adopted by potentially hundreds of users, so training programs and internal evangelism are part of the rollout plan. Culturally, veteran sales engineers may be more skeptical of AI, so leadership must position it not as a threat to their autonomy but as a sophisticated support system that can help them sell more effectively. For example, when a global cybersecurity firm implemented an AI-guided selling module (providing real-time content suggestions during sales calls), they paired it with extensive training and positioned it as “the collective knowledge of our best people, at your fingertips” to encourage adoption. Large enterprises also often opt for enterprise-grade AI platforms either building in-house with data science teams or partnering with established providers like Opine that offer robust integration, security, and customization to fit their workflow. The investment is larger and the timeline longer, but the payoff can be ecosystem-wide improvement: consistent presales messaging across regions, faster onboarding of new hires, and identification of best practices that lift the whole organization’s win rate.

Common ground and unique insights: Interestingly, despite differences, both startups and enterprises ultimately seek to leverage AI to boost presales impact and customer experience. Startups teach us the value of speed and experimentation, they often identify novel AI use cases (for instance, using AI to auto-qualify inbound leads with technical criteria) that bigger companies can learn from. Enterprises demonstrate the importance of data and process, they show that to truly scale AI’s benefits, one must invest in data quality, system integration, and user training. A nuanced insight is that large enterprises sometimes envy the “blank slate” of startups, as they can deploy cutting-edge tools without legacy friction; conversely, startups envy the rich data and resources enterprises have. This is why some large enterprises are adopting a more startup-like approach by creating internal “innovation labs” or pilot teams for presales AI, and some growing startups are instituting more formal data management early on to pave the way for advanced AI analytics as they scale. In practice, successful AI-powered presales can emerge in both settings by playing to one’s strengths, agility for the disruptors, and depth for the incumbents.

Regardless of company size, one trend is clear: organizations that treat AI in presales as a strategic initiative (and not merely an IT project) see far greater success. Whether it’s a ten person startup team or a thousand strong global sales engineering group, strong executive sponsorship and alignment of AI projects to business outcomes (like increasing pipeline conversion or improving deal size) makes the difference between experiment and impact. 

Presales leaders at companies of all sizes should ask: How can AI address our biggest bottlenecks or opportunities? The answer will guide whether the focus should be on quick automation wins, advanced predictive modeling, or a balanced approach. By tailoring their AI strategy to their context, both Davids and Goliaths in the B2B world are unlocking new presales value.

ROI: The AI Growing Pains No One Talks About

Amid the excitement about AI, it’s easy to overlook the less glamorous hurdles on the road to success. Implementing AI in presales comes with challenges, some technical, some human, that presales leaders must navigate. A candid look at these under-discussed insights and challenges can help organizations plan more effectively for AI transformation:

• Data Quality and Siloes, The Hidden Bottleneck: AI is only as good as the data feeding it. Many enterprises discover that their sales and presales data is fragmented across CRM systems, spreadsheets, and individuals’ laptops. Important context, like why a past deal was lost or which product configuration was proposed  may not be recorded in structured form. This poses a serious challenge: predictive models and content recommendation engines will underperform or even mislead if the input data is incomplete or biased. In technical presales, product usage data, support ticket history, and engineering effort estimates are valuable inputs for AI, but they often reside in separate systems. Integrating these data sources and ensuring accuracy requires upfront investment. Gartner has noted that cleaning and unifying data is a top barrier holding back AI initiatives in sales (often consuming 60-70% of the effort in AI projects, according to industry benchmarks). Savvy presales leaders are learning to partner closely with IT and data teams to address this. One rarely discussed tactic is the need to establish a “single source of truth” for presales knowledge, for example, consolidating all past proposals and demo scripts into a centralized repository that an AI tool can draw from. It may not be glamorous work, but tackling data foundations is critical. Without it, AI in presales might deliver flashy demos in pilot, only to falter in real-world usage due to missing or messy data. This is where modern solutions like Opine really shine.
  
