B2B software buyers are no longer dazzled by AI. In a market flooded with lookalike capabilities and unfulfilled promises, they are increasingly immune to conventional marketing and demand quick proof of value. Only vendors who reimagine their go-to-market (GTM) strategies can overcome this skepticism and win market share.
AI is reinforcing existing trends, such as the importance of customer success, while simultaneously requiring vendors to deliver more complete proof of concepts, validated customer references, and faster time to value. Companies can leverage a new landscape of sales roles to support this journey.
The Challenge: Increasingly Similar Offers, Increasingly Discerning Buyers
Customers are willing to invest in the right AI solutions that reliably deliver value to their employees and end customers. In BCG’s 2025 IT Buyer survey, 45% of IT buyers said they are planning to increase their AI spend over the next 12 months, and 46% of buyers are planning to expand their roster of suppliers for AI solutions. Buyers are also looking for AI tools more often: software refresh cycles that were typically three to seven years are being replaced with a continuous evaluation process.
However, buyers are facing a sea of sameness when it comes to AI, with multiple major software vendors offering solutions for enterprise search and analytics, coding, and customer support. Standing out is now less about what capabilities a solution offers and more about how reliably and accurately it performs. As a response, buyers are self-educating, proactively seeking out vendors for priority use cases, and focusing on proven impact. For specific use cases, incumbent stickiness and prior advantage are eroding, with buyers looking beyond their existing suppliers for best-in-class solutions, further raising the importance of a differentiated offering.
Buyers want to be sure of the value before expanding their AI solutions. After a first round of largely failed pilots and false starts, buyers are less likely to trust vendors’ self-reported metrics and want to try solutions now instead of waiting for a long implementation timeline. To support these changes, customers are centralizing purchasing, setting top-down AI mandates, and creating AI centers of excellence, sometimes with veto power.
Finally, customers are increasingly unwilling to experiment with immature products, demanding enterprise-grade offers and support. They often have checklists around security, compliance, governance, liability, and integration capabilities. Additionally, there is a new emphasis on information security, business cases, ROI, and time to value. In this environment, it is unsurprising that 57% of B2B buyers expect positive ROI within three months of purchasing software, according to a survey by G2.
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To adapt to new customer needs, both incumbent and AI-native software vendors offering AI solutions must make GTM changes, including:
- Upskilling their field teams with more technical knowledge
- Developing a more value-focused and consultative selling motion
- Expanding their focus to new buying centers, such as the AI centers of excellence
- Leveraging system integrators to fit their AI solution into a broader AI transformation roadmap
- Establishing new top-down motions based on executive-to-executive conversations
Additionally, vendors must rethink which stakeholders to engage with and what to highlight in their messaging. Some vendors are leading with their AI solutions and telling customers to de-prioritize other product upgrades; others are telling customers that to unlock the true value of a platform, companies need to invest in both AI and the core. Regardless of approach, they are all focused on demonstrating the effectiveness of their solution through case studies, customer references, and demos.
One example of the value focus is the increased importance of the proof-of-concept (POC) stage of the sales process. We see four POC models emerging, ranging from traditional demos to embedding the solution in customer sandbox environments with real customer data. The type of POC to offer varies based on product, contract size, customer size, and use case. (See exhibit.)
In this framework, software vendors are increasingly offering higher-touch (and higher-effort) POCs for their AI solutions. AI-native customer support vendors are prioritizing POCs with actual customer data to generate real results, especially for larger enterprise accounts. Established software vendors support both customized demos and embedded POCs but are increasingly investing in rigorous proof of concepts/pilots for their largest customers or prospects.
Regardless of which approach vendors use, they must shift from generic to tailored demos specific to the industry and use case. Customers expect customized POCs, whether that’s explaining potential business value using their own data, a demo that describes a real-world end-customer situation, or a deployment within their environment with their data. These POCs should deliver real outcomes on impactful metrics such as deflection rate or customer satisfaction and must meet increasingly strict security standards that address compliance requirements and reduce liability concerns.
Given the hesitancy to spend on AI without a clear ROI, this stage in the sales process is becoming a critical make-or-break moment, with only one to two vendors typically invited to the POC stage. Both incumbent and AI-native vendors are creating and expanding GTM roles in both pre- and post-sales to increase the odds of being included in these highly selective shortlists.
