The integration of AI into sales and marketing is transforming how software companies operate, boosting seller productivity, redefining go-to-market (GTM) models and driving cross-functional alignment to successfully commercialize new AI offerings.
Key Highlights
- AI enhances seller productivity by automating routine tasks, enabling data-driven decision-making and freeing up time for high-value customer interactions.
- Integrated commercial functions powered by Revenue Operations (RevOps) are now essential to deliver personalized, data-driven experiences and ensure successful AI adoption across sales, marketing and customer success.
- GTM models are evolving to support AI product sales with new roles, refined ideal customer profiles (ICP) and incentive structures aligned to longer, consultative sales cycles.
The integration of AI into commercial functions is reshaping how organizations operate, particularly within the domains of sales and marketing. Sellers, GTM strategists and marketers are encountering a profound transformation in their roles, tools and operating models. As AI technologies mature, organizations are using AI internally to improve seller productivity. Externally, the addition of new AI features is causing organizations to reevaluate how they go to market.
Teneo's POV
- AI requires a shift in coverage model: New roles and overlays will be required to bridge the gap between technical buyers and users to facilitate clear value messaging and help shorten sales cycles.
- Customer adoption is key to sustainable success: Sales team adoption of AI-tools will reduce time spent on low value sales administration and sales completion tasks – businesses that actively reallocate seller time to focus on adoption will drive greater customer value realization, retention and expansion opportunities.
- Sales motions will become more personalized and more complex: AI will enable highly customized sales motions built around customer characteristics, use case and ICP, but will require tactful management to ensure customer trust and integrated motion across sales, marketing and customer success.
A Guide to Commercializing AI Through Productivity Gains and Evolved GTM Design
AI is reshaping the way organizations sell, both in how individual sellers work and how entire GTM models are structured. In this section, we explore two shifts. First, we look at how AI-powered tools and RevOps insights are driving seller productivity. Off-the-shelf solutions are automating administrative tasks and surfacing real-time customer insights, allowing sellers to spend more time building relationships. Next, we examine how organizations are evolving their GTM strategies to support longer, more consultative AI sales cycles. From redefining roles and refining ICPs to realigning incentives across sales, marketing and customer success, companies are building more agile, AI-ready commercial models.
1: How AI is Being Used by Sellers to Increase Productivity
AI has been a transformative tool for software sellers. It has enhanced productivity by freeing up time for high-value, client-facing activities. For software sellers, the goal is to spend over 40% of their time directly engaging with customers on live opportunities. This has historically been difficult to achieve due to administrative overhead and disjointed systems. Today, emerging AI technologies are making that target far more attainable.
The introduction of AI-enabled solutions and services has fundamentally shifted where GTM teams are spending their time. Leading firms are reallocating time to focus much more directly on engagement and adoption, leveraging the time savings AI solutions provide on previously manual sales admin tasks.
Market-leading revenue operations teams now leverage AI-powered insights to develop predictive lead scoring models, optimize account assignments and streamline sales processes. Meanwhile, sellers benefit from accessible, off-the-shelf tools that automate routine tasks and improve decision-making. Although AI has numerous use cases, the top areas where sellers see immediate productivity uplifts are:
- Predictive lead scoring: Machine learning models trained on past conversion patterns help prioritize the most promising leads and flag at-risk accounts. This enables sellers to focus efforts where they are most likely to succeed or intervene before churn occurs.
- Content creation: Generative AI helps sellers accelerate outreach by drafting personalized sales emails, call scripts and follow-up content. It also summarizes calls and tailors content recommendations using CRM data and real-time buyer intent signals.
- Account planning: AI-driven forecasting and opportunity scoring tools allow for smarter account planning. Rather than relying on intuition, sellers gain access to data-backed guidance that enhances planning and performance consistency.
- Real-time feedback and coaching: Tools like Gong and Salesloft provide live coaching and interaction analysis, empowering sellers to adapt their approach mid-conversation and improve outcomes through targeted performance insights.
Collectively, these capabilities not only reduce administrative burdens but also enable sellers to be more strategic and customer focused. The result is a measurable uplift in productivity, stronger pipeline execution and higher quota attainment.
