As we move into year three of Generative AI, its potential for enhancing operations, driving innovation and building a competitive edge is becoming ever clearer, as are the challenges and risks.
The world’s most innovative companies have moved, or are moving, beyond experimentation to integrate AI-first models, adjusting spending and recalibrating business strategies to maximise ROI and stay ahead of the curve. PwC found almost half of the US’s Fortune 1000 companies now have AI fully embedded in their workflows, with a third using it in their products and services.
This year, priorities should include solidifying foundational structures, measuring outcomes and adjusting programmes to make Gen AI work effectively and safely and secure that advantage. Those late to the party or failing to understand the critical need to constantly evolve and manage Gen AI may struggle to ever catch up.
In a recent survey by Ernst & Young (EY), 97% of senior business leaders reported positive returns on their AI investments with a third planning to spend £8 million or more on AI initiatives this year while UK software buyers expect to increase spending by an average 5-15%. Organisations that commit 5% or more of their total budget toward AI are seeing more positive returns than more cautious investors with the biggest in operational efficiencies, (84%) and employee productivity (83%).
It is essential for c-suite executives to have a full and proper understanding of the AI landscape, both within their sector or industry and beyond. Trying to experiment or get to grips with Generative AI in a bubble or silo is like constantly trying to reinvent the wheel when budgets would be better spent targeting funding to improve its performance. Progressive organisations will research thoroughly the tools, programmes and platforms used by competitors and sector leaders to learn what has and has not worked for them and how they are prioritising their AI budgets in 2025 and beyond.
Chaotic implementation has led to lost ROI and confidence in some early iterations of Gen AI-powered programmes as over-eager organisations put the cart before the horse, buying the latest hyped-up tools or platforms through FOMO (Fear of Missing Out) without really understanding their value, testing them or building sound foundational infrastructure. Only 12% reported using sandboxes in one survey, for example, leaving too much to chance and increasing risks of damaging failures. Getting it right demands a disciplined approach with co-operation and collaboration from every department and at every level.
UK senior decision makers told Capterra’s 2025 Tech Trends that successful technological implementation was the greatest challenge they now faced as they moved onto the next phase of adoption, followed by training and upskilling employees, economic and geopolitical pressures, assessing value and risk of AI and identifying the right technologies to invest in.
The most innovative companies will be patient, appreciating that real returns on investment may take years to materialise in terms of profit, but that agile, future-focused and strategically aligned Gen AI-led programmes will ensure long term competitive growth.
(See our previous insight on the five stages of AI maturity)
Here we look at trends within the three main focuses for the AI spending priorities c-suite executives should be considering over the next 12 months: Tech, data and upskilling the workforce.
Spending on Tech:
Globally, spending on hardware and devices, including computers and smartphones, is likely to grow by £10 billion to £118.5 billion, with Covid lockdown working-from-home technology nearing the end of its useful life and new AI-powered devices offering far more possibilities.
Spending on software is expected to see an even greater increase, accelerating by 13.2% in 2025 to £230.5 billion.
Most software buyers in the UK expect to spend between 5-15% more on digital systems this year as they seek to increase ROI on their AI investments, according to the Capterra research. Six in 10 will dedicate one to four months choosing the right product and 38% see implementation as a key challenge.
The survey found security will be the highest priority, followed by AI, IT management, IT architecture and business intelligence and data analytics.
Automation: Justina Nixon-Saintil, vice president and chief impact officer at IBM, believes AI automation will be the story of 2025. “Any tasks or jobs in the company that could be automated by AI will happen within the next year,” she said.
Alicia Pittman, global people team chair at the Boston Consulting Group said a priority should be custom GPTs and mini-automations to build bottom-up power, enabling entire knowledge-based workforces to boost productivity and quality. She said: “It’s super quick, and it doesn’t require big investments or processes.”
CRMs: This year, more companies are expected to move away from in-house Gen AI solutions towards buying partner solutions. Customer Relationship Management (CRM) platforms such as HubSpot, Salesforce and Amazon AWS are constantly improving their AI-powered offerings with broad options for customised integrated systems that can enhance almost all business objectives, from identifying new product or market opportunities and analysing big data to hyper-personalised marketing and sales which vastly improve customer experience, boost sales and build loyalty.
Fortune Business Insights predicts that the CRM market will more than double from the £50 billion spent in 2022 to £120 billion by 2029. Most platforms offer free, simplified versions of subscription models which can keep costs down, such as Microsoft Pilot and Salesforce Einstein, enabling smaller businesses and start-ups to capitalise on these fast-evolving Gen-AI powered technologies.
Spending on Data:
As the AI landscape matures, decision-makers at innovative organisations will look to upscale, standardise and refine AI use with connected, clean data across all functions and lines of their organisations to ensure it remains relevant, agile and that risks are understood and managed.
Two in five UK companies identified data quality as the greatest challenge to successful AI adoption in a survey by Hitachi Vantara. Nearly half reported significant challenges with data storage and 56% admitted to using less than half their data. Meanwhile 83% of senior business leaders say stronger data infrastructure would enable faster AI adoption.
Gen AI is only as good as the data on which it is trained and building scalable and flexible data architecture that can manage speed, variety and volume of data is critical to enable any organisation to scale up programmes and ensure maximum ROI, potentially accelerating adoption by 30%. The IBM Institute for Business Value found that poor data quality costs the US economy around $3.1 trillion a year.
