New Book Shows Businesses How to Navigate Technological Disruption

Discover how The Z-Curve by Alberto Torres helps business leaders understand AI, quantum computing and technological disruption to build long-term success.

New Book Explains How Businesses Can Stay Ahead of Technological Disruption

The pace of technological change is accelerating at an unprecedented rate, leaving many organisations struggling to keep up. 

From artificial intelligence and automation to quantum computing and genetic engineering, businesses are facing a rapidly evolving landscape that demands constant adaptation.

A new book, The Z-Curve: Navigating Your Business Through Technological Disruption, by business strategist Alberto Torres, aims to give leaders a practical framework for understanding how technological disruption unfolds and how organisations can position themselves for long-term success.

Today's businesses are operating in an environment where technological innovation is closely intertwined with global politics, competition for critical supply chains, and the race to control valuable data. As nations invest heavily in emerging technologies, companies are being forced to rethink how they compete, innovate and future-proof their operations.

The book introduces the concept of the "Z-Curve", a model designed to help organisations recognise the warning signs of disruption before it is too late. Rather than simply reacting to change, the framework encourages leaders to anticipate technological shifts and develop strategies that build resilience and sustainable growth.

History offers plenty of examples of companies that successfully embraced change, alongside those that failed to adapt. Technology giants including Apple, Amazon, Google and NVIDIA have transformed industries and achieved remarkable growth, while once-dominant household names such as Xerox, Nokia, AOL and Blockbuster have seen their influence fade as markets evolved.

According to Torres, the next wave of disruption is already underway. Advances in generative and agentic AI, quantum computing and biotechnology are expected to reshape industries even more dramatically over the coming decade.

Drawing on a series of real-world business case studies, The Z-Curve examines both successful transformations and costly failures, providing practical lessons for executives, managers and entrepreneurs seeking to navigate uncertainty with greater confidence.

Torres, a former senior executive and Partner at McKinsey & Company, is currently a member of Adobe's International Advisory Board and an Adjunct Professor at IE Business School. He believes technological disruption is no longer an occasional challenge but an ongoing reality for every organisation.

He said: "Technological disruption is no longer a distant threat; it is a constant reality for every organisation. Understanding how it works is the first step towards managing it successfully. The Z-Curve gives leaders a way to see disruption more clearly and act with confidence."

As technology continues to redefine the business world, books such as The Z-Curve provide valuable insights for leaders looking to remain competitive in an increasingly unpredictable future.

The Z-Curve: Navigating Your Business Through Technological Disruption is published by LID Publishing on 25 June 2026. Available in hardback (ISBN: 9781917391672), priced at £19.99.

For more about LID Publishing, please visit www.lidpublishing.com

You can buy the book at a discounted price here:- https://amzn.to/4xQOATn

KYND Named Among World’s Top AI FinTech Companies for Second Year Running

Cyber risk analytics provider KYND has been named in the AIFinTech100 2026 list, highlighting the growing importance of AI-powered cyber intelligence in insurance.

Artificial intelligence continues to reshape the financial services sector, but industry experts increasingly agree that success depends not just on powerful AI tools, but on the quality of the data that powers them.

That principle has helped cyber risk analytics specialist KYND secure a place in the prestigious AIFinTech100 list for the second consecutive year, reinforcing its growing reputation within the insurance sector.

Published annually by FinTech Global, the AIFinTech100 recognises the world's most innovative companies applying artificial intelligence across financial services. The 2026 edition was the most competitive yet, with more than 2,000 companies assessed by industry experts and analysts before the final 100 were selected.

For insurers, brokers and reinsurers, the recognition highlights the increasing importance of reliable cyber risk intelligence in an era where AI is transforming underwriting, portfolio management and risk assessment.

KYND provides insurers with a continuously updated view of an organisation's cyber risk exposure, helping decision-makers move beyond traditional point-in-time assessments and static risk scores. Its platform combines proprietary technology, cyber security expertise and ongoing exposure monitoring to provide insights into organisations ranging from global enterprises to small and medium-sized businesses.

According to KYND Co-founder Melanie Hayes, the award reflects the growing role of cyber intelligence within modern insurance operations.

She noted that while AI is helping insurers analyse risks more quickly and efficiently, the effectiveness of those systems ultimately depends on the quality and accuracy of the intelligence underpinning them. By delivering a real-world view of cyber exposure, KYND enables insurance professionals to make more informed decisions at both individual risk and portfolio levels.

The wider financial services industry is also evolving rapidly. Richard Sachar, CEO of FinTech Global, said the focus of AI adoption has shifted from experimentation to practical implementation. Financial institutions are increasingly looking for solutions that deliver measurable value across areas such as fraud prevention, customer experience, risk management, automation and insurance operations.

The recognition also comes at a time when AI itself is creating new challenges for insurers. From AI-assisted cyber attacks and faster vulnerability exploitation to emerging questions around AI liability, the risk landscape is becoming increasingly complex.

As insurers look to develop the next generation of cyber insurance products, companies such as KYND are helping the market gain greater visibility into emerging threats and opportunities. That intelligence could prove vital in supporting profitable and sustainable growth in the years ahead.

https://www.kynd.io/uk

AI Isn't Failing Businesses – Why UK Companies Need to Rethink AI ROI

AI Isn't Failing Businesses – Expectations Are.

Three-quarters of UK businesses now use AI, but only 31% report positive ROI. Discover why the issue may be expectations rather than the technology itself.

Artificial intelligence has rapidly become part of everyday business operations across the UK. From customer service chatbots to content creation tools, organisations of all sizes are embracing AI in the hope of boosting efficiency and profitability.

Yet new research suggests many businesses are struggling to see the returns they expected.

