Martin Murphy

Organisations are adopting GenAI at pace, but real-world adoption is proving far more complex than headlines suggest. 1️⃣ A mixed-methods study of 304 global decision-makers shows GenAI adoption is shaped by both internal capacity (skills, change readiness) and external pressures (regulation, tech complexity). 2️⃣ Perceived relative advantage (e.g., better efficiency, automation) is the strongest driver of GenAI uptake. 3️⃣ Staff skills significantly influence adoption: without upskilling and AI literacy, organisations struggle to realise GenAI's benefits. 4️⃣ Complexity is a key barrier: integrating GenAI with legacy systems, ensuring explainability, and managing unfamiliar workflows hinder adoption. 5️⃣ Surprisingly, regulatory pressure was not a statistically significant barrier, though concerns about privacy, accuracy, and compliance were widespread. 6️⃣ Change capacity moderates adoption: high readiness reduces the drag of complexity, while low readiness makes adoption more dependent on staff expertise. 7️⃣ The study validates the TOE (Technology-Organisation-Environment) framework but suggests it needs adaptation for GenAI's fast-evolving, high-risk landscape. 8️⃣ Most organisations still lack clear strategies for managing GenAI's ethical, legal, and sustainability risks, underscoring the need for governance innovation. 9️⃣ Findings highlight stark cross-sector variability: organisations in regulated or safety-critical sectors face greater hurdles than those in creative or marketing roles. 🔟 Overall, readiness, not hype, determines whether GenAI becomes a productivity tool or a stalled experiment. ✍🏻 Dr Laurie Hughes, Fern Davies, Keyao Li, Senali Madugoda Gunaratnege, Dr Tegwen Malik, Professor Yogesh K Dwivedi. Beyond the hype: Organisational adoption of Generative AI through the lens of the TOE framework–A mixed methods perspective. Elsevier. International Journal of Information Management. 2026. DOI: 10.1016/j.ijinfomgt.2025.102982

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This morning, I joined BBC Radio 5’s Wake Up to Money with Felicity Hannah to dig into how AI is really affecting jobs in the UK. AI is having a two-sided impact on businesses – and the conversation needs to go beyond just “AI is taking our jobs.” It’s also pushing leaders to rethink roles and even create new ones. We should be asking: what can AI genuinely improve, and where do humans still do it better? By now, most of us know that repetitive, structured tasks with clear processes are most at risk – and that often includes many entry-level roles. But that doesn’t mean new opportunities aren’t emerging. Take our Hire, Train, Deploy program, for example – it helps early-career talent build in-demand skills and places them straight into business-critical roles. That’s especially valuable now, as UK employers face rising costs due to increased national insurance contributions. There are still plenty of areas where AI just doesn’t cut it. Right at the top of that list is the ‘human premium’ – skills like strategic thinking, creativity, and emotional intelligence. AI isn’t here to replace everyone, but it is moving fast. As it continues to handle more of the background work, it’s on us to ask whether our skills are keeping up with what the future of work demands. Listen to the full conversation here (from 19:28 mins): https://lnkd.in/eDDNbx_g Henry Jones Tom Elliott Robert Walters

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 CF's second installment of #ValueInHealth series takes a closer look at the systemic decline in acute care productivity, what it is costing the NHS, and what can be done to reverse it. We’ve analysed NHS productivity over the last decade, it shows that while the pandemic is often blamed for productivity challenges, the decline in acute care productivity began before COVID-19. While the exact cause can't be pinpointed, this is broadly driven by a mismatch between rapid workforce growth and plateauing activity levels. Since 2018/19: ➡️ Productivity in acute care has dropped by 10-14% despite rising spend and workforce ➡️ This decline has cost the NHS £12–1bn, funds that could have been reinvested to shift care closer to home, in line with the NHS 10 Year Plan. Read the full article here: https://lnkd.in/evevG5WM

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Last week, CF shared our take on what the NHS 10-Year Plan means for the Life Sciences sector. On the same day, the government published its Life Sciences Sector Plan. While some of the operational content was already outlined in the NHS plan, the sector plan adds important strategic narrative, explicitly positioning Life Sciences as central to the UK’s economic growth. It highlights investment commitments as well as a delivery timeline. 💡 The key challenge that remains unaddressed it the Voluntary Scheme for Branded Medicines Pricing and Access. At the ABPI annual conference, the Secretary of State committed to reviewing the VPAG scheme, which has grown from c.£600m to nearly £3.3 billion in annual payments. 📈 Read our snapshot summary below. ⬇️ Medicines and Healthcare products Regulatory Agency NICE - National Institute for Health and Care Excellence The Association of the British Pharmaceutical Industry (ABPI) HM Treasury The Rt. Hon. Wes Streeting MP Samantha Roberts Lawrence Tallon #LifeSciences #LifeSciencesSectorPlan #VPAG #NHS #HealthcareTransformation #IndustryPartnerships #DataDigitalAI #Prevention #PHM

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We’ve just launched our new single-interval ErlangC calculator. It’s a Python-powered tool, now live on the Atlantic Insight website. This is the first in a series of Python-based calculators and tools that we’re developing, related to planning and performance within operational environments. The ErlangC equation is a standard method for estimating how many advisors are needed to meet a given service level target, defined as answering a set percentage of calls within a set number of seconds. It’s most effective where all advisors can handle all calls, and is typically applied to each 15- or 30-minute interval. The formula comes from queueing theory, a branch of Operational Research. Try it here: 🔗 https://lnkd.in/e9qjmgCg

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