Many tech organizations have operated the same way for years. Now they are challenged to transform with AI. Where should they start? Customer experience and employee productivity are often effective places to establish AI’s cost effectiveness across the organization. Below are some areas primed for AI-driven transformation. 

Sales journey and revenue operations orchestration 

Tech organizations can harness AI to orchestrate the end-to-end buying journey, from education, to contracts, to proposal preparation and real-time seller support. One example in the workstream can be to deploy agentic AI “avatars” to coach sales representatives before, during and after calls to improve seller effectiveness and customer interactions. These powerful enterprise AI tools can integrate directly with an existing tech stack, derive important insights from customer datasets, and free up B2B and B2C sales reps to spend more time getting to know their customers, rather than on tedious administrative tasks. 

Additionally, today’s leading B2B sales organizations are getting ahead by prioritizing a robust revenue operations function (RevOps). AI tools can streamline the end-to-end RevOps process—especially by easing the complexity of larger B2B technology deals that historically require hours to coordinate. From simplifying the quoting process with data-driven lens, to mitigating leakage, to contract tailoring, to real-time deal support and more, GenAI and agentic AI can amplify and accelerate effective RevOps.² 

Personalization for complex B2B journeys 

For B2B efforts in the tech industry, the enterprise marketing and selling motion needs to reach and convert several different types of decision-makers across C-suite and director-level buyer groups. This makes personalization efforts more complex. B2B technology deals often have an extended sales cycle, so precision in personalized communications is critical to moving deals forward.³  

C-level leaders especially wield budget responsibilities within their tech organizations, and mistargeted or inaccurate personalization efforts can make or break a final deal. For example, a personalized email written for a CMO audience could have a negative impact on perception if incorrectly delivered to a CFO or CTO instead. 

Accelerated service case resolution 

Technology organizations that leverage AI can realize near-term value for customer service accuracy, efficiency and relevance. The right tools can help customer support teams surface the right answer to a customer's problem faster, thanks to AI’s ability to synthesize large research sets, summarize key points from customer calls, and power internal "chat with your data" capabilities for Voice of the Customer (VoC) analysis. In our recent work with Salesforce,⁴ for example, we helped their service employees apply powerful Agentforce technology to drive faster service case resolution with agentic AI. 

Agentic AI for product development 

AI tools can breathe new energy into the product development process, enabling faster prototyping, coding, and realistic demos that can prove value early. The most successful product teams drive strong collaboration between employees and the AI tools they use. When implemented correctly, these AI-driven product acceleration initiatives can synthesize experimentation data to help teams make better decisions during iteration and prototyping stages,⁵ more rapidly narrowing down which products warrant further development and investment. 

Additionally, product teams are seeing a positive impact from AI investments that enable access to company data more broadly such as a model context protocol (MCP). These serve as an internal “company brain” that track each new system upgrade or information update and connect them directly to AI agent knowledge so that enterprise-wide agentic processes can remain relevant and effective. This can help further break down silos and democratize access to knowledge across teams. 

Synthetic market research 

Some organizations have successfully tested campaigns with a synthetic audience—an AI-powered tool that can mirror the demographics and preferences of a target audience. This can help teams test and refine messaging before launching, saving time and money while enabling creative teams to quickly adjust campaigns to better meet customers where, when and how they prefer.

The most significant barriers are usually not technical. Rather, they frequently stem from a lack of strategic alignment and cross-team collaboration. The human element is critical—a lack of widespread adoption and education about AI tools across the organization can risk slowing down value capture. Many teams within tech organizations are stuck in AI “pilot purgatory,” and need to demonstrate AI’s proof-of-value to their enterprise leaders in practical ways—fast. 

Scaling challenges in established organizations 

Large, established technology organizations sit in a precarious position when it comes to GenAI governance, adoption and scaling. Many are balancing legacy ways of working, risk sensitivity from both employees and customers, and a lack of time and support given for internal teams to adopt the tools.  

Outdated rulesets—such as uneven AI permission settings across an organization, where certain teams have access to more AI tools than others—may unintentionally sabotage efficiency gains. Unwieldy manual approval cycles, for example, can negate the potential positive impact of AI in a workflow. Large technology organizations may feel (and present to the market) that they are "early adopters," but that adoption is often happening unevenly. 

