SOFIAH NICHOLE SALIVIO

News Editor

Scran has launched an iOS and Android cooking app for neurodivergent users. Developed in Edinburgh by founders with backgrounds in technology and accessibility, it aims to change how recipes are presented to reduce the mental effort involved in cooking.

Users can import recipes from websites, social media, photos and cookbooks. The app then restructures them into shorter, more explicit steps.

Its launch comes amid growing attention on digital products designed around accessibility needs rather than adapted later. The founders said common recipe formats can be hard to follow because preparation is often buried in later instructions, ingredient quantities are separated from method steps, and longer directions require repeated rereading.

Scran moves preparation tasks to the start of a recipe, places ingredient amounts within each step and breaks longer directions into smaller actions that users can tick off as they cook. It also includes serving-size adjustments, aisle-sorted shopping lists, measurement conversion, larger text, dark mode and dyslexia-friendly fonts.

Scran was created by Grant Macgregor and Jonny Kirkaldy, an Edinburgh-based couple who said they have spent 15 years designing digital products, including accessibility work for neurodivergent users. The app was built with input from home cooks, including people with ADHD, dyslexia and autism.

During a four-month pilot, 200 people tested the product and their feedback shaped how recipes are imported, simplified and followed. Scran said the app recorded a 30% week-four retention rate during the trial, while a voluntary follow-up survey found that 32 of 36 respondents said it made cooking from recipes easier. Of those, 19 said it was much easier and 13 said it was somewhat easier.

Inclusive Design

The founders say the central issue is cognitive load. Rather than offering a standard recipe database, the app focuses on changing the structure of recipe instructions so each task is clearer and easier to complete in sequence.

“Standard recipe formats assume everyone processes information the same way. We’ve heard from so many people who blame themselves when a recipe trips them up, when really it’s the recipe that’s failing them. We want to give people the confidence to cook whatever they want to,” said Jonny Kirkaldy, co-founder of Scran.

Scran uses artificial intelligence to import and simplify recipes, but the wider proposition rests on design rules created through research with neurodivergent cooks. The founders said the system is intended to create more consistent step-by-step instructions across different recipe sources.

The app enters a crowded market for meal planning and recipe tools, but it targets a narrower user group with a specific accessibility problem. That may help explain the pilot’s retention figures, which Scran cites as evidence of demand for a simpler way to follow recipes.

Consumer apps aimed at accessibility needs have often focused on reading, communication or mobility. Cooking has received less attention, even though recipe formats combine several common pain points at once, including dense text, task switching, sequencing and working memory demands.

One tester described the effect of restructuring steps into single actions.

“This app is life-changing. Having the steps broken down into single actions makes my life so much easier and less overwhelming. I want to enjoy cooking and the app does the hard bit for me!” said Sofia.

Commercial Model

Scran is self-funded and is launching with a subscription model after a free trial period. The service is priced at £29.99 a year or £4.99 a month, putting it in line with many paid productivity and lifestyle apps rather than free recipe platforms supported by advertising.

That pricing suggests the founders are betting users will pay for a tool that solves a practical problem rather than for access to recipes alone. The pilot’s retention and survey data will be watched as an early indicator of whether accessibility-led consumer software can sustain paid subscriptions in a competitive app market.

Macgregor said the project was built around a specific use case rather than broad claims about technology.

“For us, technology is at its best when it solves a specific, real-world accessibility issue. By combining inclusive design, a supportive app experience and ongoing community feedback, we’ve built something that can help more people stay on track, feel confident and enjoy cooking more,” said Grant Macgregor.

Jack Bristow, 23, from Sutton Courtenay, died after a tree fell on his truck while he was working in Hampshire.

He suffered a catastrophic head injury and was pronounced dead at the scene. He left behind his son, Harvey, who was one at the time.

Mr Bristow and driver Callum Smith had left Hooke Highways Limited’s Watlington depot at about 7.55am to remove traffic management equipment across the South East that could have been blown away in severe winds.

Jack Bristow , 23, died working in Storm Eunice in 2022 (Image: Unknown)

They were travelling back to Oxfordshire on Old Odiham Road when the accident happened at about 11.43am.

His parents, Teresa White and Gary Bristow, brought a claim against the company, arguing it had breached its duty of care by sending him to work during a Met Office Red Warning for extremely strong winds.

However, Judge Irena Sabic KC dismissed the claim, saying the risk of death while travelling as part of the assignment “was not reasonably foreseeable”.

She warned against “setting a novel standard of care” by which negligence could be established against an employer.

In her judgment, she said: “My view is that the precautions that the Claimants say should reasonably have been taken are not practicable or realistic. The risk of this horrific accident occurring was simply not foreseeable.”

Expressing sympathy for Mr Bristow’s relatives, the judge said he had been described as “hard working, caring, witty and completely devoted to his son”.

The family’s separate claim against landowner Davis Meisels, from whose land the tree fell, will be determined in due course.

AI adoption continues to accelerate across both public and private organisations. In healthcare, three-quarters of surveyed public-sector organisations are already exploring or implementing generative AI initiatives.

