AI scalability is a tightrope. Can your firm keep its balance?

If AI scalability is a tightrope, most pilot projects never make it across the execution gap. According to IDC, for every 33 AI proof-of-concepts a company launches, just four successfully walk the high wire to widescale deployment. ⁱ That’s despite skyrocketing AI spending that’s on course to double to $632 billion annually by 2028.ⁱⁱ

Scalable AI demands that capital markets firms strike a careful balance between the competing needs of the front, middle, and back office. Pull too far in any direction and the whole fabric frays. Yet, this tension between competing roles isn’t a flaw, it’s what makes AI performance possible at scale.

Harness tension, eliminate friction

No one can whistle a symphony; it takes a whole orchestra to play it.

— Halford E. Luccock, Yale University

The barrier to AI scalability isn’t chiefly technical. Better algorithms, more computing power, and larger datasets are just table stakes. The real bottleneck is successfully orchestrating people, process, and data to eliminate friction in a world where signal is scarce, noise is abundant, and time is short.

The competing demands of traders, quants, and engineers all pull in different directions as you try to operationalize AI at scale. Where traders want instant answers, quants prioritize the freedom to experiment, and engineers value reproducibility and control. These roles don’t just have different priorities, they use different tools and even speak different languages.

A quant thinking in terms of probabilities and statistics might not have much sympathy for a trader complaining that their 95% effective model failed for a specific order. Meanwhile, the trader may have some thoughts about the quant, their ivory tower, and their lack of interest in real-world situations.

If you’re scaling up AI, you’re likely feeling this frictionTension between different roles is natural and necessary in any organization, but too great an imbalance creates failure states when it comes to scalable AI. Consider what happens when you overfocus on:

• Production speed: You react instantly, but on yesterday’s signals, not tomorrow’s edge
• Quant innovation: Brilliant lab models become brittle in production, eroding trust
• Engineering control: Rock-solid but rigid systems can’t adapt when markets shift

Imbalances always create the same result: AI systems that can’t cope with the real-world demands of capital markets. Small wonder McKinsey has found that 80% of all AI initiatives fail to achieve their intended bottom line impact.ⁱⁱⁱ

Additionally, tension between roles is often made worse by fragmented workflows and siloed systems that add complexity, impede collaboration, and erode alpha. Every handoff adds latency, every translation raises risk, every delay means lost opportunity. And, as fidelity, speed, or explainability decline, AI value decays and trust in its potential withers.

The solution isn’t to eliminate tension, but to convert it into strong teamwork through shared standards and flexible systems. Seamlessly orchestrating people and data depends on a unified foundation, otherwise the tightrope frays and your chance of AI success plummets.

Finding balance in the data layer

Data teams should act like the head chef…providing ingredients, recipes, and structure, but allowing individual(s) to experiment and innovate.

— Heidi Lanford, NavAlytix AI

The best basis for collaboration is data. A shared data layer that flexes across roles and workflows enables organizations to balance competing priorities like speed and accuracy, innovation and stability, or discovery and outcomes as they cross the AI scalability tightrope. Unified pipelines, shared semantics, and accessible tools let teams work in sync, for instance switching seamlessly between pandas, SQL, and Spark within one workflow.

In this light, effective data analytics becomes the unifying operating fabric that turns organizational tension into productive alignment, allowing for the push and pull between data and people to strengthen competitive advantage. By offering a single source of truth, analytics helps traders to validate signals, quants to iterate faster, and engineers to monitor real-time systems, all on common ground.

Visual tools, alerts, and explainable metrics reduce ambiguity, replace opinion with evidence, and build cross-functional trust. By adding dynamic context, data analytics moves conversations from frustration to fruitfulness. From micro movements to macro dynamics, it also enables different roles to see each other’s market perspectives and apply limited resources to the most productive end.

For instance, our quant from earlier could rapidly backtest and confirm the trader’s issue, then make a collaborative decision: ship a 95% effective model now, or invest for a 99% effective one in six months. Alternatively, our quant and trader might agree criteria to deactivate the model in certain market micro-conditions to minimize risk and protect alpha.

Aligning for AI value

Data and analytics can be a unifying language to enable better collaboration.

— Cat Turley, ExeQution Analytics

Organizational alignment doesn’t mean the absence of tension; it means pulling in different directions without breaking the system.

Analytics can break down barriers between the front, middle, and back office, democratizing access to data and offering even less technical people opportunities to add value. Coordinated workflows create a virtuous circle of compounding improvement, driving faster consensus, quicker decisions, and stronger business outcomes.

Ultimately, that’s what unlocks scalable AI value: alpha, trust, resilience, and automation.

Think of KX as the collaboration engine that converts tension into performance: enabling traders, quants, and engineers to work in sync using familiar tools, unified pipelines, and shared context. We let you accelerate AI development while balancing competing priorities, giving you the strength and flexibility to succeed at scale.

Our high-performance data layer and time-series analytics are engineered for the unique demands of AI in capital markets. Known for our ability to efficiently manage mission critical data at massive velocity and scale, we’ll give your organization the shared foundation it needs to successfully cross the AI scalability tightrope:

• Real-time pipelines: Process and act on data with microsecond latency to support alpha generation, automated execution, and dynamic risk management
• Unified data access: Integrates structured, unstructured, historical, and streaming data into a single pipeline
• Time-aware architecture: Natively handles high-frequency, time-series, and streaming data, the lifeblood of capital markets AI
• Closed-loop learning: Supports continuous model updates and feedback loops that help AI systems adapt as markets evolve
• Integrated model lifecycle: One environment for exploration, training, deployment, and monitoring eliminates version drift and accelerates time to value
• Enterprise-grade governance: End-to-end auditability, explainability, and compliance to meet regulatory and risk mandates

Is your organization ready for the high-wire act of scalable AI success? See why the world’s leading firms, including the titans of Wall Street, trust KX . Alternatively, check out our ebook: AI in capital markets: Drive transformative value with five real-world use cases.