  

• Talent and Change Management Upskilling the Team: Deploying AI in presales is as much a people project as a technology project. Sales engineers and solution consultants are highly skilled experts, but many have never worked directly with AI tools before. Suddenly asking them to rely on AI-generated recommendations or content can provoke skepticism (“Can I trust this suggestion?”) or even fear (“Will AI make my role less important?”). A common but underappreciated challenge is driving user adoption. If the presales team doesn’t use the new tool, it doesn’t matter how powerful it is. Executive leaders need to champion a culture of experimentation and learning. This might involve upskilling the team through training sessions, sandbox play, and sharing early successes to build confidence. Some companies appoint internal “AI ambassadors”  tech-savvy presales members who help peers learn the ropes and collect feedback to improve the tools. There’s also the question of skills: as AI handles routine tasks, presales engineers may be expected to focus on more advanced analytical or consultative activities. This elevates the skill bar over time. Forward-looking leaders invest in ongoing development, teaching their teams new competencies like interpreting AI insights (e.g. understanding what a lead score really means) or managing AI-augmented customer interactions. Notably, change management is often the make-or-break factor for AI projects. A Forrester study found that companies with dedicated change programs for sales tech projects had adoption rates 3-4 times higher than those without. Yet many presales AI initiatives underestimate this need. Ensuring the human side is addressed by clearly communicating the benefits to the team (e.g. “This tool will save you 5 hours a week on research”) and providing support  is essential to unlock the full value of AI. In short, AI won’t transform presales if the people behind the presales process aren’t brought along on the journey.
  
  

• Trust, Transparency, and the Human Touch: Technical sales is ultimately a people business built on trust. Presales professionals are trusted advisors to customers. Introducing AI into the mix can raise questions about transparency and judgment. For example, if an AI suggests a particular product configuration to a client, how do we ensure the rationale is sound and can be explained? Black-box AI recommendations can be problematic in presales, where clients will probe “why do you recommend this solution?” and the sales engineer must have a convincing answer. Ensuring AI systems have a level of explainability (or at least that users understand the factors considered) is an emerging challenge. Additionally, generative AI tools might occasionally produce inaccurate or overly generic content known as AI “hallucinations.” If not carefully reviewed, this could lead to a sales engineer presenting incorrect information to a customer, potentially damaging credibility. Presales teams must implement checks and balances: AI output should be a starting point, but human validation remains mandatory for client-facing deliverables. Many leading organizations adopt a “human-in-the-loop” approach, where AI drafts are edited by experts, and predictive scores are used alongside not in place of human judgment. Another subtle challenge is maintaining the human touch in relationships. As AI automates emails and answers, there’s a risk of interactions becoming impersonal. Presales leaders should ensure the technology augments empathy and understanding rather than replacing it. For instance, using AI to analyze a customer’s tone or sentiment in communications can help a sales engineer know when to personally intervene with a phone call. Balancing automation with authentic human engagement is an art one that presales teams must master to keep client trust strong in the AI era.
  
  

• Measuring ROI and Avoiding the Hype: Investing in AI for presales needs business justification. However, linking AI initiatives directly to sales outcomes can be tricky. There’s often a time lag and multiple contributing factors to improved win rates or shorter sales cycles. A challenge that gets little spotlight is figuring out the right KPIs to measure AI’s impact in presales. Is success defined by more deals per presales head? Faster proposal turnaround time? Higher win percentage on AI-qualified deals? Many organizations are still learning how to quantify the benefits. It’s important to set clear metrics up front for example, “Reduce average proof-of-concept duration by 20% within 6 months of AI tool deployment” and track progress. Without this discipline, AI projects risk falling into the realm of interesting experiments without clear business value. Another challenge is avoiding shiny object syndrome. With the surge in AI solutions on the market, presales leaders might feel pressure to implement something quickly to keep up with trends. But not every AI tool addresses a real pain point. It’s under-discussed that some companies have invested in AI only to find low adoption or negligible improvement because the solution wasn’t aligned to a true need (a classic case of technology in search of a problem). The lesson is to stay focused on strategic priorities: if your team’s biggest challenge is, say, inconsistent messaging, then an AI content consistency tool will yield ROI whereas an AI lead scorer might not move the needle. In essence, presales organizations must be judicious and evidence-driven in their AI adoption, piloting ideas and scaling those that demonstrably work. The hype around AI is huge, but separating signal from noise is itself a critical leadership challenge.
  
