Enterprise AI
Enterprise ai:Building for scale in the age of rapid adoption
78% of organisations now use AI in production, with 71% deploying generative AI regularly, up from 65% just twelve months ago*. Companies are putting guardrails in place, building AI teams and reskilling workers to scale gen AI use.
Across global surveys, companies report tightening generative-AI safeguards, adding specialist roles and training staff to deploy at scale. Adoption keeps climbing, with more than three quarters of organisations using AI in at least one function and regular generative-AI use surging over the past year, especially among firms with more than $500 million in annual revenue. The shift has been unusually fast across sectors, regions and company sizes, what began as AI experimentaton now underpins critical business systems. But progress still feels slow for many teams – deployment has outpaced the foundations, exposing gaps in capacity, controls and engineering discipline across teams and processes.
We’ve been told “AI will revolutionise everything”… but for many organisations, it’s just not working. AI isn’t failing because it’s not powerful enough. It’s failing because organisations aren’t ready enough. AI success depends on data, people, process, and infrastructure alignment. SCC is your partner that builds that bridge.
*www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
From adoption to optimisation
Twelve months ago organisations were questioning whether AI could deliver value. Today they’re dealing with the consequences of rapid deployment; sprawling model portfolios, escalating compute costs and governance gaps that are creating regulatory exposure.
AI success now depends on architectural maturity. Organisations that built proper foundations, scalable infrastructure, robust data pipelines and comprehensive governance are the ones reporting added value. Those that prioritised speed over structure are facing mounting technical debt and operational complexity.
Success is built on systems that accelerate sustainably, where each deployment strengthens rather than complicates your AI estate.
Building for real-world demands
Our teams design and build full-stack AI foundations that deliver value and scale across infrastructure and operations. SCC’s approach has been refined across hundreds of enterprise deployments, in regulated and high-scale settings. Not all AI is equal. There is a clear difference between systems that merely run and systems engineered for sustainable outcomes and performance that holds under load, controls that stand up to scrutiny and operating rhythms teams can live with. As the curve steepens, sturdy fundamentals remain the most reliable way to turn enthusiasm into durable impact.
Key enablers for successful AI scale
Responsive infrastructure for AI workloads
What changes when pilots meet production? Infrastructure must pivot quickly as requirements shift and demand climbs. Baseline specifications built for proofs of concept rarely hold under real load, which drives cost leakage and uneven performance. SCC shapes forward-ready infrastructure strategies using proven technologies across cloud, on-premises and hybrid environments. SCC focuses on right-sizing capacity and tuning network design so growth tracks business demand, avoiding bottlenecks and service disruption. For example, a retailer rolling out computer vision during a seasonal campaign can see inference traffic double; a responsive design scales edge GPUs and backbone bandwidth without overprovisioning.
Data foundations for agile AI
Organisations gaining value from AI rely on adaptive data architectures that accommodate constant change. These approaches blend streaming pipelines, feature stores and flexible governance to keep iteration moving. SCC delivers scalable data platforms that integrate with leading technologies while maintaining data reliability, transparency and compliance. SCC streamlines access and quality so teams have what they need for ongoing development and secure operations. For instance, a fraud team can promote a new feature set from stream to store within hours, with retention and access policies enforced automatically.
Efficient AI operations
Scaling AI from test to real life hinges on operational discipline, not only better models. Mature environments feature monitoring, controlled update paths and systematic quality checks to reduce risk and optimise outcomes. SCC embeds best-practice MLOps (Machine Learning Operations), experiment tracking, model and data versioning, automated evaluation and proactive cost controls, so teams maintain stability yet adjust to business shifts without added complexity. Picture a demand-forecasting model that rolls back after drift alerts while a shadow candidate is evaluated in parallel.
