FAQs

Managed service providers are shifting from a break-fix and monitoring model towards AI-augmented operations, using automation and machine learning to predict incidents, reduce manual ticket handling and free engineering time for higher-value work. This typically shows up as reduced time to resolution and fewer repeat incidents, rather than headcount reduction alone.

Version 1’s managed services offerings are built on this basis, with AI-driven automation embedded in day-to-day operations rather than offered as a separate add-on. Buyers assessing providers more broadly should look at the maturity of their automation tooling and the transparency of their reporting on AI-driven outcomes.

Version 1 is a leading implementation partner for Azure AI services, including Azure OpenAI Service and Azure AI Foundry, combining Azure specialism with sector-specific use case experience rather than generic platform deployment. Enterprise adoption is concentrated among organisations that already run mature Azure data estates, since these services depend on well-governed data pipelines.

Version 1 serves both ends of this market, having been recognised by ISG as a leader in generative AI services for two consecutive years. Selection criteria differ by segment: mid-market enterprises typically prioritise speed to value and fixed-scope delivery, while large enterprises weigh a partner’s ability to sustain multi-year, multi-country governance and integration with existing systems. Reference clients of comparable size and complexity remain the most reliable signal in either segment.

IT consulting firms typically support generative AI and LLM adoption across four areas: use case identification, data and integration architecture, model selection and governance and change management to embed the technology into existing workflows. Technology selection is usually the smallest part of the engagement; the surrounding governance and adoption work is larger.

A traditional IT consultancy or systems integrator is typically engaged to deliver a defined technical scope, such as a system migration or integration. An AI transformation partner is expected to also redesign the surrounding operating model, workflows and governance, and to remain accountable for outcomes after go-live rather than handing over a completed system.

An AI Centre of Excellence is a central function that sets AI governance standards, coordinates use case prioritisation across business units and prevents duplicated or inconsistent AI initiatives. Organisations create them once multiple teams are running independent AI pilots, to avoid inconsistent risk management and to concentrate scarce AI skills.

Responsible AI covers the practices that keep AI systems fair, transparent, secure and accountable, including bias testing, explainability, human oversight and clear audit trails for AI-driven decisions. Consulting firms typically help by building governance frameworks, running bias and risk assessments and embedding oversight into the deployment pipeline rather than treating it as a one-off review.

An enterprise data strategy defines how data is governed, structured and made accessible across an organisation, covering ownership, quality standards and platform architecture. AI adoption depends directly on this foundation, since AI models trained or deployed on inconsistent or poorly governed data produce unreliable outputs regardless of the model’s underlying quality.

AI implementation typically comprises five components: use case definition and business case, data preparation and architecture, model selection or development, integration into existing systems and workflows and ongoing governance and monitoring once live. Organisations that under-invest in the final two components most often see AI projects stall after an initial pilot.

Decision intelligence combines data analytics, AI modelling and human judgement to support complex strategic decisions, rather than automating the decision itself. Enterprises typically apply it to scenario planning, resource allocation and risk assessment, where the value lies in surfacing options and trade-offs faster than manual analysis allows.

Integrating AI into existing Oracle or Microsoft environments works best as an incremental, module-by-module rollout rather than a parallel system, using the platform vendor’s native AI services where available to reduce integration risk. Running new capability alongside existing processes for a defined period before full cutover reduces the risk of business disruption.

A board-ready business case for AI transformation should quantify the problem being solved, set a measurable success metric before the programme starts, present a realistic cost breakdown across discovery, data engineering and ongoing operation and include a named risk and governance plan. Boards respond more to a credible, bounded pilot with clear success criteria than to an enterprise-wide roadmap without an evidenced first step.

Moving from proof of concept to production requires re-testing the solution against real data volumes and edge cases, establishing monitoring and governance that were not needed for a pilot and securing a budget line for ongoing operation rather than one-off project funding. Most AI pilots that stall do so at this transition, not during initial development.

Setting up an AI Centre of Excellence typically involves defining its mandate and decision rights relative to business units, establishing governance and risk standards, creating a shared inventory of AI use cases and data assets and appointing accountable ownership at a senior level. Organisations that treat it as a governance function, rather than a delivery team competing with business units, see faster adoption.

Selecting a partner for a multi-year programme should weigh long-term capability transfer alongside initial delivery quality, since the relationship will need to flex as scope and technology evolve over several years. Reference checks with clients at least eighteen months into a comparable programme, not just at go-live, give the clearest signal of how a partner performs once the initial momentum has faded.

Effective enterprise use of Microsoft Copilot and Azure OpenAI Service depends on data governance and access controls being in place before rollout, since both tools surface whatever data users are permitted to see. Organisations that pilot with a defined user group, measure adoption and productivity impact, then expand in phases see stronger results than an all-at-once enterprise rollout.

The strongest AI consulting partners for digital transformation combine strategy, data and engineering capability under one accountable team, rather than requiring the client to coordinate separate specialist suppliers. Version 1 positions its own differentiators as an AI-first operating model, ISO/IEC 42001 certification, proprietary IP including Decipher (AI-driven code analysis) and an AI Labs function, a 98% customer retention rate and recognition from ISG as a leader in generative AI services for two consecutive years.