This lack of agility also puts large, well-established organizations in danger of falling behind in the market—especially compared to smaller startup organizations with more nimble AI adoption strategies and a culture designed for speedy innovation. 

Lack of strategic vision 

Some tech organizations treat agentic AI and GenAI⁶ as a disjointed collection of isolated pilots and experiments, rather than a clearly defined roadmap based on strategic priorities. We see this approach when organizations fail to secure broad leadership buy-in or decide to layer AI on top of existing processes rather than fundamentally rethinking them, which limits scalability. Moving too fast without a strong, strategic vision for what AI adoption will look like across the enterprise could result in different groups trying to create the same AI functionality. Teams could also end up with many disparate point-to-point solutions across vendors, instead of adopting one cohesive—and cost effective—process. 

For example, simply weaving an AI tool into a product development step does not have a positive downstream effect if the teams delivering project briefs beforehand—and the teams building the prototype afterward—are not also well-versed in that same AI tool’s output and capabilities. These cases can potentially cause misalignment, delays and frustration that impact delivery times. One segment of the product team using GenAI and agentic AI tools is not effective if the rest of the downstream development process does not also adopt the same tools.  

Over-indexing on build vs. buy 

Engineering-centric cultures in tech organizations can tempt teams to build custom AI solutions from scratch for purely experimentation-driven purposes with no larger strategy in mind. This can delay scale and unintentionally pull talent and resources away from core product innovation. For example, an engineer might create an AI tool to sort through articles about industry news related to their product’s competition in the market. Though such a tool could help with ideation and forecasting, if another team already handles this monitoring, then the engineer’s AI tool could unintentionally detract finite resources from more urgent work.   

While teams are eager to build new AI agents and tools, the full agentic development lifecycle is still evolving. It is important for tech organizations to rethink and redesign processes on a fundamental level with AI, rather than simply build more AI for the sake of AI. Rather than prioritizing speed over quality, it is important for tech organizations to first develop a robust and thoughtful AI governance and development lifecycle strategy. 

Cost inefficiencies and tokenomics 

It is important for technology organizations to zoom out from their teams’ day-to-day AI use and analyze their token budgets. Some piloted features leveraging enterprise AI platforms are not built for economic sustainability at scale. Not all tasks require a powerful large-language model (LLM) to execute, and unprofitable AI use can snowball if a relatively minor feature requires multiple, costly tokens.  

Applying tokenomics carefully throughout product development cycles is especially crucial. AI is a powerful tool, but when deployed without proper upskilling available for all teams early on, it can drive down profit margins and create a new kind of tech debt. A value-centered approach with real, tangible goals at the outset is important to establish across the enterprise.

Start at the top 

AI transformation in the technology industry is, at its core, an internal change management effort. Success depends on:  

• Trust 
• Visible leadership 
• A culture that supports experimentation  
• The right incentives for collaboration and adoption 
• The right governance to help ensure new tools and features are economically sustainable, high value, accurate and trusted by users 

When senior technology leaders use AI tools in front of employees, they demonstrate that adoption matters. Providing employees with dedicated time and space to learn—along with hands-on onboarding from trusted peers or engineers who have deep experience with AI tools—is critical for overcoming resistance and accelerating adoption across the organization. 

C-suite and cross-organization alignment also impacts AI deployment across workflows. Enabling some teams with AI tools but not others limits gains and hinders adoption. Speed in one step could create bottlenecks in the next. A creative team, for example, may use GenAI to produce campaign assets faster, but if the web team cannot operate at the same level of AI-enabled efficiency, work stalls and campaigns don’t reach customers faster.  

Moving from automation to autonomy 

GenAI and agentic AI can deliver significant value for teams in the technology industry when it acts as a collaborator to augment human capabilities. Subject matter experts should stay involved at critical control points across the engineering and product lifecycle and through marketing, sales and service functions to set evaluation criteria and validate outcomes. By effectively connecting multiple AI agents that can talk to each other across the enterprise, business leaders can work toward automating tedious processes that previously held human employees back from more valuable, hands-on work. Let AI handle the scale, and let humans provide the trust, nuance and strategic judgments that drive growth.