IT leaders are scrambling for skills
Demand for AI expertise has reached a critical point for IT leaders across the UK. Over 80% of IT managers report an urgent shortage of AI skills, a steep increase from 72% last year—according to a recent survey conducted by Censuswide.
Rapid escalation here points to the growing need for businesses to leverage advanced AI technologies but reveals a lack of internal capabilities to do so effectively. Shortages affect key areas like generative AI, large language models (LLMs), and data science—technologies that are rapidly becoming central to modern business processes.
Generative AI, for instance, is being adopted across industries to automate content creation, improve customer engagement, and streamline workflows, but its potential is limited by a lack of in-house expertise.
In similar fashion, LLMs, which underpin the functionality of AI-driven customer service bots and advanced analytics, require specialized skills that many businesses currently lack. Gaps in data science expertise adds another layer of complexity to this, as businesses struggle to make sense of massive data sets and translate them into actionable insights.
As AI continues to integrate deeper into business operations, this growing skills shortage threatens to slow down innovation and affect competitiveness.
Cloud investments are soaring, but are businesses ready to handle them?
Despite the mounting AI skills gap, IT leaders are still heavily investing in cloud technology. For many organizations, the cloud is essential to their digital transformation strategies, especially as AI and machine learning models become more reliant on cloud infrastructure for scalability and processing power.
Cloud strategies have become central to modernizing IT infrastructures, providing businesses with the flexibility and agility needed to keep pace with technological advancements.
Many organizations are shifting to AI-driven cloud platforms to automate operations, improve decision-making, and manage data more efficiently. These strategies are driving innovation and are seen as a means of staying competitive in an increasingly digital marketplace.
The ability to fully exploit cloud investments, however, is contingent on having the right AI and cloud expertise. Without the necessary talent, businesses risk underutilizing their cloud platforms and missing out on potential efficiencies and innovations that AI can offer.
Enterprise open source is the key to unlocking AI’s full potential
Enterprise open-source solutions are becoming more important in AI development, with 98% of respondents recognizing their benefits. Open-source software lets businesses adopt AI more quickly and cost-effectively.
Providing a collaborative development environment, open-source platforms accelerate innovation and reduce time to market.
Companies using enterprise open-source solutions benefit from a continuous stream of innovation. Since open-source communities frequently update software with new features and security improvements, businesses can stay at the leading edge of technological advancements without incurring the high costs associated with proprietary software.
Adding to this, open-source AI tools let organizations customize solutions to meet their specific needs, boosting innovation in areas like automation, data analysis, and customer service.
Cost-efficiency is another major advantage, as enterprise open-source eliminates the need for expensive licensing fees. It’s particularly important for AI projects, which typically require extensive resources and infrastructure.
Leveraging such open-source solutions, companies can maximize the impact of their AI investments while keeping costs manageable.
AI-powered cloud strategies are revolutionary, but the risks are real
Is your data safe? Privacy concerns cast a doubt on AI-cloud solutions
As AI-driven cloud strategies continue to grow, IT leaders are growing more concerned about the privacy and security of their data. According to the same Censuswide survey, 43% of IT leaders identify data privacy as a major issue in adopting AI technologies in the cloud.
The more AI is used to process, analyze, and store sensitive data, the higher the risk of breaches and misuse.
With strict data protection regulations like GDPR in place, businesses must make sure AI models comply with privacy standards. Failure to protect sensitive information can lead to severe legal consequences and damage to a company’s reputation.
As cloud environments become more complex and interconnected, guaranteeing data privacy becomes an ongoing challenge that requires comprehensive governance frameworks.
AI’s power demand is soaring – but what’s the cost to the planet?
Sustainability is another growing concern for IT leaders when it comes to AI-driven cloud strategies. Nearly 39% of leaders express worries about the environmental impact of their AI initiatives, specifically due to increased energy consumption.
AI models, especially large-scale ones like LLMs, require enormous computing power, which directly correlates to higher energy usage. This then increases operational costs and raises sustainability challenges for businesses that are committed to reducing their carbon footprint.
The cloud, while offering scalability and flexibility, also consumes a large amount of energy, especially when AI workloads are factored in.
Businesses are under growing pressure to balance their technological needs with sustainability goals, leading many to explore more energy-efficient AI solutions or partnerships with cloud providers committed to renewable energy.