A survey of 500 senior decision-makers conducted by Studio Graphene found that over three-quarters of UK businesses are now using AI tools. 

However, only 31% reported seeing a positive return on investment, while fewer than half could clearly define what success from AI would actually look like.

At first glance, those figures might suggest AI is underperforming. But some industry experts believe the real problem lies elsewhere.

According to Angus Hay, CEO and Founder of Edinburgh-based AI agency Vereus, businesses may simply be measuring the wrong things.

Many organisations adopt AI with the expectation that it will directly increase sales, win new customers or generate additional revenue. While AI can certainly support these goals, Hay argues that its greatest value often comes from something far less glamorous: removing time-consuming administrative tasks from employees' workloads.

In many businesses, highly skilled professionals spend significant portions of their week on reporting, compliance, research, data gathering and other repetitive tasks. While necessary, these activities rarely generate revenue directly.

This is where AI can make a genuine difference.

Rather than replacing people, AI can automate many of these routine processes, freeing employees to focus on work that creates real value. More time can be spent serving customers, developing products, building relationships and driving growth.

Vereus has seen this approach deliver impressive results. One investment firm reportedly reduced a six-day intelligence-gathering process to less than two minutes. A rental business reclaimed nearly two weeks of manual reporting time during each reporting cycle, while a telecommunications company cut expansion costs by over £30,000 per month.

In each case, AI wasn't generating income directly. Instead, it was creating additional capacity for people to perform at their best.

Interestingly, separate research from KPMG suggests that 65% of UK businesses plan to continue investing in AI regardless of whether they can currently demonstrate a clear return on investment.

That may be because many business leaders instinctively recognise AI's potential, even if traditional ROI measurements fail to capture its true value.

Perhaps the most important question businesses should ask isn't "What will AI earn?" but rather "What could our people achieve if AI gave them more time to do what they do best?"

https://www.vereus.co.uk

UK employers can now get five AI qualifications for their staff through a single apprenticeship at no cost

A growing number of UK employers are discovering they can upskill their entire workforce in AI and automation without spending a penny, thanks to a Level 4 apprenticeship that bundles up to five industry qualifications into a single programme, all fully funded through the Apprenticeship Levy.

TESS Group, an Ofsted Good training provider working with employers including the Financial Times, EDF Energy, Transport for London, DPD, and NHS England, has built its AI & Automation Practitioner programme around a simple idea: AI skills shouldn't be limited to IT departments, and employers shouldn't have to pay extra for professional certifications.

The 15 to 18 month programme is designed for employees at every level, from front-line staff to senior leaders. There's no coding required. Learners work with tools like Microsoft Copilot, ChatGPT, and Power Automate to build practical AI and automation skills they can apply to their day job from month one. 

It's equally suited to a team leader looking to make better use of AI across their department as it is to someone in an operational or support role. For organisations that run on Google Workspace rather than Microsoft, the programme can be tailored to Google's AI tools instead.

What makes it unusual is what's included. Where most training providers deliver only the apprenticeship standard, TESS Group embeds up to five additional qualifications at no extra cost: Microsoft AI Business Professional (AB-730), NCFE Level 3 certificates in Cyber Security and Data, NCFE AI Prompt Engineering, and BCS certification. Employers don't pay for any of it. 

The full programme is funded at £18,000 through the Apprenticeship Levy for levy-paying employers, with 95% government funding available for smaller businesses.

"Most employers have no idea they can get this many qualifications wrapped into one apprenticeship," Rod Doyle, Director and Founder of TESS Group told That's Business.

"They're already paying the levy. This is money they've already spent sitting in a pot that expires after 24 months if they don't use it. 

"We're helping them turn that into real capability across their teams, not just in IT, but in HR, finance, operations, marketing, every department. And it works just as well for leaders as it does for the people they manage."

The business case is straightforward. Learners typically reclaim over 10 hours a week by automating repetitive workflows, and because they're applying what they learn directly to their role throughout the programme, the return on investment starts immediately rather than after graduation.

All sessions are delivered live online via Zoom or Microsoft Teams, with new cohorts starting every month. Employers can enrol a single learner or run a private cohort tailored to their sector and challenges. For organisations wanting dedicated AI leadership development, TESS Group also offers specialist pathways for people leaders, operations managers, and coaching professionals, each with CMI management qualifications built in.

TESS Group currently delivers to over 100 employers across England and has trained over 10,000 learners, with a 59% distinction rate against a national average of around 20%. The provider holds a 97% employer satisfaction rate and a 4.9 out of 5 rating from over 680 verified reviews on Google and Trustpilot.

Employers interested in using their Apprenticeship Levy for AI training can book a free 15-minute discovery call at www.thetessgroup.com or contact info@thetessgroup.com.

Brazilian Startup Merges 19th-Century Study Method with AI to Transform How Students Learn

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Card.AI turns any content into AI-generated flashcards in seconds, democratising a technique long used by medical students and top performers

While AI dominates headlines with promises to transform education, a Brazilian startup decided to look to the past before building the future.

Card.AI combines a nearly 200-year-old memorisation technique, flashcards, with generative AI to create a tool that could change how millions of students study.

Flashcards originated in the 19th century. In 1834, English educator Favell Lee Mortimer published "Reading Disentangled," considered the first documented use of memorisation cards in education. 

The technique has since been scientifically validated: in 1885, German psychologist Hermann Ebbinghaus demonstrated through the "forgetting curve" that spaced repetition can dramatically increase long-term memory retention.

The problem? Creating quality flashcards has always been time-consuming. Students spent hours turning notes into cards — time that could have been spent actually studying.

"We eliminated that barrier," explains the Card.AI team to That's Business. "Students paste their lecture notes, articles, or PDFs into our platform, and within seconds our AI identifies key concepts and automatically generates flashcards. What used to take an hour now takes seconds."

