Why Yesterday’s Tickets Power Tomorrow’s AI: The Critical Role of Historical Data in Service Management

Picture a cook trying to create an award-winning dish with no knowledge of flavor combinations, cooking techniques, or recipes developed over centuries. The result would be mediocre at best. That's the challenge many of us face when adopting AI in service management without leveraging our historical data.

In the recent past, the focus was purely on moving processes online. Data was captured, but the idea of machines learning from this information was a future concept, not a current priority. Fast forward to today, and that same data has become the secret recipe for AI success.

The organisations best positioned to benefit from AI are those that have preserved their historical data and, more importantly, understand how to leverage it. For AI to work optimally, it requires context, patterns, and history to make informed decisions and generate valuable outcomes.

Just as cooks rely on years of culinary knowledge to create exceptional dishes, AI systems need historical data to deliver remarkable results.

The Recipe for Success: Why Historical Data Matters

Just as a chef's skills improve with experience and knowledge of countless recipes, AI grows smarter with access to past data. In service management, historical records such as ticket logs, incident history, change records, and request fulfillment data provide the essential ingredients that help AI understand how your organisation operates.

This context enables AI tools to deliver accurate predictions and intelligent automation. Without this foundation, even the most sophisticated AI system operates blindly, missing the nuanced patterns that drive meaningful insights.

Real-World AI Applications Powered by Historical Data

Your historical data isn't just supporting AI; it’s fuelling advanced solutions:

Predictive Incident Management: AI analyses patterns to forecast potential outages before they occur. By understanding seasonal trends, equipment lifecycles, and failure patterns, your system can alert teams to high-risk scenarios days or weeks in advance.

Intelligent Ticket Routing: Historical resolution data trains AI to automatically assign tickets to the most qualified teams based on past success rates, reducing resolution times and improving first-contact fixes.

Automated Knowledge Discovery: AI mines past tickets to identify recurring issues and automatically generates knowledge base articles, turning yesterday's problems into tomorrow's solutions.

The Hidden Value in "Imperfect" Data

In the past, people thought it best to remove historical records in an effort to streamline systems or free up storage. When organisations begin to realise the power of their legacy data, their mindsets shift.

Here's the reality: even incomplete or inconsistent historical data contains patterns that AI can learn from. That ticket with missing fields? It still shows resolution pathways. Those valuable incident records with different categorisation systems? They reveal how problems evolved over time. Historical data can be remediated to transform unused information into a valuable asset, enabling AI models to identify deeper patterns and generate more accurate, context-driven intelligence.

Cloud storage is cheap, but the insights locked in your historical data are priceless. The question isn't whether you can afford to keep it, it's whether you can afford to lose it.

Don’t Wait For AI - Start Building Your AI Foundation Today

Every AI success story begins and ends with data. Even if not AI-ready yet, we need to start preserving our historical data now.

A good starting point is to ensure no data is deleted going forward. Of course, there may be some legitimate reasons to delete or archive our data, but this should be limited where possible. Rather than deleting valuable records, consider the future-focused approach of migrating and organising them instead - once that data is gone, it’s never coming back.

Precision Bridge’s automated data migration tools make preserving your valuable data effortless and seamless. We ensure your information stays secure, accessible, and perfectly positioned to unlock new opportunities ahead. Precision Bridge has successfully completed over 150 projects, migrating an impressive volume of over 100 terabytes of data. By leveraging our efficient data migration solutions, organisations have saved an incredible 100,000 hours collectively.

HSBC and IBM Pioneer Quantum-Enabled Algorithmic Trading in Financial Markets

The financial services sector has reached a pivotal moment. HSBC and IBM Quantum have successfully demonstrated the world's first quantum-enabled algorithmic trading application, marking a significant milestone for quantum computing in global financial markets.

Using IBM's quantum platform, HSBC developed algorithms that process complex market data and execute trades beyond classical computing capabilities. The demonstration focused on portfolio optimization and trade execution in volatile conditions, with quantum algorithms simultaneously evaluating vast combinations of market variables - a task that becomes exponentially harder for classical systems.

