Key Takeaways
- AI adoption is accelerating across industries, but firms must overcome data bottlenecks to scale efficiently.
- The transition from traditional automation to real-time, adaptive AI mirrors the aviation industry's evolution.
- High-performance data engines and vectorized databases are essential to overcoming AI’s scalability and latency issues.
- AI is transforming multiple sectors, from finance and manufacturing to pharma and retail, driving innovation and efficiency.
- Firms that fail to harness AI effectively risk falling behind, much like those who resisted the transition from propeller planes to jets.
“What the history of aviation has brought in the 20th century should inspire us to be inventors and explorers ourselves in the new century.” – Bertrand Piccard, Aviator
In the early 1950s, the jet-powered race to break the sound barrier was accelerating fast — literally and figuratively. Yet, challenges with early jet aircraft led skeptics to question the need for this new fuel-hungry and unreliable technology. Even in military circles, many believed that tried-and-tested propeller planes would continue to dominate.
Today, AI finds itself in a similar position. Like early jets, AI is hungry for fuel — the vast datasets models must absorb to improve understanding and make more accurate predictions. Reliability is an issue too. Generative AI (GenAI) is especially famed for hallucinations with no clear basis in data, due to biases in training information, knowledge gaps, and more.
Despite these challenges, as well as workforce concerns and regulatory risks like the EU’s AI Act, firms are nonetheless prioritizing AI adoption and actively experimenting with how best to leverage it for speed, insight, and profitability. According to the 2024 EY AI Pulse Survey, 95% of senior leaders are now investing in AI, with 35% saying their organization is working on a roadmap for full-scale implementation.
At KX, we’ve spent decades at the forefront of this transformation, helping the titans of Wall Street and the best quantitative analysts in the world solve the challenge of turning high-volume and high-velocity data into actionable insights. Machine learning algorithms and discriminative AI have long been mission-critical in capital markets, but firms are increasingly also applying GenAI to interrogate alternative data. These apex innovators know the race is on, and that AI is vital to their future competitiveness.
From Wall Street to Main Street
“The horse is here to stay but the automobile is only a fad.” – President of the Michigan Savings Bank, 1903
Just as past industrial revolutions automated horsepower, we’re now automating brainpower and lowering the cost of cognition.
Like electricity or the internet, AI itself isn’t a product — it’s a utility. AI will be the enabler for groundbreaking services and solutions that reinvent global industry. Indeed, researchers are already shifting from pure technological innovation to solving real-world, targeted problems across various sectors.
The AI innovation and learnings that have transformed Wall Street are now exploding onto Main Street, unleashing opportunity and disruption in equal measure. While AI advances offer the financial sector $200 – $340 billion in value, that figure will be utterly eclipsed by the potential across other industries — up to $4.4 trillion annually according to a recent article from the McKinsey Global Institute.
In pharma, AI is transforming drug discovery and genomic analysis. Firms like Johnson & Johnson are leveraging AI to design and optimize drug candidates, to speed up clinical trials, and to enable targeted and personalized healthcare. With recent academic studies published by the National Library of Medicine putting the cost of creating a new drug at $2.8 billion, AI’s potential to double development success will have an enormous impact.
According to the National Association of Manufacturers, 72% of manufacturers report AI is reducing costs and improving efficiency. Optimized Industry 4.0 design and production processes, as well as supply chains, significantly reduce waste. For instance, AI-powered collaborative robots (or cobots) are working alongside people in companies like BMW and Boeing to improve productivity, while predictive maintenance based on IoT sensor data is slashing downtime by as much as 15% according to a 2024 article from Oracle.
AI-powered trend predictions, personalized recommendations, and autonomous delivery systems are changing the retail experience too. NVIDIA’s 2025 State of AI in Retail and CPG survey found that 98% of retailers are currently planning GenAI investments. Innovators like ASOS are already using AI-powered systems to improve demand forecasting, provide bespoke recommendations, or allow customers to run visual searches for similar products.
These advances — spanning discriminative, generative, and hybrid AI — are just a glimpse of what’s ahead. Whatever your industry, AI isn’t just going to change how you work — it’s going to change what you do. We’re rapidly progressing beyond traditional automation and data handling capabilities towards a general-purpose cognitive technology that can augment human thinking. AI is becoming an adaptive, contextual, and time-conscious intelligence that can drive faster and more insightful decisions based on shifting data rather than predetermined rules.
Beating AI drag
“You don’t concentrate on risks. You concentrate on results.” – Chuck Yeager, first pilot to break the sound barrier
In this environment, harnessing AI isn’t a choice but a necessity. Across industries, organizations are actively experimenting and iterating at speed — but the real challenge is building out solutions at scale, while ensuring transparency and explainability.
Just as aerodynamic drag slowed early jets, traditional data solutions create friction for AI-driven innovation—introducing complexity, latency, and rising costs. The faster companies try to scale AI, the more resistance they face.To break through this AI drag, firms need Completeness, Timeliness, and Efficiency in their data strategy:
- Completeness: Seamlessly manage vast volumes of structured, unstructured, historical, real-time, and alternative data to provide full AI context
- Timeliness: Ingest, process, and analyze time-sensitive data instantly, enabling AI models to act in real time
- Efficiency: Maximize performance and scalability with in-memory processing, columnar storage, and vectorized querying—delivering faster insights at lower cost
By addressing these three pillars, organizations can overcome AI’s scalability and latency challenges, unlocking the full power of real-time intelligence.
Analyzing massive volumes of both historical and streaming data in real-time, in structured and unstructured formats, is a deeply complex challenge. You need the ability to seamlessly ingest, process, and analyze vast amounts of time-oriented, high-frequency data. This allows you to uncover patterns, relationships, and behaviors at a scale and speed that no other technology can match in terms of cost-effectiveness and precision.
This is where organizations hit the AI sound barrier. At KX, we’re breaking through these limits by delivering an ultra-high-performance, real-time analytics platform purpose-built for AI-driven decision-making. Our unique combination of a vectorized time-series database, in-memory processing, and real-time streaming capabilities enables businesses to ingest, process, and analyze vast volumes of structured and unstructured data at unmatched speed and efficiency.
You can’t afford to stand still as others leverage AI to accelerate innovation and gain competitive advantage. Just like those backing propeller planes in the jet age, firms that fail to harness AI’s potential will be rapidly outpaced by those that use it to innovate, optimize, and scale.
The good news is you don’t need to break the AI sound barrier alone.
AI is transforming industries at an unprecedented pace, but scaling it effectively demands the right data infrastructure. See why the world’s leading firms trust KX to help them overcome AI drag with real-time, high-performance analytics that power smarter, faster decision-making.