"Card.AI completely changed how I study Biology. I no longer waste time printing or transcribing cards — I just create, save, and review them on my phone before class. Thanks to the platform, my GPA rose to 3.8 this semester." Beatriz, 21, Biology student, Federal University of Rio de Janeiro

How It Works

The platform operates in three simple steps: users input their content (notes, articles, or PDFs), the AI processes and generates flashcards, and students review through a gamified system that automatically schedules future reviews based on individual performance.

The key differentiator is the application of spaced repetition, developed by Sebastian Leitner in 1972. The system identifies which content students got wrong and schedules more frequent reviews for those topics, while spacing out reviews for mastered material. It's the same logic used by apps like Anki — popular among medical students — but with the convenience of AI-powered automatic generation.

Card.AI offers a free version with basic features and a Premium plan for full access. The platform is available via web (mobile-responsive) and native iOS and Android apps, with automatic cross-device sync.

The Global EdTech Market

The global EdTech market is projected to reach $404 billion by 2025, according to HolonIQ. Brazil alone has over 47 million students in primary, secondary, and higher education, representing a massive underserved market. The demand for personalized, accessible study tools has never been higher — especially among students preparing for standardized tests and professional certifications.

Try it free: www.cardai.app

Available on: Web, App Store, and Google Play

Card.AI is a Brazilian EdTech startup using artificial intelligence to transform any content into intelligent flashcards. Founded in 2024, the company's mission is to democratize high-performance study techniques, making them accessible to students worldwide. The platform combines the science of spaced repetition, validated by nearly 140 years of cognitive psychology research, with state-of-the-art generative AI.

Data skills are now ‘fundamental’ in the age of AI, says expert

Caroline Carruthers

A more concerted approach to improving data literacy is urgently needed to unlock the full growth potential of artificial intelligence (AI), claims data consultancy Carruthers and Jackson.

As AI tools become embedded across everyday business functions, data and AI literacy can no longer be viewed as niche skills reserved for specialist roles. 

From HR and finance to marketing and operations, employees are increasingly expected to interpret data outputs, question automated recommendations, and make informed decisions based on AI-driven insights.

For businesses, the challenge is becoming more urgent. While AI adoption continues to accelerate, workforce capability is not keeping pace.

In Carruthers and Jackson’s annual Data Maturity Index, a survey of senior data leaders, 40% reported that AI is being used by a high number of employees across their organisation or within specific departments, up from 21% in 2024.

Yet 58% said most of their employees are not data literate, with a further 3% reporting almost no employees in their organisation are data literate.

Carruthers and Jackson, one of the leading global data consultancies and part of the Praesto Group, warns that this disconnect threatens to undermine global AI ambitions. Significant investment is being channelled into new platforms, infrastructure and tooling - but technology alone cannot deliver transformation.

Caroline Carruthers, Co-Founder and Chief Executive of Carruthers and Jackson told That's Business: “Artificial intelligence is now embedded in everyday workflows, and data literacy can no longer be confined to technical specialists.

"It's becoming a core business capability every bit as fundamental as financial literacy or digital skills.”

“All the AI investment in the world will count for little if our people cannot question outputs, challenge assumptions or translate insight into action. If an organisation, or a country, fails to act now in improving data literacy they will fall behind and the highest-value, data-enabled roles will simply be outsourced elsewhere.”

Ultimate responsibility for a shift in approach to data literacy, however, remains unclear.

While financial literacy has undergone a step change in recent decades with increased financial education in schools and a proactive role played by the banking sector, there is no single commercial player in the AI and data space with the same incentive to champion broad data literacy beyond product-specific training. 

As a result, says Carruthers, the burden must be shared. The education system needs to embed data literacy earlier, employers must invest in upskilling their current workforce, and governments must provide consistent and clear guidance to enable both.

Caroline went on to say: “We are entering what I call the ‘second coming’ of data. Where many board-level conversations about data were centred on compliance, now the questions are more strategic and purpose-driven. 

"Organisations are increasingly asking where they want to go with their data, why, and for whom. The answers require not just technical capability, but a workforce confident in interrogating and applying data responsibly.”

“We’re already in the next industrial revolution, and the real question is how we intend to bring people on board with it. For modern workplaces it might mean reframing their thinking towards the younger generation. They’re often digitally native, highly curious, and adaptable - ideal characteristics for a data-savvy employee. Smart, imaginative organisations are going to embrace that skillset and reap the benefits of effective data and AI.”

https://carruthersandjackson.com

Resilient Organisations Practise Until Response Becomes Routine, Says Horizon3.ai

Dan Bird MBE, Field Chief Technology Officer (EMEA) at Horizon3.ai, highlights why cyber resilience must move beyond policy and toward continuous operational rehearsal, reinforcing insights from co-founder and CEO Snehal Antani.

Cyber resilience is often framed as a new challenge driven by modern threats, yet many of its core lessons were already solved decades ago in disaster recovery. 

According to Dan Bird MBE, Field Chief Technology Officer (EMEA) at cybersecurity company Horizon3.ai, resilient organisations succeed not through static defence models but through continuous rehearsal and validation, an approach strongly echoed by Horizon3.ai co-founder and CEO Snehal Antani.

Bird points to a growing gap between policy and real-world readiness. Horizon3.ai, regarded as one of the leading providers of offensive security, promotes an approach in which organisations test their own IT environments through continuous penetration testing to uncover potential weaknesses that cybercriminals could exploit. Rather than solely relying on passive layers of defence, organisations can safely hack themselves, fix their issues, validate their fixes, and repeat the process as often as they like.