One of the most significant tests of AI at scale is now approaching, with NHS England announcing plans to provide Microsoft 365 Copilot to roughly half a million clinicians and support staff. This isn’t happening in a vacuum. More than a quarter of surveyed GPs are already using AI tools in their clinical practice, making broader adoption a logical next step. The goal across healthcare is to reduce administrative burdens, improve efficiency, and give staff more time to focus on patient care.

The real test is no longer whether organisations can deploy AI. It is whether their governance can keep pace once they do.

The readiness gap

The more difficult question, however, is whether the surrounding environment is ready. Sixty-eight per cent of surveyed physicians said the NHS lacks the digital infrastructure needed to introduce AI effectively. The concerns are not limited to the technology itself. Training gaps, interoperability issues, patient safety and data privacy are all important parts of the readiness picture.

At the scale at which public-sector organisations operate, AI adoption requires governance that can keep pace with both the technology and the environment around it. This must include clear policies for data access, retention, accountability and human oversight.

That gap is not unique to healthcare. Private-sector organisations face many of the same readiness questions, even when the regulatory context, systems and consequences differ. 

Why existing governance models need to evolve

Many governance practices still rely on periodic reviews, where changes to systems and data access are assessed at defined intervals. That made it easier to track risk, document decisions and respond as regulation evolved. Human decision-makers were also more visibly positioned at the centre of many workflows, making informed judgment calls. 

AI changes those operating conditions. AI systems can retrieve, combine and summarise information at a scale that makes interaction-by-interaction human review impractical. At the same time, many organisations are adopting multiple tools across operational and governance functions. Similar information may be accessed through several systems, making it harder to maintain consistent visibility into what was used, by which tool and for what purpose.

The nature of the modern workforce compounds the challenge. Today, staff join, leave and move between teams, while organisations are regularly reshaped through restructuring, mergers and acquisitions. The problem of access no longer matching someone’s role or legitimate business need is not new.  Introduce AI into that environment, and existing data sprawl and oversharing become easier to discover and more consequential.

The recurring mistakes

A few patterns repeatedly undermine otherwise well-intentioned governance efforts. Organisations often lack a clear inventory of what sensitive data exists, where it lives, who owns it and who can access it. In large, long-established organisations, where information may have accumulated across decades of systems and restructures, this picture is rarely as tidy as teams assume.  Deploying AI into an environment that has not been properly assessed can amplify operational and reputational risk. 

Governance is also frequently treated as a pre-launch checklist rather than a continuous operational function.  Teams may invest heavily in preparation, but real-world use can surface behaviours, use cases and risks that pre-launch testing could not fully anticipate.

Many organisations are also layering multiple specialised tools that do not communicate effectively with one another. An organisation might use one platform for administrative automation, another for back-office processes and a third for access governance. Each tool may perform its individual function adequately, but together they can create fragmented oversight, duplicated effort and additional sprawl that is difficult to contain. 

The confidence-reality gap

There is a striking gap between how ready organisations believe they are and what their operating environments are revealing. ShareGate’s 2026 Microsoft 365 AI Readiness Survey of IT and security leaders found that 93% of surveyed IT and security leaders were confident their Microsoft 365 governance framework could support AI responsibly. Yet 29% reported that AI tools had already surfaced sensitive internal information that they believed should not have been accessible. A further 8% were unsure whether this had occurred.

The types of data being surfaced are not abstract.  They include contracts, employee records, strategic plans and customer lists. In a healthcare setting, that could mean an employee receiving an answer grounded in sensitive information they were technically permitted to access but no longer had a legitimate reason to see. 

With Microsoft 365 Copilot, one common issue is not a broken security boundary but an existing permission model that no longer reflects legitimate business need. The real issue is that those permissions are often broader, older, or less deliberate than leadership assumes. Permissions designed for human search and manual discovery were not created with prompt-based retrieval in mind. Information that once required someone to know where to look can now be surfaced through a single prompt.

When governance tools are fragmented and oversight is inconsistent, oversharing becomes a recurring pattern rather than an isolated incident. Remediation becomes slow and costly. The distinction that matters here lies in whether governance is reactive or proactive.  Organisations that establish and continuously review the right controls before and after deployment can reduce the likelihood and impact of data exposure, while avoiding the cost of remediating problems after trust has already been affected.

Getting the foundations right

The NHS Copilot rollout will be a major test for workplace AI at scale in the UK. Its success will depend as much on the governance surrounding it as on the technology itself.

That means organisations need to move beyond surface-level readiness checks. Effective governance starts with a thorough understanding of the existing environment: what data exists, where it lives, who owns it, and who can access it.  It requires collaboration across IT, security, legal, compliance, data and operational teams, with clear accountability for the decisions each group owns. It also requires scrutiny of the broader toolset: whether platforms work together, support consistent oversight and reduce more complexity than they introduce.   

Good governance isn’t a brake on AI adoption; it’s what makes fast adoption sustainable. By identifying risk earlier rather than responding after an incident, organisations give AI deployments a better chance to deliver. Teams can focus on efficiency, service improvement and better employee experiences rather than spending their time correcting governance problems that AI has made easier to see.

The goal is not more governance for its own sake. It is the confidence to use AI responsibly at scale.