2: How Organizations Are Adapting GTM Models in Light of New AI Offerings
The buyer journey has become increasingly non-linear, with multiple digital and human touchpoints influencing decisions in parallel. AI empowers commercial teams to respond by delivering personalized, predictive and self directed experiences. This includes tailored messaging, smart content recommendations and proactive engagement, allowing sellers to anticipate needs and to engage at exactly the right moment.
Increased customer touchpoints and greater focus on customer outcomes to meet specific use case requirements are leading to prolonged sales cycles for AI enabled solutions. Internal AI-driven efficiencies, however, allow for greater deal sizes and customer coverage, leading to improved seller productivity and countering the potential slowdown in sales from longer cycles.
The commercialization of AI products presents both opportunities and challenges for sales and marketing teams. Unlike traditional software, AI solutions often involve deep technical knowledge, education and trust building, business alignment, continuous optimization and integration into complex workflows. These characteristics require a new sales motion with different messaging, sales enablement and adoption support approaches. Sales, marketing and customer success play pivotal roles in commercializing AI products.
As a result of the introduction of new AI products and a new sales motion, GTM models are undergoing fundamental changes. The traditional silos between sales, marketing and customer success are giving way to integrated, dynamic systems that prioritize speed, personalization and data-driven execution. In response to this shift, organizations are making several structural updates to how they design and deploy their commercial teams:
- Refocusing the ICP: Organizations are redefining their ICPs with far greater precision by analyzing behavioral patterns, technographics, buying intent, AI maturity and technological readiness. Instead of relying on static demographic and firmographic filters, teams now build dynamic, evolving customer profiles that align with real-time market signals.
- Demystifying technology through messaging: Marketing teams are increasingly focused on educating buyers about what new AI products are and how they deliver value. This involves not just promoting product features but also demystifying the technology. Content strategies are shifting toward use-case-based storytelling, thought leadership and third-party validation. Educational assets such as white papers, webinars and industry-specific AI maturity models are
used to build buyer confidence. - Evolving the coverage model with new commercial roles: The complexity of AI solutions is prompting the rise of new roles, such as AI Sales Strategists and AI Product Evangelists. These professionals bridge technical depth and customer-centric storytelling, translating AI capabilities into compelling value propositions. They help shorten sales cycles, increase credibility with technical buyers and drive demand across industries.
- Aligning sales incentives with AI sales motions: Sales compensation plans are adapting to reflect the longer, more collaborative nature of AI sales cycles. Organizations are introducing incentives that reward consultative engagement, pilot-to-production success and post-sale expansion. This ensures that compensation aligns with the value realization journey rather than just contract signing.
- Proactively monitoring adoption with Customer Service (CS): CS teams are offering AI deployment playbooks, proactively monitoring usage and performance and translating complex outputs into business outcomes. High-performing teams now include AI enablement specialists who guide change management, reinforce ROI through metrics like model accuracy and efficiency gains and drive continuous improvement through tight feedback loops with product and engineering.
- Integrating functions through revenue operations RevOps: RevOps has emerged as the connective tissue across sales, marketing and customer success. RevOps teams are tasked with operationalizing AI insights, managing clean and centralized data systems and aligning performance metrics across the funnel. Their role is critical in coordinating cross-functional efforts to improve buyer experiences and accelerate pipeline velocity.
Common Pitfalls
- Adoption of AI-focused sales motions without adjustment to incentive plans: Companies fail to review and update sales compensation plans proactively when adopting AI-focused sales motions, resulting in misalignment between GTM team focus and corporate strategy. Compensation plans must incentivize cross-functional collaboration, adoption and long-term expansion motions.
- Maintaining silos across GTM functions: Companies maintain their existing GTM approach without an active plan to facilitate cross-team collaboration, resulting in inconsistent messaging and handover points throughout the sales process.
- Insufficient focus on integration and adoption: Companies that lack dedicated resources and focus often fail to drive full customer adoption of AI-enabled solutions. This results in end-users lacking knowledge and access to full value of solution capabilities.
Conclusion
The integration of AI into commercial functions is not just a technological shift, it is a strategic imperative. Organizations that embrace AI-driven productivity tools, restructure their GTM models and realign incentives are better equipped to navigate the complexity of AI product sales and meet evolving customer expectations. As the pace of AI innovation accelerates, those that adapt their commercial strategies now will be best positioned to lead in the markets of tomorrow.