Parallels between cyber resilience and high availability systems

Drawing on Antani’s comparison between cyber resilience and business continuity, Bird emphasises that resilience is not a theoretical concept but an operational discipline. In high availability environments, downtime is not acceptable and failures are anticipated rather than avoided. 

Systems are intentionally failed over from one data centre to another to prove recovery works, building the kind of repetition and accountability that creates “muscle memory” within teams.

This model of continuous rehearsal for real events aligns closely with offensive security principles and reflects what both Antani and Bird see as the most effective modern response to escalating cyber threats. Instead of viewing cyber resilience as a tooling or reporting exercise, Horizon3.ai argues that organisations must treat it as an operational challenge: systems fail, attackers exploit weaknesses, and businesses must recover under pressure. “Customers expect availability, and regulators demand accountability,” Bird told That's Business. 

Resilient organisations practise until action becomes routine

Bird reinforces Antani’s view that resilient organisations should assume something will go wrong and actively look for weak points before attackers do. “Resilience means rehearsing response and recovery until execution becomes routine, whereas many organisations still rely on assumptions,” he explained. “Defence and recovery plans may appear strong on paper but often fail in practice when testing is missing.”

In real incidents, he notes, operational failures and malicious activity can appear indistinguishable at first. Service restoration cannot wait for perfect attribution. 

Disaster recovery and cybersecurity converge, requiring teams that have rehearsed together rather than siloed plans that have never been exercised under pressure. The challenge, Antani has argued, is rarely tooling alone but often process and leadership.

One penetration test a year is far too little

Both leaders highlight the limits of traditional annual penetration testing in environments that change constantly. 

Risks evolve faster than yearly cycles can capture, with patches arriving weekly, configurations shifting and cloud and identity architectures continuously developing. Without regular security validation, organisations risk making decisions based on outdated assumptions.

Bird emphasises that continuous testing tied closely to change creates the feedback loops organisations need. Regular pentests following patch cycles help teams validate whether improvements actually reduce risk, moving security toward continuous improvement rather than periodic assessment.

A new phase of cybersecurity driven by artificial intelligence

As Antani has observed, cybersecurity has entered a phase where speed matters more than ever. AI-driven attacks compress timelines, meaning organisations must rely on trained muscle memory rather than reactive decision-making. 

From Bird’s perspective, the lesson is clear: “Under pressure, teams fall back on training, not expectations. Continuous rehearsal, combined with leadership commitment, determines performance.”

https://horizon3.ai

Follow them here:-

http://www.linkedin.com/company/horizon3ai

https://x.com/Horizon3ai

Synpulse appoints Marcel Loetscher as Senior Partner and Joel Smith as Partner, strengthening global leadership in Commercial Insurance and Reinsurance

Marcel Loetscher 

Synpulse, a leading financial service consultancy has announced the promotion of Marcel Loetscher to Senior Partner and Joel Smith to Partner within its Commercial Insurance, Reinsurance, ILS and Delegated Authority (CRID) practice. 

These appointments come at a time where insurers and reinsurers are reassessing their operating models due to mounting cost pressure and the demand for efficiency grows. At the same time, rapid advances in technology - especially agentic AI - are creating new opportunities to transform underwriting, claims, and finance.

Marcel Loetscher promoted to Senior Partner

Marcel Loetscher has been with Synpulse for nearly 16 years and is based in New York. He played a key role in launching Synpulse’s U.S. operation by opening its New York office in 2012, and now leads the firm’s activities across North America. 

Marcel also heads Synpulse’s global commercial insurance and reinsurance practice, steering strategy and delivery across the U.S., Canada and global markets.

Marcel helps clients navigate an increasingly complex risk landscape shaped by climate volatility, wildfires and social inflation. He uses agentic AI to create practical and measurable impact so clients can modernize their operations and move from reacting to disruption to guiding it.

Marcel told That's Business: “I'm honoured to step into this role alongside an exceptional team at a pivotal moment for our clients. 

!Together we will harness agentic AI and deep industry expertise to build stronger, future-ready insurance and reinsurance operations.”

Joel Smith promoted to Partner

Joel Smith

Joel Smith, based in Zurich, has been appointed partner after nearly ten years at Synpulse. Over the course of his career, Joel has supported major reinsurers and ILS fund managers through significant operational and technology transformations and has played an important role in shaping CRID’s global capabilities.

Across the market, companies are reassessing their operating models for strategic, market cycle, and technology driven reasons. 

This is increasing the focus on efficiency, productivity, and new ways to deliver sustained value to cedents and investors. At the same time, recent advances in Generative AI are creating entirely new possibilities for how core processes can be executed.

For the industry, this represents one of the most exciting shifts in decades. Commenting on his appointment, Joel said: “I am incredibly grateful for this opportunity. This is a fascinating time to be working within the space of reinsurance and transformation, with technology and evolving operating models creating entirely new possibilities for our clients. I look forward to continuing to build capabilities with our teams, supporting our clients as they make their operations fit-for-future, and contributing to Synpulse’s growth story in this anniversary year.”

Konrad Niggli, CEO and Managing Partner of Synpulse, commented: “These promotions reflect the strength of our people and the ambition that defines Synpulse. Marcel and Joel have shown exceptional leadership in helping our clients navigate an industry undergoing profound change, while advancing the capabilities and culture that set Synpulse apart. Their appointments reinforce our commitment to combining deep industry expertise with the potential of agentic AI to help commercial insurers and reinsurers to build the future with confidence.”

For further information visit www.synpulse.com.

TPP Signals AI Breakthrough — Wealth Management’s Comfort Zone About to Be Shattered?

The wealth management industry has enjoyed decades of comfortable margins, predictable fee structures and very little genuine disruption.

That comfort may be ending.

