CHALLENGE FORUM PARTICIPANTS
As BFSI IT service management leaders explore emerging technologies like generative AI to modernise operations, a key challenge arises - they are inundated with incident data yet lack actionable insights due to its fragmented nature and failure to capture essential details. This results in a significant ‘data debt’ and cultural inertia around information management - key issues needing priority focus.
IT teams must first enhance underlying data quality through a shift from input-centric metrics towards business outcomes. Standardising enterprise language and modelling high-value services consumed are the foundation.
Maintaining the accuracy of historical records is essential for effective AI training, as detailed documentation of ticket resolutions fosters ongoing enhancement.
We recently gathered BFSI IT service management experts to share their perspectives and best practices.
Listen to the episode in the Enterprise Thought Leadership podcast, powered by TechPros.io
As BFSI IT service management leaders explore emerging technologies like generative AI to modernise operations, a key challenge arises - they are inundated with incident data yet lack actionable insights due to its fragmented nature and failure to capture essential details. This results in a significant ‘data debt’ and cultural inertia around information management - key issues needing priority focus.
IT teams must first enhance underlying data quality through a shift from input-centric metrics towards business outcomes. Standardising enterprise language and modelling high-value services consumed are the foundation. Maintaining the accuracy of historical records is essential for effective AI training, as detailed documentation of ticket resolutions fosters ongoing enhancement.
We recently gathered BFSI IT service management experts to share their perspectives and best practices.
🎙️ Listen to the full interview in the Enterprise Thought Leadership podcast, powered by TechPros.io
Drilling into Real Customer Pain Points
Justin Hemming, Head of Service Management for a business unit within Legal and General, collaborates closely with their IT shared services group. “We need visibility into issues specific to us versus total volumes across the organisation,” he said.
By segmenting incident types, performance metrics, and tracking process handling, the IT service group provided fuller visibility into his unit’s experience. “This influences how well they oversee third-party technology partners to optimise our support,” Hemming explained.
As internal customers under a shared services model, Hemming’s unit rigorously reviews requirements each period. He focuses on user journey mapping.
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My priority is smooth ticket resolution without excessive reassignments causing delays or productivity impacts.
Hemming stressed quality data segmented by business unit provides essential insights to drive improvements and inform AI capabilities. “Detailed historical records allow automating tasks tailored to each group’s needs rather than one-size-fits-all,” he said.
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Detailed historical records allow automating tasks tailored to each group’s needs rather than one-size-fits-all.
He believes this business-specific data focus is vital for IT to pivot from reactive operations into predictive, personalised services by applying AI. Without quality data capturing unique business unit needs, the power of emerging technologies like generative AI cannot be fully harnessed across the organisation.
Standardising Data Consistently
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A key prerequisite enabling automation and AI innovation is organisational awareness of data quality's importance.
noted by Peter Drake, LSEG’s Director of Enterprise Service Platforms. He observed resistance to change still arises as digital transformation gathers momentum.
“Although everyone talks about AI, teams pushing deployment may face barriers from suppliers and partners within the ecosystem whose business models may need to evolve to support their customer's AI adoption.”
Processing ticket updates meaningfully provides vital training data for AI instead of one-line descriptions or opaque tech jargon. Drake said, "We must educate teams that complete updates ensure reuse by future automation."
"Calling things consistently across tools is central so models can truly connect insights enterprise-wide," concurred Mike Glock, Managing Director of ServiceNow specialist Unifii.
Glock believes pragmatic approaches deliver quicker benefit than perfect taxonomies.
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Start with top services used to prove value before expanding carefully, not huge upfront data re-engineering.
Proving Concepts with Friendly Teams
To shift mindsets and showcase AI's potential, Drake suggested working initially with receptive support teams open to piloting capabilities like ServiceNow’s Gen AI once data quality matures.
“Target user groups eager to try innovations that enhance self-service and efficiency,” Glock recommended.
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Solve a major pain point exceptionally well as an exemplar for organic adoption across the business.
He noted such lighthouse initiatives make the most impact when aligned to business outcomes beyond technology goals.
Drake also urged quantifying hard cost impacts like productivity savings through automation.
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Leaders must influence peer executives by demonstrating IT delivers real business value not just ‘tech projects’.
As AI deflects more incidents or speeds resolution, supporting teams can be downsized or re-deployed. Drake wants this presented as tangible cost avoidance. “If we deploy Gen AI well across functions, we should need significantly fewer staff doing traditional activities. That hard cost saving motivates leaders to mandate change and opens up opportunities to reinvest in more valuable work.”
Monitoring for Meaning
Centralising fragmented systems into unified platforms also helps manage enterprise complexity.
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We focus extensively on the right tools meeting our need instead of having too many disparate monitoring solutions.
said Imogen Woodley, Senior Director of Service Operations for Finastra.
Her team instituted governance procedures ensuring metrics remain relevant through regular review. “If certain alerts trigger constantly with no action taken, we question whether they still provide value or just create noise,” said Woodley. Tying improvements to tangible gains like recovered team capacity and work-life balance resonated better.
Transitioning to centralised reporting and dashboards enabled self-service as well. “Rather than a black box team producing monthly reports, real-time visualisations allow groups to analyse trends, investigate root causes and make data-driven decisions themselves,” Woodley explained.
Integrating operational and services data remains a gap though. “I still need manual steps linking events to infrastructure capacity management for accurate demand planning,” noted Woodley. “Bridging ITSM and ITOM is an ongoing journey to full AIOps.”
Joanne Price, the Director of Service Management and Support at Fidelity International, is leading an initiative to enhance the organisation's operational efficiency by fully leveraging platform data for improved observability and shift-left opportunities. Recognising the crucial need for a unified approach, she emphasises the importance of bridging existing gaps between IT service and operations through a unified taxonomy.
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Achieving full visibility across functions is essential for us to effectively manage events, respond adaptively, and align capacity with demand.
Under Price's leadership, there's a clear vision for evolving their service model to encompass a more comprehensive common model that extends beyond just a technological focus, addressing vital business capabilities. She points out, "It's imperative to understand the interconnections between our managed services, regulatory requirements, and financial data. This comprehensive perspective is crucial for organisations like ours to succeed."
The Integrated Path Ahead
In closing, Glock summarised the path ahead:
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Relevance must drive data quality and automation initiatives towards genuine business objectives, not just technology concepts. Quick wins with high-value domains make the case for steady expansion across services.
He believes generative AI brings immense possibility once the fundamentals are addressed.
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Humans still lead innovation while AI handles scale, but trash in inevitably gives trash out. Clean input is the fuel to train reliable models.
With careful collaboration between IT teams and business leaders, BFSI institutes can transform fragmented systems into integrated engines where quality data connects seamlessly to emerging capabilities. This in turn powers responsive, resilient and automated operations driving superior customer experiences.
Unifii is a leading ServiceNow pure-play specialist bringing 14 years exclusive focus on leveraging the platform's automation and intelligence to transform service delivery. Recently acquired by European digital services firm Inetum, Unifii now complements its ServiceNow expertise by leading Inetum’s wider Microsoft, Salesforce and SAP solutions delivery in the UK and Ireland. With pragmatic guidance personalised to each client, Unifii helps organisations rapidly achieve tangible value and scale emerging capabilities like AI across the service value chain.
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