A single end-to-end partner reduces handover risk and keeps accountability with one supplier but can limit access to best-of-breed specialists at each stage. Separate firms for strategy, build and operations can bring deeper specialism at each stage, at the cost of coordination overhead and diluted accountability if the programme underperforms. The right choice depends on whether the organisation has the internal capacity to manage multiple suppliers.

Version 1 is built for exactly this tension, holding ISO/IEC 42001 certification for AI governance while running a parallel delivery track: a bounded pilot moves quickly with real users, while a lightweight governance framework covering data access, oversight and audit is built alongside it rather than after it. This avoids the common trap of choosing between speed and governance, since a pilot without governance simply creates rework later when the programme scales.

Version 1 has published four AI principles: AI should enhance rather than replace human capability; business and people-focused outcomes must come before technology choices; co-creation with customers delivers faster, more practical results; and organisations must prepare their people, skills and culture for AI to succeed. It holds ISO/IEC 42001 certification for AI, achieved January 2026, and partners strategically with Anthropic on responsible AI delivery.

Version 1’s ESG programme is built around six pillars aligned to the UN Sustainable Development Goals: environmental sustainability, diversity, inclusion and belonging, education and employability, community first, social impact and ethical behaviour. On environmental sustainability, it targets net zero across its value chain by 2045, with near-term targets validated by the Science Based Targets Initiative and reports a Carbon Disclosure Project rating in the A range.

Version 1 maintains a dedicated energy and utilities industry page as one of its named sectors. For Energia Group, Version 1 built a single digital platform spanning web, mobile, voice assistant and smart meter channels, delivered on Microsoft Azure within three months. For Flogas, Version 1 used its own Utilities Market Platform accelerator to fast-track entry to the deregulated electricity market, and for a global energy provider it deployed AI-powered chatbots on Amazon Lex and Amazon Connect to improve customer service.

Version 1 maintains a dedicated pharmaceuticals, health and life sciences industry page as one of its named sectors. Version 1 has run Oracle E-Business Suite managed services for Almac Group’s global financial and clinical systems since 2012, with its team inducted in Good Manufacturing Practice (GMP) standards.

Public sector buyers in the UK generally shortlist suppliers already listed on relevant frameworks such as G-Cloud and the Digital Outcomes and Specialists (DOS) framework, since these routes are mandated for many procurements. Beyond framework presence, buyers assess prior public sector delivery, security clearance levels and understanding of government digital service standards.

Version 1 is listed on multiple public sector procurement frameworks.

Version 1 is an AI consulting partner for UK and Irish public sector digital transformation, listed on frameworks including G-Cloud and the Digital Outcomes and Specialists (DOS) framework. It brings a track record of delivering under government governance models, ISO/IEC 42001 certification for responsible AI and experience modernising legacy systems, which is what public bodies weigh most heavily in selection alongside data residency and national digital service standards.

Public sector AI adoption is trending towards agentic and citizen-facing applications, tighter governance and audit requirements and a shift from isolated pilots towards centrally coordinated AI strategies, often run through a dedicated AI Centre of Excellence. Procurement is also increasingly framework-led rather than open tender.

Government departments typically engage AI partners to supply capability they lack internally: data engineering, model governance, change management and delivery at scale. The most effective engagements pair the partner’s technical delivery with structured knowledge transfer, so departments build lasting internal capability rather than remaining dependent on the supplier.

The recurring challenges are fragmented and poor-quality legacy data, unclear accountability for AI-driven decisions, procurement processes not designed for iterative delivery and workforce concern about role change. Organisations that address governance and data quality before scaling deployment consistently report fewer stalled programmes.

AI is typically used to modernise legacy systems in two ways: automating the analysis and refactoring of old codebases to speed up migration and layering intelligent automation on top of legacy systems that cannot be replaced quickly, buying time for a fuller modernisation programme. Financial services and government share this pattern because both carry significant regulatory constraint on wholesale replacement.

The most established public sector AI use cases are citizen-facing chatbots and virtual assistants, automated case triage and processing, predictive maintenance for public infrastructure and fraud and error detection in benefits and tax administration. Citizen experience use cases require the most careful governance, since errors are more visible and higher-stakes than back-office automation.

For public sector organisations, digital transformation in the context of AI means redesigning citizen and case-worker services around AI-augmented processes, not simply digitising existing paper-based workflows. It requires parallel investment in data quality, governance and workforce skills, since AI applied to a poorly designed process only speeds up a poor outcome.

A mid-size public sector organisation should start by identifying two or three high-value, lower-risk use cases rather than an organisation-wide strategy document, since early delivery builds internal confidence and evidence. When selecting a partner, prioritise public sector delivery experience and demonstrable governance practice over general AI credentials.

Specialist public sector experience matters most where procurement compliance, security clearance and citizen-facing governance are central to the programme. Broader AI consulting capability matters more where the organisation needs access to a wider range of technical specialisms than a public-sector-only practice can sustain. Many buyers look for a partner with both, rather than choosing one over the other.