High-performance investment platform TPP, already known for benchmark-beating returns and an increasingly vocal investor base is preparing what could be the most significant shake-up the sector has seen in years.

Sources close to the firm confirm an AI-powered investment engine, internally developed and stress-tested over the past nine months, is nearing potential release.

And if early data proves sustainable, incumbents should be paying attention.

A Platform That’s Already Been Stealing Share

TPP is not a start-up chasing headlines.

Over the past 5½ years, the firm has quietly built a track record most traditional houses would struggle to match:

31.2% net of fees in 2025

20%+ annualised returns over five years

High retention

Strong organic referrals

Consistent monthly growth

While legacy firms defend fee structures and navigate regulatory drag, TPP has positioned itself as a performance-first alternative.

Investors have noticed.

Now, the firm appears ready to accelerate.

“We Think This Industry Needs a Shock”

Co-founder Lane Clark does not hide his view of the current landscape. Lane Clark told That's Business: “Let’s be honest. The traditional wealth model hasn’t meaningfully evolved in decades. Layered fees. Underwhelming performance. Defensive narratives. Investors deserve better.”

Clark confirms that TPP has been building three AI-driven investment strategies behind closed doors.

“We didn’t want hype. We wanted data. For nine months we’ve been tracking live performance. The numbers have been exceptional 28% average return, 93% accuracy on positioning decisions, zero down months, and a maximum drawdown of just 6%.”

If validated through extended live testing, those metrics would place the system among the most compelling risk-adjusted strategies currently available to retail and high-net-worth investors.

Clark continued: “We won’t release anything unless it meets our standard. But what we’re seeing is potentially transformational.”

Not Another Robo Adviser

According to insiders, this is not a passive allocation model dressed up with AI branding.

AI Investor (powered by TPP) is described as:

A risk-aware decision engine

Adaptive but not reactive

Systematic without being blindly automated

Designed to step back when conditions deteriorate

Designed to press when opportunity improves

It does not chase noise.

It does not panic.

It does not anchor to ego.

In Clark’s words: “This isn’t artificial intelligence for marketing purposes. It’s applied discipline. It behaves differently to our core strategies, but early signs suggest it may rival them.”

If AI Can Move Tech Stocks, What Happens When It Hits Wealth Management?

Recent AI-related announcements have moved entire sectors, software, legal, media, professional services, often on speculation alone.

Wealth management has remained relatively insulated.

But that insulation may be fragile.

If a high-performance platform with an existing loyal investor base introduces a credible AI execution framework with measurable results, fee-heavy incumbents may face uncomfortable comparisons.

Industry observers note that TPP already commands strong word-of-mouth advocacy among performance-focused investors.

Any expansion could amplify that momentum.

“Get Ready.”

Clark’s closing message to the market is direct: “We didn’t build TPP to blend in. We built it to outperform and to challenge an outdated model. If this product does what we believe it can do, it won’t be incremental. It will be meaningful.”

To existing clients: “You’ve been asking for another allocation opportunity. We hear you. Hold tight.”

A Cult Following, And a Ready Market

TPP has built what some describe as a “cult-like” following among performance-driven investors disillusioned with high-fee advisory models.

Client referrals reportedly account for a significant portion of new inflows, and demand for additional allocation capacity has grown steadily.

Clark acknowledges the anticipation: “Our existing clients have been asking when we’ll release another product. We’ve been very selective. But we’re close. 

"We just need to decide whether to launch these AI strategies on our platform or as a standalone product. TPP is already changing the game. The AI investment strategies look every bit as good as our platform, but they're very very different. I'm buzzing just thinking about how we can launch these when the timing is right.”

Further details are expected soon.

For incumbents, the question may not be if change is coming, but how quickly they will need to respond.

For further information on TPP please visit their website on www.tppglobal.io

Swiss AI Academy Launches Framework to Keep Humans in Charge as AI Scales

Swiss AI Academy has launched the Bionic Context Protocol (BCP), a 14-principle framework designed to prevent the erosion of human judgment and capability as artificial intelligence spreads across societies worldwide. 

The announcement was made during an independent auxiliary unDAVOS event alongside the World Economic Forum Annual Meeting.

"How you use AI matters as much as whether you use AI," Shaje Ganny, (PICTURED) Co-Founder of Swiss AI Academy, told That's Business. 

"When people passively accept AI outputs, capabilities degrade. When AI is designed to keep humans thinking and challenging, capabilities strengthen."

The problem: adoption outpacing safeguards

AI adoption is moving faster than governance and human capability safeguards. A 2025 MIT Media Lab study found that people who relied on AI writing tools showed weaker brain connectivity and struggled to recall their own work, a phenomenon researchers termed "cognitive debt." Current responses remain siloed: researchers study the problem, ethicists debate principles, organizations develop internal policies. BCP unifies this work into a coherent protocol that moves from discussion to implementation

The evidence: four decades of automation research

BCP is built on research into automation bias, skill decay, and human-machine interaction from safety-critical industries including aviation and healthcare. Studies from these fields show that when humans become passive observers of automated systems, their ability to intervene during failures declines.

The framework: three levels of protection

The protocol operates at three levels: individual, protecting personal agency and independent thinking; organisational, ensuring human needs are not subordinated to efficiency metrics; and societal, preserving the capacity of communities to shape their collective future.

The distinction: evolution versus erosion

BCP distinguishes between capability evolution, where societies intentionally choose which skills to develop or retire, and capability erosion, where skills disappear as an untracked side effect of systems optimized for speed or cost.

The call: global recruitment for five workstreams

The framework is released as version 0.6, a consultation draft intended to be completed through public contribution. Swiss AI Academy is recruiting workstream leaders and contributors across five areas: governance architecture, evidence synthesis, implementation tools, measurement systems, and sector-specific applications.

