The Revenue Imperative: Why GTM Strategy Must Evolve in the AI Era

Sentiment analysis analyzes a customer’s tone, word choice, and the context of their messages to gauge how they feel—whether frustrated or satisfied. By monitoring emotional cues, AI solutions can assist you in assessing how customers are reacting in real time. For instance, if your customer seems angry or upset, the system can automatically flag the conversation for more immediate attention or suggest strategies to de-escalate the situation. This real-time insight allows your agents to tailor their responses and elevate the immediate experience and long-term relationships with customers.

AI saves less than 3% of time, but with no noticeable work changes

The solution, Eagleman suggests, lies in designing “AI systems to check on other AI systems” and creating “translators to dumb things down for us so that we can understand what is going on.” While AI offers immense innovation potential,it has also raised ethical concerns around data privacy, algorithmic bias, and fairness. Financial institutions must prioritise ethical AI practices, including transparent algorithmic decision-making, data governance, and bias mitigation strategies, to build trust with customers and stakeholders and ensure responsible AI deployment. In the realm of investment banking, AI algorithms are revolutionising trading strategies and portfolio management. Through algorithmic trading platforms and robo-advisors, financial institutions can execute trades at lightning speed, optimise asset allocation, and tailor investment portfolios to individual risk profiles, delivering superior returns and driving investor confidence.

• This psychological framework helps us understand how AI’s influence extends far beyond simple task automation—it’s actively reshaping the cognitive and emotional landscape of human consciousness.
• We also have to be continuously on the lookout for how AI could exacerbate traditional financial stability channels such as interconnectedness, liquidity, and leverage.
• Organizations often use legacy systems and modern software together, which may not be compatible with new AI technologies.
• Academic research demonstrates how this back-and-forth creative dialogue amplifies human capabilities.

“Integrating a major acquisition like Splunk means aligning not just platforms, but philosophies,” says Unnikrishnan. “Achieving synergy at scale requires a unified GTM framework powered by automation and intelligent data pipelines.” The gap between technological capability and practical deployment has always challenged human imagination, but the patterns emerging from our AI Impact conversations reveal the true promise of AI lies not in replacing human judgment but in extending it. Where automation dreams crash against real-world complexity, augmentation thrives by preserving what humans excel at while amplifying capabilities through machine partnership. Academic research demonstrates how this back-and-forth creative dialogue amplifies human capabilities.

Advanced Predictive Analytics

Additionally, by making sure that customers are matched with the right agent faster, intelligent routing decreases the waiting period and leads to quicker resolutions, resulting in higher customer satisfaction. “If your business is an early adopter in tools and technology and your people are comfortable using them, change management will be easier. MIT’s Ravin Jesuthasan contends that introducing AI alone is insufficient; real gains require companies to redesign workflows, automate routine functions, and reorganize teams. Without this fundamental change, businesses risk maintaining outdated processes, limiting the benefits of AI adoption. When it comes to integrating acquisitions into this new GTM paradigm, an additional layer of complexity arises. The $28 billion Splunk acquisition by Cisco stands as a testament to the urgency to align differences in revenue models, tech stacks, and customer engagement styles.

In May, Siemiatkowski said that cutting labor costs had “been a too predominant evaluation factor” because “what you end up having is lower quality.” He added that “investing in the quality of the human support is the way of the future for us.” The irony is that even while touting an automated future, the limits of AI often mean thathumans are very much in the loop. When Elon Musk showed off his humanoid robot Optimus at a press event in 2024, the robots were remote controlled by humans. Before Cruise suspended operations, its “driverless” vehicles required remote human assistance every four to five miles.

The report also included a forecast for the U.S. service-drive AI market, projecting the fixed ops segment to reach $500 million to $1 billion in annual spend within the decade, based on adoption scenarios and dealership spend models. From our observation and outreach, we see finance as an industry that is particularly ready to take advantage of these advances, as the efficient processing of data is already central to most activities in finance. Therefore, from back-office operations to customer-facing interfaces, and from research to building analytical models, we expect this to take off rapidly. AI, as it should be broadly understood, has already been impacting financial markets for many years.

Scalable 24/7 Support

This kind of technology can decrease the time spent searching for solutions and increase productivity by 150 percent. AI supports operational efficiency and plays a key role in fostering a deeper connection between businesses and customers. With this technology, you can customize interactions in a way that feels intuitive to each individual. Customers no longer feel like they’re just another number in the queue; their needs are met with timely, context-aware solutions that show attention to detail. Speech recognition and natural language processing (NLP) work together in call centers to optimize operations. Speech recognition transcribes customer calls into text in real time, eliminating the need for agents to take notes.

In general, we need to think about issues like margining requirements, circuit breakers, and the resilience of central counterparties in light of a potentially rapidly changing world. However, we have also seen some limited negative impact of quantitative trading in some sudden market dislocations, and there are fears that these risks could rise with the use of AI. While HubSpot Service Hub is excellent call center software, its AI capabilities are not as advanced as those of its competitors. However, HubSpot is known for constantly improving its offerings and ensuring that its customers get the latest advancements. According to a recent Gallagher Bassett whitepaper, nearly 90% of insurers in Australia now use GenAI in claims operations, up 38 percentage points from the previous year. The adoption of GenAI, which began accelerating in late 2022, has been faster than previous technology waves such as smartphones.

For instance, predictive insights could prepare staff for high-demand periods or suggest proactive solutions for recurring concerns. Where RevOps was once seen as a support function focused on pipeline management and sales efficiency, today it’s evolving into a strategic pillar for growth. AI is enabling hyper-personalized customer experiences, allowing companies to tailor each interaction based on predictive data. According to McKinsey, firms using AI-enhanced GTM strategies are achieving 10–20% improvements in sales productivity by leveraging real-time behavioral insights and targeted engagement. Klarna CEO Sebastian Siemiatkowski learned this after receiving widespread attention for declaring “AI can already do all of the jobs that we, as humans, do” while replacing 700 customer service contractors with AI systems in February of last year. But soon after, he discovered that Klarna customers were being handed off in one-third of cases to human agents when the AI couldn’t resolve complex issues.

Bottom Line: Embrace AI in Call Centers to Elevate Service Quality

Today, I will discuss some of these recent and potentially far-reaching developments, as well as their potential impact on financial stability. Emotion AI, or affective computing, is transforming how call centers handle customer interactions. This technology enables systems to assess and respond to emotions in real time by analyzing subtle cues, like voice tone and speech patterns. Future advancements are set to refine its ability to interpret complicated emotional states, allowing AI to assist agents by suggesting empathetic responses or escalating sensitive matters to specialized staff. With the emotion AI market expected to grow to $13.8 billion by 2032, its influence in enriching customer interactions is becoming more clear.