Selling AI: New Go-to-Market Roles
In the new sales environment, every go-to-market role is evolving in both pre-sales and post-sales. Existing roles are becoming more technical, focused on adoption, and mandated to prioritize AI; new roles are emerging to support AI sales and value realization. The required roles depend on the layer within the stack, the solution’s complexity, the technical difficulty of implementing the product, and whether the product is a platform or a point solution. For example, a full platform solution will require higher pre-sales investment since the effort to set up a POC and connect to customer data from multiple sources is greater than a point solution.
Some new roles we see emerging across both incumbent and AI-native players include:
- AI Sales and Technical Specialists. These are overlay roles to support account executives as they sell AI solutions. These specialists not only have significant technical depth but are also equipped to highlight the value, differentiation, and industry-specific benefits of the AI solutions. Although it is typical to deploy specialists when deploying a new technology, these roles differ because of their deeper domain expertise. They have the ability to support use-case discovery, the skills to develop relationships across a series of buying centers and personas, and responsibility for scoping pilots and POCs. More broadly, they know how to connect the specific solutions to the broader customer AI transformation journey.
- Senior AI Technologists. These very senior roles—often called field chief technical officers or AI evangelists—are primarily focused on pre-sales. They leverage their experience deploying AI to convince buyers, including AI centers of excellence, of value potential, explain pitfalls to avoid, and add industry-specific credibility.
- AI Outcome Managers or Agent Strategists. Pioneered by AI-native companies, this is the next iteration of the customer success manager role. They ensure customers realize value with hands-on support, webinars, and technical engagement. Their success is judged on the customers’ success.
- Go-to-Market Engineers. Also pioneered by AI-native companies, this is commonly a role within revenue operations. It focuses on automating seller work (for example, manual account research, CRM updates), automating and personalizing demand generation and outreach, surfacing buying signals from unstructured data, and automating the creation and suggestion of revenue and expansion plays. These roles are turning revenue operations teams into growth architects, reducing the burden on frontline teams of manually sifting through data across systems.
- Forward Deployed Engineers (FDEs). This is the most common new role in the AI GTM process. They are highly technical and focus on getting customers up and running with a few targeted use cases to drive quick time to value and encourage expansion of the AI solution. The role was initially designed to support both pilots and broader implementations and is now evolving into three archetypes, as shown in the slideshow below.
Even though all three forward deployed engineering models aim to accelerate AI adoption, some are more closely aligned with professional services offers, while others lean toward product development and enhancement. Depending on the model vendors choose, FDEs can be paid and treated as cost of goods sold or they can be organizationally aligned with the product organization to support rapid development.
Existing roles are evolving consistently with broader trends in SaaS, such as an increased focus on customer success, greater technical abilities, and greater emphasis on value selling. AI is accelerating these trends across core GTM roles regardless of where in the stack the software vendor plays. Specifically:
- Account executives are increasingly focusing on new buying centers, leading with value, and navigating compliance, security, and governance conversations. Many software vendors are now setting account executives’ targets not only on bookings but also on adoption or consumption, creating a clear incentive for them to spend time on post-sales. To drive focus on AI, account executives are being upskilled to have the technical expertise to explain the AI solution and are often given specific incentives for AI deals.
- Solutions engineers are morphing into solution architects. They not only run custom demos but also explain how this solution will fit into the broader customer landscape, how the architecture should be designed, and how to think about integrations.
- Customer success managers become custodians of value. They are increasingly involved in pre-sales to ensure tight alignment between what the customer is asking for and the solution being delivered. In post-sales, they serve as the primary point of contact for additional commercial offers from software vendors, including hands-on-keyboard, technical, and architectural support.
With all these changes, software vendors are fundamentally asking for more from their field teams. Thankfully, GTM teams can leverage AI within their internal processes to drive productivity, deal velocity, win rates, higher-quality forecasting, and more.
Players with existing GTM engines will require multiple quarters of effort to drive this transformation. The inherent tension is that while AI can be deployed in days and weeks, the impact comes only from shifts in human behavior, process reimagination, and adoption, which take months or even years to execute. Embarking upon this journey requires a coordinated narrative from senior management on the case for change, re-architecting roles, upskilling teams, and driving adoption. Employees will face a profound shift and need support throughout the effort while simultaneously ensuring evolving customer needs are met.
The opportunity to reset GTM teams for the AI era is very real, and vendors need to start now to generate measurable, lasting impact.
