Streamlined data management and collection is another prerequisite for AI

Last month, Kyndryl released a report highlighting a significant rise in pressure for business leaders to prove positive return on investment in relation to AI, as AI expenditure was found to be up 33pc compared to last year.

The second edition of the Kyndryl Readiness Report – which gathered responses from 3,700 senior leaders across 21 countries – found that 61pc of business leaders are feeling more pressure to prove their AI investments compared to a year ago, with more than half (54pc) reporting positive returns.

While the report found that overall AI readiness has increased since last year’s survey – with 36pc reporting complete readiness – 62pc said that their AI projects haven’t advanced beyond the pilot stage.

Momentum.io: The AI Driving Sales Execution

One of the AI-driven tools making this possible is Momentum.io. Both Kyle and Nate are power users.

Instead of relying on manual tracking, Momentum automates deal intelligence, risk alerts, and CRM data entry—allowing Ramp’s revenue teams to focus on execution instead of admin work and becoming the data pipeline for the go-to-market and revenue teams and leadership.

Let’s break down exactly how it works in action.

1. AI-Driven Deal Risk Alerts: Saving Deals Before They’re Lost

Sales leaders always want to catch at-risk deals before they slip away. However, manually tracking buyer engagement signals across sales calls, emails, and meetings in a fast-paced sales cycle isn’t scalable.

Ramp solved this problem with Momentum’s AI-powered risk alerts.

“We really want to catch those deals before we lose them. So now, if a buyer is disengaged—camera off, vague objections, noncommittal language—Momentum automatically pings the manager in Slack to step in before it’s too late.”

We at Empower AI want agencies to solve problems sooner, not later. So we've built accelerators that reduce time to value, minimize technical debt, and accelerate mission outcomes. Empower AI has designed accelerators tailored to specific agency needs, including:

Predictive IT. This leverages historical and real-time data, machine learning and predictive analytics to proactively forecast and resolve issues before they happen

Agentic AI. Going beyond rules-based automation, this uses a large language model (LLM) to automate complex tasks, adapt to dynamic environments, and optimize outcomes using real-time data and predefined objectives.

GovQuery. Using a Retrieval-Augmented Generation (RAG) LLM, this on-demand data search capability results in precise, contextual answers with citations within government-controlled systems

AI Readiness. With multiple tool sets that prepare an agency’s infrastructure, data, and workflows for AI models, along with training, Empower AI ensures agencies are prepared to benefit from AI

Unlike standalone AI tools, Empower AI’s accelerator approach integrates seamlessly into an agency’s current technology environment, ensuring maximum adoption and usability.

Streamlined data management and collection is another prerequisite for AI in IT operations, according to Ari Silverman, director of platform automation and enterprise architecture at OCC, an equity derivatives clearing organization in Chicago. Silverman's team uses LogicMonitor as an AIOps monitoring tool, primarily for predictive analytics and automated capacity planning and management.

"You have to be able to connect to all your different systems and pull data from all those systems into one central location for it be analyzed," he said. This pooled and synthesized information enables a team to better identify correlations between application performance and the IT infrastructure that drives it.

By - Aaradhay Sharma