  

By acknowledging these challenges early, presales leaders can mitigate risks and set more realistic expectations. Each challenge has a flip side opportunity; for instance, cleaning up data for AI often yields broader operational insights, and upskilling the team for AI can foster a more analytically savvy sales culture. Companies that openly discuss and address these hurdles are far more likely to succeed with AI than those that assume a plug-and-play implementation. The path to presales transformation with AI is attainable, but it runs through terrain that requires careful navigation. The good news is that the journey, while complex, is navigable with the right strategy and the destination, a presales organization that is faster, smarter, and more attuned to customers, is well worth the effort.

The Bottom Line

The future of presales is being rewritten by artificial intelligence. What we are witnessing is the evolution of presales from a labor-intensive art to a technology-empowered science without losing the human artistry that makes it effective. AI is enabling presales and sales engineering teams to leverage data, scale their expertise, and engage customers in unprecedented ways. But as this report has shown, success lies in a balanced approach: blending AI’s speed and intelligence with the nuanced judgment and relationship-building skills of seasoned sales engineers.

For presales leaders at enterprises like CrowdStrike, Adobe, Elastic, Zoom, DataDog and beyond, the implications are strategic. Those who move decisively to integrate AI into their presales operations will equip their teams to win in a market where insight and responsiveness define competitive advantage. Consider the impact: repetitive tasks minimized, playbook best practices available on-demand, each client interaction informed by the full depth of your organization’s knowledge. When presales can focus on solving problems and shaping solutions rather than scrambling through spreadsheets they transform from product demonstrators into trusted advisors. This is the promise of AI: not to replace the presales profession, but to elevate it.

Of course, realizing this vision requires leadership. It means investing in the right tools and partners, cultivating an adaptable team culture, and steering through the challenges of implementation. The leaders who succeed will treat AI as a strategic priority, with executive sponsorship and cross-functional collaboration (sales, IT, product, data science) to back it. They will pilot boldly, measure rigorously, and scale what works. They will also maintain a customer-centric lens using AI to enhance how they solve customer problems, not just for internal efficiency.

The next 12-24 months are pivotal. Generative AI and advanced analytics are accelerating capabilities at a breakneck pace. What felt futuristic in presales a short while ago, AI-generated technical proposals, virtual demo assistants, real-time deal coaching is quickly becoming not just feasible, but standard. According to industry analysts, by 2025 a majority of B2B sales organizations will have AI embedded in their sales processes in some form, guiding everything from account targeting to solution design. Presales cannot afford to be a late adopter in this shift. In fact, technical presales may well become the proving ground for some of the most impactful AI applications in B2B sales, given its rich data and critical role in deal cycles.

In closing, the organizations that thrive will be those that act now. They will empower their presales teams with AI-driven platforms (for instance, leveraging solutions like Opine’s presales AI suite to accelerate their transformation) and continuously refine their approach as results and learning accrue. They will also keep the ethical and human aspects front and center ensuring that AI usage builds trust with customers and augments the credibility of their experts. The reward for getting this right is substantial: higher win rates, greater efficiency, scalable personalization, and a presales team that is a true force multiplier for the business.

The future of presales is here, and it’s intelligent. Executive leaders have a unique opportunity today to unlock that future turning the buzz of AI into concrete strategies for success. Those who seize the moment will not only see immediate performance gains, but also position their organizations at the forefront of presales innovation for years to come. In the competitive arena of global B2B sales, there is no time to wait. The companies that unlock the power of AI in presales now will be the ones closing the biggest deals tomorrow.

Ready to lead your presales organization into the AI-powered future? The insights and data presented in this report provide a roadmap. The next step is yours to take. Embrace the change, equip your team, and watch the future of presales unfold one intelligent win at a time.