"A small group cannot carry this alone," Ganny said. "We need researchers, practitioners, educators, and policymakers who understand what is at stake."

The full framework and contributor registration are available at bcporg.info.

The AI Skills That Will Define Business Success in 2026

Image courtesy QA

As businesses prepare for a decisive shift from AI experimentation to scaled adoption, tech training specialist QA has outlined the AI skills it believes will define business success in 2026, based on insights from Dr Vicky Crockett, Portfolio Director for Artificial Intelligence.

Why skills will define the next stage of AI adoption

As AI becomes increasingly embedded across everyday business tools, QA’s insights highlight the AI skills that business leaders will need to invest in most. 

This includes a shift from broad AI experimentation, towards deeper technical adoption and practical, human-centric capability.

Dr Vicky Crockett, QA’s Portfolio Director for Artificial Intelligence, told That's Business: “AI transformation isn’t optional in 2026, it’s the new competitive edge. As we head into 2026, we can increasingly see AI becoming the interface that we use to work with all other software.

As this develops further and AI pilot programmes move to scale, more people will need basic AI literacy and prompting proficiency training, and technical teams will need to train and manage AI systems at scale.”

QA’s insight suggests that the next phase of AI maturity will be defined less by technology and more by how effectively people work with it across every role, from frontline employees to senior leaders and technical teams.

Essential skills for employees, leaders and tech teams

QA’s insights break down the most in-demand skills across different teams. Here are some of the most important skills highlighted:

AI for all employees

Prompt engineering – Learning how to prompt effectively will become an essential skill

Critical and creative thinking with AI – AI may save us time, but learning to quality check and edit outputs will be key

Data awareness – It will be everyone’s role to understand how to use AI safely and securely.

AI for leaders

Strategic AI adoption – Leaders will need to understand how to scale AI in the most effective way.

Change management – AI transformation means change at every level and teams will need to balance resistance to change and adoption at scale.

AI ROI analysis – Change will happen at pace and it will be crucial to understand the investment and return data.

AI for tech teams

Building complex agentic AI systems – Tech teams will be required to build increasingly complex agentic AI systems.

Integration of apps within LLMs – 2026 will see apps arrive within LLMs and tech teams will need to capitalise.

Advanced prompting – Businesses will need to take prompting further, with sophisticated workflows and multi-step prompts.

The role of data and governance

As well as these key technical skills, it will be vital for businesses to pay attention to AI governance and compliance in 2026. Compliance frameworks will continue to evolve, and businesses must be ready to face this challenge.

“Most organisations will be creating AI governance structures if they haven’t already. This is likely to mean detailed AI training for leadership, governance and security teams, as well as mandatory AI literacy training for all staff so that they understand how to work with AI within their organisation’s policies.”

QA’s central message is clear: AI will not replace jobs, but it will fundamentally change how work is done. Organisations that invest early in the right AI skills will be better placed to improve productivity, manage risk, and build trust in AI-powered decision-making.

*Terms and conditions apply

Get skills ready for 2026 by exploring QA’s Most In-Demand AI Skills of 2026 and Dr Vicky Crockett's AI Predictions for the Year Ahead. 

https://www.qa.com/

Just a third of UK tech scale-ups boast AI expertise on their boards – lagging behind FTSE 350 tech giants

UK tech scale-ups are risking failure to meet their growth potential by not appointing artificial intelligence (AI) experts to their boards, according to new research from global growth consultancy, Think & Grow.

Just a third (32%) of the UK’s fastest-growing technology scale-ups boast AI expertise on their boards compared to four in ten (40%) of the largest tech companies on the FTSE 350 index.

The findings highlight a trend between AI expertise and revenue - FTSE 350 tech companies with AI expertise on their boards generate an average revenue of £6.8 billion, dwarfing an average of £953 million for those companies without AI knowledge.

Similarly, half (50%) of UK tech scale-ups with annual revenue greater than £50 million boast AI expertise on their boards compared to just 15% of companies with revenue below that level.

Efforts to appoint AI expertise to boards has increased in recent years as companies look to upskill their boards and leverage growth opportunities. The research findings show that the average tenure of board directors with AI expertise is three years, compared to five years across all board directors.

Additional research from Think & Grow reveals a third (32%) of technology companies plan to appoint individuals with AI expertise to their boards in the next 12 months. Surprisingly, given the clear investor appetite for AI - UK AI companies secured £2.9 billion in private investment in 2024* - 13% of technology companies have no plans to appoint AI expertise to their boards in the next year.

Figures from the Department for Science, Innovation and Technology and HM Treasury indicate that UK AI companies secured £2.9 billion in private investment in 2024, with average deals worth £5.9 million – with companies in the AI industry contributing £11.8 billion to the UK.

Think & Grow explains that AI expertise is becoming increasingly sought-after as growth companies compete for funding and market share as they look to scale at pace.

Jonathan Jeffries, Co-Founder of Think & Grow, told That's Business: “Companies without AI expertise on their boards risk losing ground to competitors and stifling growth.

“It’s a challenging climate for many sectors but there is huge investor appetite for high-growth tech companies, the issue is many of those who secure funding are unable to maximise the opportunity to propel growth as they lack key expertise on their boards. 

"AI is transforming business and society, ambitious tech companies won’t fulfil their potential if they don’t embrace it.

“The most successful companies weaponise their board connections and expertise to gain a competitive edge – UK tech companies need to ensure that they are building boards that are capable of overcoming upcoming challenges and leveraging commercial opportunities if they’re going to scale effectively.”

These research findings are part of an upcoming report from Think & Grow titled Breaking & Remaking the Next Generation of High Impact Boards which will be published in November.