This development is particularly timely. As markets grow increasingly complex, traditional algorithmic systems face mounting limitations. HSBC's quantum approach leverages superposition and entanglement to explore solution spaces that would take classical computers impractically long to analyse. Beyond trading, quantum computing could revolutionize fraud detection, credit risk assessment, derivatives pricing, and regulatory compliance.

For financial services leaders, quantum computing is becoming a competitive differentiator. Organizations should build quantum literacy, explore partnerships with technology providers, and identify use cases delivering measurable value. The technology demands strategic attention and investment today.

As HSBC and IBM refine their algorithms, the industry approaches an era where quantum-classical hybrid systems become standard infrastructure. Those who act decisively will be best positioned to capitalize on the quantum revolution reshaping finance.

Click here to read the full story.

Google Quantum AI Achieves Breakthrough in Generative Quantum Advantage

Researchers from Google Quantum AI, the California Institute of Technology, and Purdue University have achieved a significant breakthrough in quantum machine learning by demonstrating "generative quantum advantage" - the ability for quantum computers to both learn and generate outputs that classical computers cannot efficiently produce. Using a 68-qubit superconducting quantum processor, the team showed that their quantum models can perform learning and sampling tasks in what they call the "beyond-classical regime," establishing a foundation for quantum-enhanced generative models with provable advantages over traditional computing.

The breakthrough centers on developing generative quantum models that are efficiently trainable while avoiding common pitfalls like barren plateaus and local minima that typically plague complex quantum systems. The team demonstrated these capabilities in two key scenarios: learning probability distributions that are classically intractable, and learning quantum circuits that could accelerate physical simulations. The research introduces innovative techniques including an exact mapping between families of deep and shallow circuits, as well as a "sewing technique" - a divide-and-conquer learning algorithm that simplifies the learning landscape.

What makes this research particularly significant is that it addresses a fundamental challenge in quantum machine learning. The team proved that efficiently identifying a constant-depth circuit representation for a general circuit requires a quantum computer, demonstrating that certain computational tasks are genuinely beyond the reach of classical systems. This moves quantum computing beyond simply producing hard-to-verify outputs toward creating systems that can learn patterns and generate useful new data in ways that classical computers fundamentally cannot replicate, regardless of their computational power. The implications extend far beyond theoretical computer science, opening new pathways for practical quantum applications in fields ranging from drug discovery and materials science to optimization and artificial intelligence.

Click here to read the full story.

Agentic AI to Dominate IT Budget Expansion Over Next Five Years

The enterprise technology landscape is on the brink of a massive transformation. According to new research from market intelligence firm IDC, agentic AI is expected to capture over a quarter (26%) of worldwide IT spending within the next five years, with year-over-year growth predicted at 32% between 2025 and 2029. If these projections hold true, investment in agentic AI-enabled applications and fleet management systems will reach an astounding $1.3 trillion by 2030.

This unprecedented surge signals more than just a technology trend—it represents a fundamental shift in how enterprises allocate their IT resources. The research indicates that spending on AI-enabled applications is expected to increase faster than any other segment, creating major competitive shifts across the software industry. Service providers are positioned to be most profoundly affected, with IDC predicting they will account for 80% of infrastructure spend to support the massive increases in agentic workloads. This could translate to a 10x increase in the number and complexity of both third-party and custom-built AI agents used by enterprises over the next five years.

The implications for IT leaders are clear: those who fail to integrate AI agents into their product development roadmap risk significant market share losses. As Rick Villars, group vice president for Worldwide Research at IDC, notes, there's a clear alignment between AI spending growth and IT leaders' confidence that effective AI use will drive future business success. However, this shift comes with challenges, particularly the massive increase in compute capacity required to support AI agent growth, which will initially demand significant infrastructure investment from cloud providers.

