Karl Smith of Creative ITC outlines pragmatic steps to AI success, combining aspiration with the pursuit of business benefits.
IT budgets are on the rise. 91% of technology decision-makers project higher allocations​ this year and AI, machine learning, large language models and automation continue to dominate IT roadmaps.
But there’s been a noticeable slow-down AI adoption over recent months as business leaders’ attitudes have evolved. Replacing the headlong exploration of endless possibilities, there’s widespread acknowledgement that AI’s potential and an organisation’s ability to leverage its benefits are two very different things. Firms are now prioritising considered strategies to overcome challenges and ensure AI delivers tangible business value.
And no wonder. Gartner research reveals a striking challenge: less than half of digital initiatives meet their business targets​. This disconnect highlights the urgent need for organisations to rethink their approach digital transformation, aligning AI initiatives with clear, measurable objectives. While the thirst for competitive advantage shows no sign of abating, the stakes are higher than ever to demonstrate ROI from IT innovation.
An intelligent approach
Of course, AI has already proven its value in many areas. Combined with advanced data analytics, IoT adoption and ML deployments, it’s empowering businesses to make informed, data-driven decisions by uncovering patterns and forecasting trends that were previously out of reach.
Automated threat intelligence, machine learning and fraud analysis applications have benefited risk management, cybersecurity, financial and legal operations, flagging patterns and risks to expedite necessary interventions. In healthcare, AI aids drug discovery, disease detection and patient treatments. Intelligent chatbots are prevalent across the retail sector and customer services interfaces, while in manufacturing and warehouses robots and autonomous vehicles are being deployed to reduce risk and accelerate operations.
Generative AI is revolutionising creative industries, supporting content creation, research and audience insights. In architecture, engineering and construction, digital twinning and OpenUSD simulations are among the techniques informing design, aiding materials selection, improving how plans are translated into completed structures and driving sustainable development.
Critical questions
However, for every ambitious AI outcome, unfortunately there’s a potential pitfall. While AI promises to unlock new possibilities, it also raises critical questions about control, change management and IP protection. As firms integrate these technologies, they must carefully consider how to retain creative control while leveraging AI’s capabilities to enhance, rather than replace, the design process. At the same time, some AI tools raise critical questions around originality, authorship and data ownership; AI models are trained on vast datasets, which may include proprietary or copyrighted material, meaning IP protection is a major concern.
In addition to ethical and legal implications, AI deployments can also reveal underlying physical problems. Existing IT infrastructure is frequently the most severe limitation exposed; one in three IT leaders identify this as their biggest issue. Immense AI processing requirements all too often swamp data centre and network capacity, causing latency and outages. Efforts to deliver insights to stakeholders can also uncover flaws in IT architectures that weren’t designed to share huge datasets securely at speed and scale.
Another commonly overlooked aspect is an organisation’s data management processes, which are crucial for AI adoption. If data quality, modelling and storage are not invested in, IT leaders will be unable to deliver the actionable insights needed by their organisations.
In an effort to overcome the limitations of on-premise IT infrastructures, cloud migration has long been a keystone of digital transformation. Unfortunately, many businesses have fallen victim to unexpected costs associated with public cloud, such as data egress charges and price hikes following tempting entry fees. For those who have experienced both the positives and pitfalls of public cloud adoption, cloud repatriation is becoming recognised as a valuable strategy. Almost all (96%) IT leaders declared cost saving as the main benefit of repatriating workloads and applications away from public cloud providers. A similar number (95%) reported an improved security posture, and 85% firms enhanced performance, control and business agility.
Cloud choices demand expertise and continued focus – sprawl leads to rising costs, complexity, and security and compliance issues.. Robust governance is required to improve visibility and control, enabling businesses to right-size their cloud consumption by identifying waste and making data-driven decisions to optimise usage and manage costs. The expertise of a managed service provider delivers savings on infrastructure, upgrades, optimisation, licensing, application deployment, support and headcount.
The human factor
Alongside IT infrastructure challenges, Gartner’s 2025 CIO Survey reported some common pitfalls organisations should avoid to ensure their transformation programmes succeed.
A common hurdle to AI adoption is lack of internal resources, hampering progress and resulting in poor ROI. 71% of IT leaders plan to increase investment in IT personnel this year to facilitate digital transformation and ensure their teams have the new skillsets required to implement and deploy AI solutions in the long-term. For organisations unused to deliver this type of major digital transformation, seeking the support of external experts can also accelerate progress and optimise results. Their technical know-how and industry-specific insights can be invaluable to help overcome obstacles, address new security risks and avoid over-running budgets and project timelines.
Looking outside the IT department is also critical. Company-wide collaboration is essential for success and firms that instil co-ownership of digital transformation across their C-suite are 1.5 to 2 times more likely to enjoy ROI from digital investments. Aligning IT initiatives with overarching business goals and setting clear KPIs ensures efforts are both relevant and measurable. Rolling out AI programmes should involve staff from across the organisation from the outset to determine use cases, understand system dependencies and required functionality, and evaluate workforce readiness. When teams share a common goal, AI adoption will be more seamless and impactful.
AI implementation has the potential to hinder operations, resulting in downtime, friction and loss of support. Devising and sharing an AI roadmap can help a business navigate the change. Clear communication of a phased plan, combined with employee training, will help to ensure changes are supported and successfully integrated into workflows. Focusing on incremental improvements and communicating quick wins builds momentum and confidence. For example, automating repetitive admin tasks or enhancing specific workflows with AI-driven insights will demonstrate immediate benefits and pave the way for larger-scale initiatives.
A more considered, gradual approach to AI not only mitigates risks but also ensures that every investment contributes tangible business value. Businesses that strike the right balance between aspiration and pragmatism will navigate the AI minefield, unlock greater value and sharpen their competitive edge.
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