Think & Grow is a global growth consultancy built to support high-growth tech companies through today's unpredictable and competitive market realities. Over 11 years, it has supported some of tech's most innovative companies, including the likes of Spotify, Stripe, Square, Dropbox, Peloton, Datadog, Canva and Etsy, to successfully scale their businesses and solve unique growth challenges.

https://www.thinkandgrowinc.com

The AI approval paradox: Why 2/3 of approved tools fail employees

Artificial Intelligence (AI) has moved from experimental to essential in a record time. 

According to McKinsey's 2025 State of AI report, 78% of organisations now use AI in at least one business function, while their use of generative AI has increased to 71%.

However, widespread adoption masks failure in execution. According to the latest Cybernews survey, whilst 52% of employers have approved or provided AI tools for their workforce, only 33% of employees using those approved tools say they fully meet their work needs.

The result? Governance structures are failing to prevent the very vulnerabilities they were designed to address. 

Despite widespread awareness of the dangers, 59% of employees use unapproved AI tools, and 75% of them share sensitive company and customer data with these unauthorised applications, creating the very security breaches leadership aims to avoid.

Žilvinas Girėnas, head of product at nexos.ai, a secure all-in-one AI platform for enterprises, explains why the approval paradox continues despite widespread awareness of security risks.

“This isn't a user problem but a procurement and implementation crisis. We're approving AI tools on promises and checklists, not on how well they fit work practices. Insufficient tools lead employees to bypass approval, risking customer data on unknown platforms,” he told That's Business.

The rise of “shadow AI”: A conflict between productivity and risk

The failure of approved tools is creating a dangerous trend known as “shadow AI.” This phenomenon describes employees using unauthorised software and platforms to get their work done, creating a massive blind spot for IT and security leaders. 

This practice results in a clash between the company's need for security and control versus the employee's interest in productivity and efficiency. 

Employees are rarely acting with malicious intent. They are simply seeking the convenience, speed, and features of AI tools that actually allow them to do their jobs better and faster.

Leadership then faces a dilemma, to either block the use of unapproved tools and risk losing a critical productivity edge or to permit their use and lose control over the company's most sensitive data. 

The potential risks are tangible. When employees use unapproved tools, 75% admit to sharing potentially sensitive information, including customer data, internal documents, financial records, and proprietary code. 

Once this data enters an unapproved AI platform, companies lose control over what happens to it. While these platforms have privacy policies, most employees never read them, and the policies themselves often permit data to be stored, used for model training, or even exposed to other users.

This leakage of intellectual property and confidential information increases a company's vulnerability to costly data breaches, a risk that can increase breach costs by an average of $670K, according to IBM. 

This entire shadow ecosystem often thrives in a corporate "gray zone," where official policies are either absent or quietly ignored by managers who also want their teams to perform.

“The gray zone exists because having a policy on paper doesn’t mean it is an effective one. Many organisations implement AI policies with just a simple ‘I acknowledge’ checkbox, without providing training, approved tools that work, or ongoing communication on how to apply the rules practically. 

"When employees don't understand the policy or lack real alternatives, they make their own decisions. That's when sensitive data starts flowing onto platforms the company hasn't vetted. A policy is only as effective as the training, tools, and feedback systems that support it,” says Girėnas.

Why sanctioned AI tools miss the mark

The disconnect between high adoption rates and low employee satisfaction is the direct result of a flawed, top-down implementation strategy common in many organisations. The problem often isn’t the technology itself, but an absence of planning and user involvement.

Leaders, rushing to participate in the AI boom, frequently make procurement decisions in a vacuum, choosing tools based on vendor promises or security checklists without a clear understanding of their teams' day-to-day workflows. 

This can lead to “innovation theatre,” where companies adopt AI tools superficially to signal modernity but fail to integrate them into the business. When employees are not involved in the selection process, the result is predictable. a sanctioned tool that doesn't solve their problems.

This failure typically manifests in three critical areas:

Limited functionality. Companies approve generic, one-size-fits-all tools without understanding what different teams actually need, pushing employees toward unapproved alternatives built for their specific work.

Poor workflow integration. Approved enterprise tools are often standalone applications, disconnected from the daily systems where employees work.

Lack of training and clear guidance. Many organisations have a significant guidance gap, leaving employees without adequate training or support from leadership.

“The problem is that companies are treating AI adoption as a finish line. They buy a platform, check a box, and celebrate their ‘innovation,' but they skip the real work of changing processes and ensuring the tool actually solves a real-world problem for their employees. Employees aren't rejecting AI — they're rejecting a solution that was thrown over the wall at them without any thought. They’re left with a tool that feels more like a burden than a benefit,” says Girėnas.

Practical steps forward

According to Girėnas, solving the approval paradox requires a shift away from a top-down, tool-first mindset. He outlines four non-negotiables for organisations to build a secure and productive AI ecosystem.

Map employee workflows before selecting tools. Analyse daily workflows for different roles, from marketing to engineering, and identify friction points, data needs, and gaps. Use this map as a blueprint for selecting tools that solve real problems and drive meaningful adoption.

Offer a secure sandbox, not just restrictions. Creating a sanctioned alternative within a controlled environment provides access to powerful AI models with guardrails and audit trails. 

This meets employees' need for advanced tools while giving organisations control, turning a security risk into a managed asset.

Implement a “living” policy with ongoing feedback. Treat policies as living documents, not static ones. Create simple channels for employees to give feedback on tools and rules. This helps update policies to stay current, preventing them from becoming irrelevant as employees set their own rules.

Identify internal AI champions. Identify employees who are already seeing success with AI tools and create structured opportunities for them to showcase their workflows, share tangible results, and demonstrate the real-world changes they've achieved.