Cloud-native applications are the future, and businesses are going all in
Cloud-native application development has become a top priority for IT managers, reflecting the industry’s shift toward more flexible, scalable, and resilient cloud-based solutions. These applications, designed to fully leverage the capabilities of cloud environments, are key for businesses looking to innovate rapidly and respond to market changes.
UK businesses, in particular, are projected to boost their cloud investments by as much as 50% by 2025, pointing to the growing reliance on cloud infrastructure for business operations.
Cloud-native solutions improve operational efficiency and let businesses deploy updates and new features faster, improving their ability to compete in dynamic markets. As organizations continue to transition to cloud-native architectures, the demand for skilled cloud developers and engineers will only increase.
Internal roadblocks are slowing cloud adoption, and it’s costing businesses
Internal silos are holding back cloud transformation
A staggering 96% of IT leaders report that internal silos are a major barrier to adopting cloud technologies—preventing departments from collaborating effectively, and leading to disjointed cloud strategies and missed opportunities for optimization.
Silos create inefficiencies by isolating information and resources, slowing decision-making, and preventing a unified approach to cloud integration.
Breaking down these silos requires strong leadership and cross-departmental collaboration. IT leaders must encourage a culture of transparency and communication to align cloud adoption efforts across the entire organization.
Without addressing these internal barriers, businesses risk falling behind in their cloud transformation efforts, which could lead to higher operational costs and reduced agility.
Cloud costs are exploding, and businesses are feeling the pain
As cloud adoption grows, so do the associated costs. Approximately 60% of businesses report rising cloud expenses, with nearly 40% noting that their cloud costs have increased by over 25%. The sharp rise here is due to several factors, including the complexity of managing multi-cloud environments, growing data storage needs, and the resource demands of AI workloads.
Uncontrolled cloud spending can quickly erode the expected benefits of cloud migration. IT leaders need to adopt effective cost management strategies, such as optimizing resource usage and leveraging cloud cost analytics tools, to prevent expenses from spiraling out of control.
Failure to manage these costs effectively will impact profitability and force businesses to scale back their cloud ambitions.
AI can’t succeed without people
Laura Lodwick, chief for talent at AND Digital, stresses that AI investment must go beyond just the technology itself. Businesses need to invest in their workforce to develop the broad skillset required for successful AI implementation—including technical skills like data science and AI model development, as well as soft skills like communication and problem-solving.
AI’s growing role in business decision-making also brings ethical considerations to the table. Employees must understand the ethical implications of AI to make sure models are designed and used responsibly.
It’s something that’s particularly important in sectors where AI informs critical decisions, such as finance, healthcare, and law. Through investing in both AI technology and human skills, businesses can build a workforce capable of driving responsible and effective AI development.
Ignoring AI upskilling could put your business at risk
Neglecting workforce development can create serious risks for businesses implementing AI. Without properly trained staff, companies face the potential of mismanaging AI projects, which could result in inaccurate predictions, biased decision-making, or compliance issues.
These risks are then compounded by the speed at which AI is evolving. Businesses that fail to upskill their teams may struggle to keep pace with the rapid technological advancements, ultimately losing out on competitive advantages.
AI is transforming business, but it’s got to be done right
Businesses are betting on AI to drive innovation and slash costs
IT leaders widely acknowledge that AI has the potential to rapidly accelerate innovation and reduce operational costs. Through automating routine tasks, analyzing vast datasets, and improving decision-making processes, AI lets businesses increase productivity and gain deeper insights into customer behavior, market trends, and operational efficiencies.
AI-powered solutions are already proving their worth in areas like predictive maintenance, personalized marketing, and supply chain optimization.
For companies, the value lies in their ability to scale these solutions and apply them across multiple areas of the business. Those that succeed will see faster innovation cycles and improved profitability.
Balance people and technology to get the most from AI
The real key to maximizing AI’s potential lies in balancing technological investments with workforce development. While AI can automate processes and improve efficiencies, it still requires human oversight to guide its implementation, ensure accuracy, and address ethical concerns.
A people-centric approach to AI development mitigates risks while making sure businesses can adapt as AI technologies evolve. Through this process of aligning technology with human skills, businesses can fully capitalize on the promise of AI while maintaining trust and responsibility in their operations.
Final thoughts
As AI continues to transform industries and cloud technology becomes the foundation of modern business, the question you need to ask is this: Are you investing enough in your people to keep pace with your technology? Without the right skills and ethical foresight, will your business thrive—or get left behind?