For enterprises navigating this transformation, the message is unmistakable—informed leadership and strategic planning around agentic AI implementation will be critical to success. Companies that proactively invest in platform solutions enabling secure and efficient agent management will be best positioned to capitalise on this technological revolution, while those that delay risk being left behind in an increasingly AI-driven marketplace.

Click here to read the full story.

AI models of Google and OpenAI win milestone gold at global maths contest

Artificial Intelligence has reached a remarkable milestone. For the first time, AI models developed by Google DeepMind and OpenAI have performed at a gold-medal level in the International Mathematical Olympiad (IMO), a competition known for pushing the limits of human mathematical ability.

As reported by Business Standard, Google’s Gemini DeepThink and an experimental OpenAI system solved five out of six Olympiad-level problems, a score that placed them in the gold-medal bracket - a level typically achieved by only about 11% of human participants.

This is a significant breakthrough because Olympiad problems demand far more than simple calculation. They require creativity, deep reasoning, and multi-step proofs - qualities long thought to be uniquely human. The success of these models shows how far AI has advanced in tackling complex, abstract problems that were once considered beyond its reach.

Google’s model followed the same time constraints as the human contestants, working within the 4.5-hour limit, while OpenAI’s model demonstrated similar capability through independent evaluation. Both systems relied on advanced reasoning strategies and immense computational power to achieve their results.

This achievement isn’t just about winning a math competition; it hints at a future where AI plays an active role in research, engineering, and scientific discovery. While human insight remains essential, AI’s growing ability to reason and solve complex problems opens the door to powerful collaborations that could help tackle some of the toughest challenges in the world.

Click here to read the full story.

Customers Praise Precision Bridge for Accuracy, Support, and Ease of Use

As a product led company that provides Service Management Data Migration Automation Solutions, customer satisfaction is a key focus at Precision Bridge.

We recently launched a Customer Satisfaction Survey, and we are proud to announce that our Customer Satifaction (CSAT) score is a PERFECT 5/5! These results reflect the value our solutions bring to the important transformation projects delivered by our partners and customers. From our outstanding support, to precision in data handling, this feedback highlights exactly why organisations continue to trust us with their migration and archiving needs.

We also want to thank all of our customers and partners for their feedback and to be assured that far from sitting on our laurels, we continue to strive to improve our products, especially in those areas we have had suggestions from our partners and customers or in areas where they have found migrations challenging. 

What Our Clients Are Saying

Outstanding Support

Support is often the difference between frustration and success. Over 70% rated our support as “Outstanding”, with the remaining 30% stating they were “Very Satisfied.” A key element of our support, which continues to set us apart, is our hands-on guidance.

Execution Speed Satisfaction

When you are migrating large volumes of data, speed matters, and we are delighted to report that 100% of respondents were satisfied with our execution speed. 

Ease of Use

We are continuing to work on adding more automation, improving our templates and improving our user interface, and this appears to be yielding results. 99% said we either met or exceeded their expectations in terms of ease of use.

Defining Record Sets & Migrating Attachments

Record sets and attachment migration are critical elements of a migration and  are make-or-break for many Service Management migrations:

• 86% found our help with defining record sets extremely valuable

• 100% were highly satisfied with our support in migrating attachments

Template Coverage

The pre-built templates provided with Precision Bridge help significantly accelerate migration projects,  reduce errors and improve accuracy. 86% of respondents said our coverage helped “A great deal” or “A lot.”

Low Monitoring Needs

Automating data migration means significantly reduced risk of something going wrong, especially if you have followed best practice in terms of pre-production testing. Our partners and customers are clearly experiencing the benefits.. 71% felt they needed to monitor the migration only minimally, confident in the quality of automation and execution.

What This Means for You

These results speak to what we’ve always prioritised: secure, accurate, and expertly supported data migrations tailored to Service Management systems. Our focus on reliability, clarity, and value-driven support continues to deliver strong results for our clients.

If you're planning a Service Management migration or want to discuss how our Data Migration Automation tool can help, contact us today.