Companies like Netflix and Tesco have shown the value of substantial investment in data and data infrastructure, able to process huge datasets to hyper-personalise their services, innovate, and get closer to their markets. Innovative enterprises are investing in tools including ETL (Extract, Transform, Load) processes, data lakes, or iPaaS (Integration Platform as a Service) solutions to optimise the value of their data.
Cloud storage: More than half of IT spending in key market segments is projected to shift to the cloud by the end of 2025, with global spending on cloud computing services expected to reach £1 trillion. Organisations are moving towards multicloud, open data storage to avoid vendor lock-in.
The UK government has welcomed news of £25 billion investment in data centres which will provide more computing power and data storage building infrastructure to boost AI development and innovation.
Businesses will need to manage 150% more data by 2026 and Gartner predicts that spending on data centres will climb by 15.5% in 2025 on top of a 35% rise in 2024.
Security: With this increasing reliance on data and cloud storage, security becomes ever more essential, especially in sensitive sectors such as finance, defence and healthcare. IBM reported the average cost of a data breach at more than £3.5 million in 2021. Gartner expects cybersecurity spending to increase 15% in 2025.
ESG: Organisations also need to think about the energy costs and impact on Environmental, Social and Governance (ESG) credentials of increased use of Gen AI and other technologies, investing in renewable sources wherever possible. Two thirds of senior leaders fear the negative impact of increased AI use on their sustainability targets and energy supply.
Steve Wanner, EY head of Americas Industrials & Energy said: “Leaders are waking up to the energy challenges inherent in scaling AI. To create innovative solutions that enable energy efficient and sustainable AI growth, companies must collaborate across the value chain, connecting the dots from energy providers to the end-use AI customer.”
Technology could also be part of the answer. Deloitte found three quarters of public companies planned to invest in AI-powered reporting tools to help them evaluate, analyse and share ESG data to comply with tightening regulations worldwide.
However, the biggest rewards are likely to be found in the joining up and safe (anonymised) data-sharing of and between AI systems, which demands greater collaboration within and between organisations, sectors and industries.
Spending on upskilling:
This year, CEOs and other c-suite decision-makers will be more hands on and, hopefully, AI-literate, and therefore committed to restructuring operations so that departments have access to data scientists and AI leads as well as focusing on educating and upskilling all knowledge-based workers and ensuring investment is more disciplined, methodical and targeted.
The speed of Gen AI evolution has taken even tech experts by surprise since ChatGPT opened it up to the masses in November 2022, so it’s hardly surprising that most of the workforce, from CEOs to customer agents and even IT managers, often feel overwhelmed and even intimidated by it.
Almost half of companies admit to lacking the know-how to integrate AI while 90% of executives say they do not know their workforce’s AI skill and proficiency. Four in five IT professionals say they are confident they can adapt but just 12% have significant experience working with AI. Organisations should consider the users of the technology before they buy it and the current skills landscape to avoid workforce burnout and unsafe or under-use of the tools and platforms.
This skills gap threatens to seriously destabilise and restrict the opportunities offered by Gen AI while increasing risk. Babies born in 2025 will be the first of Generation Beta and will grow up with AI all around them. Until they mature, businesses need to retrain their own workforces and bring in data science and Gen-AI planning expertise where it is lacking.
Tech companies are ahead of the curve on this. Amazon developed a Machine Learning University, investing heavily in training and development programs to build its internal capabilities.
IBM has made a commitment to scale up two million of its workers in AI by 2026. Nixon-Saintil said. “There’s a sense of urgency in making sure we are not leaving people behind.”
The growing sophistication of Natural Language Processing (NLP) will continue to enable employees at all levels to leverage AI, so the workforce needs to undergo continuous learning to keep up with new and evolving tools, platforms and emerging risks. Staff who will be using Gen AI models such as Chat GPT, Microsoft CoPilot and Google’s Gemini need to learn to craft clear prompts, interrogate the responses and use them to augment their own productivity and quality of work while understanding the inherent risks and having a clear chain of supervision.
EY says 59% of organisation are planning to increase training for workers on the responsible use of AI in 2025, up from 49% six months ago.
Investment in AI is only expected to absorb around a fifth of IT spending next year. Much more, then, will go into infrastructure and the people required to make it work. Both programmes need to be organisation-wide to enable AI-first business models.
Senior leadership also need to prioritise investing in their own AI literacy to make rational, evidence-based decisions before spending on AI programmes. In the EY survey, 54% of respondents said they felt they were failing as a leader as they struggled to keep up with AI’s rapid growth.
The pressure to act decisively is intensifying. Yet many leaders find themselves navigating incremental changes, unsure of how to transform their business models or confidently prove GenAI’s ROI.
Responding to feedback from our c-suite and senior leadership clients, Rialto are facilitating a virtual strategic collaboration programme between leaders from across the globe, to share experiences, perspectives, and best practices on GenAI adoption. It is designed to support leaders with the critical insights, tools, and actionable strategies needed to broaden their understanding of the complexities & opportunities of GenAI.
All participants in the programme will receive a personalised and group alignment report, to support them to more confidently lead their organisation in the GenAI era
To find more details and register onto the Adoption of GenAI Global Virtual Dialogue click here.