Flexible architectural patterns
Advanced AI deployments rely on orchestrating multiple specialised models and tailoring placement for varied workflows. SCC engineers systems for intelligent routing and model selection that support demand peaks and accuracy while containing resource use. SCC uses streamlined orchestration technologies, matching processing style to business needs, from edge to core. For example, low-latency vision runs at the edge, while batch retraining sits in the core under a lightweight control plane.
Seamless, Adaptive Governance
Rapid AI adoption can expose gaps in oversight. Rigid controls slow teams, but too little structure creates risk. So how do teams keep speed without compromising standards? SCC implements adaptive governance that automates policy checks and balances compliance with developer efficiency. SCC focuses on scaling oversight naturally with usage, so control supports progress rather than hindering it. Consider a controlled release process where sensitive prompts and outputs are auto-screened and audit evidence is captured as throughput rises.
Key opportunities, challenges and SCC solutions in Artificial Intelligence
What our customers say
“Working with SCC, we were really at home. We felt like we had a similar culture in the way that we approached the problem and very quickly we understood what needed to be done. There was a really good conversation about how practically we could take that forward to deliver the solution against the goals we had.”
Chris Fenton,
Head of Data & Architecture,
Browne Jacobson
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Build your AI future
The organisations succeeding with AI share common characteristics: they’ve built robust foundations that support rapid iteration, implemented governance that enables rather than constrains and developed operational capabilities that ensure sustainable scaling. Most importantly, they view AI as a continuous journey rather than a destination.
The window for establishing AI leadership remains open, but it’s narrowing. Organisations that build proper foundations now will compound advantages as AI capabilities expand. Those that accumulate technical debt through hasty implementation will find themselves increasingly constrained.
Your competitors are moving quickly. The question isn’t whether you can afford to invest in proper AI architecture, it’s whether you can afford not to. Every month of delay represents lost opportunity and competitive ground that becomes increasingly difficult to recover.
Discuss your AI ambitions with a member of our team. We’ll develop an architecture that delivers immediate value while establishing foundations for sustained leadership in your industry.
Meet our specialists
Richard Knott
Head of AI
As Head of AI, Richard Knott leads the design and delivery of cutting-edge artificial intelligence solutions that help customers unlock the full potential of their data. With a deep background in hyperscale cloud architecture and intelligent automation, Richard partners with SCC customers to embed AI into their core operations – accelerating innovation, improving decision-making, and driving measurable business outcomes.
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Jonathan Higginbottom
SCC AI Business Consultant
Jonathan has learnt over 35 years the importance of business transformation to change people and processes enabled by advanced technologies including AI, data and automation to accelerate business strategy and create a platform for continuous improvement.
Jonathan’s passion is to work closely with an organisation to assess and create business cases justifying investment in high impact areas where developing capabilities in advanced technology will bring competitive advantage and then to oversee and assure the business transformation and benefits realisation.
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Andrew Flavell
Andrew Flavell is a Technology Strategy Director specialising in Enterprise & Networking technologies at SCC. He is a Professional Member of the British Computer Society (MBCS). Andrew joined SCC in 2016.
Andrew leverages his extensive IT industry experience to provide strategic advice and technical guidance. He uses his background to offer innovative solutions that help businesses reach technology goals. Andrew is skilled in market analysis, competitor assessment, and technical and sales execution.
Andrew recognises the dynamic nature of the digital environment and the importance of effective digital technology utilisation which is now acutely pivoting in the age of AI. Andrew sets the strategy and direction of the Enterprise and Networking Business Unit here at SCC.
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Am Bhooi
Chief Technical Officer, UCC
Am is a seasoned technology leader with nearly three decades in IT and communications, delivering large-scale transformation and innovation for major blue-chip clients worldwide. As CTO, Am blends business acumen with technical expertise to set a clear vision and strategy that drive tangible results. A passionate advocate for agentic AI, Am is leading the integration of intelligent agents into real business ecosystems—automating work, boosting efficiency, and elevating customer experience—while setting a high bar for excellence for customers and colleagues alike.
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