This transforms AI adoption from a top-down process into a peer-driven one, where employees learn from colleagues who speak their language and understand their specific challenges.

Roffey Park Institute to Host Virtual AI Conference Exploring the Human Future of Work

As AI reshapes our world, the real question isn’t what technology can do – it’s how leaders can or should respond.

Roffey Park Institute, an internationally renowned centre for executive education and organisational development, will host its Virtual AI Conference 2025 on 18th June. 

It's described as a timely gathering that tackles one of the most urgent questions facing global workplaces today: how can we shape a human-centred future of work in the age of artificial intelligence?

With AI reshaping economies, leadership, and culture at pace, the conference brings together world-class thinkers, executives, and practitioners for a day of bold ideas, live debate and practical insight. From the implications of AI for leadership and ethics, to its role in enhancing wellbeing, performance and equity at work, the event aims to spark dialogue and action across industries and borders.

Conference Highlights Include:

New Research: Exclusive findings from Roffey Park’s latest international study on AI’s impact on leadership, decision-making and organisational culture.

Keynote Speakers: Provocative sessions from thought leaders at the forefront of AI, sustainability, psychology and systems thinking.

Panel Discussions: Real-world perspectives from diverse global organisations using AI to tackle climate change, manage complex geopolitics and build inclusive workplaces.

Interactive Labs: Hands-on virtual spaces for leaders to explore responsible AI use in talent management, hybrid collaboration and strategic foresight.

“As AI accelerates disruption, leadership must adapt not only technologically, but also emotionally and ethically,” Dr Arlene Egan, CEO of Roffey Park Institute told That's Business. “This conference is about navigating complexity with humanity. We’ll explore not just how AI can work for us, but how it can uplift people and communities when used wisely.”

A Global Conversation with Local Impact

The event will feature case studies from Asia-Pacific to Europe, highlighting how organisations are embedding AI in ways that reflect local challenges and values. Delegates will hear directly from leaders transforming education access in rural areas, improving healthcare systems using predictive data, and navigating workforce reskilling in sectors hardest hit by automation.

Who Should Attend?

This event is open to senior leaders, HR professionals, change agents, policy makers, educators, and anyone passionate about the responsible, human-centred integration of AI in the workplace.

Register Today at https://www.roffeypark.com/ai 

Harnessing Responsible AI: Transforming Recruitment Practices

In today's digital age, the integration of technology has revolutionised various aspects of our lives, including recruitment processes. 

With the advent of Artificial Intelligence (AI), businesses are increasingly turning to AI-driven solutions to streamline their hiring procedures, making them more efficient and effective.

However, as we embrace these advancements, it's crucial to prioritise ethics and responsibility in AI implementation, particularly in recruitment.

Responsible AI in recruitment refers to the ethical and transparent use of AI technologies to facilitate fair, unbiased, and inclusive hiring practices. By leveraging Responsible AI, organisations can mitigate biases, enhance diversity, and foster a more equitable hiring environment. So, how can businesses effectively utilise Responsible AI in their recruitment strategies? Let's look at some key practices:

Data Quality and Diversity: The foundation of any AI-driven recruitment system lies in the data it operates on. To ensure fairness and accuracy, it's imperative to utilise diverse and representative datasets. By incorporating data from various demographics, backgrounds, and experiences, organisations can minimize biases and promote inclusivity in hiring decisions.

Algorithm Transparency and Explainability: Transparency is paramount in Responsible AI. Companies should strive to make their AI algorithms transparent and explainable to both candidates and hiring managers. Providing insights into how decisions are made fosters trust and enables stakeholders to identify and address any potential biases or flaws in the system.

Bias Detection and Mitigation: Despite efforts to curate diverse datasets, biases can still exist within AI models. Implementing mechanisms for bias detection and mitigation is essential. Regularly auditing algorithms for bias and adjusting them accordingly ensures that hiring decisions are based solely on merit and qualifications.

Continuous Monitoring and Evaluation: The landscape of recruitment is dynamic, and so should be the approach to Responsible AI. Organisations must continuously monitor and evaluate their AI systems to adapt to changing circumstances and emerging challenges. Regular audits, feedback mechanisms, and performance evaluations are essential to ensure ongoing fairness and effectiveness.

Human Oversight and Intervention: While AI can expedite the recruitment process, human oversight remains absolutely indispensable. Human intervention is crucial for interpreting nuanced information, understanding context, and making complex decisions that go beyond the capabilities of AI. Incorporating human judgment alongside AI algorithms helps safeguard against unintended consequences and ensures accountability.

Candidate Experience and Privacy: Respect for candidate privacy and providing a positive experience throughout the recruitment journey should not be overlooked. Organisations must prioritise data protection measures, secure storage practices, and transparent communication regarding data usage. Additionally, providing candidates with clear insights into the AI-driven aspects of the recruitment process can alleviate concerns and foster trust.

Education and Training: Building awareness and competence among stakeholders is essential for the successful implementation of Responsible AI in recruitment. Training programmes on AI ethics, bias mitigation techniques, and best practices empower recruiters and hiring managers to make informed decisions and uphold ethical standards.

By embracing Responsible AI in recruitment, organizations can unlock numerous benefits, including improved decision-making, enhanced diversity, reduced bias, and increased trust among candidates and employees. However, achieving these outcomes requires a concerted effort to prioritize ethics, transparency, and fairness throughout the recruitment process.

In conclusion, Responsible AI offers immense potential to revolutionize recruitment practices for the better. By integrating ethical principles and best practices into AI-driven systems, organisations can build a more inclusive, equitable, and sustainable future of work. As we navigate the ever-evolving landscape of technology and talent acquisition, let us remain steadfast in our commitment to harnessing AI responsibly for the benefit of all stakeholders.

Exploring the Pros and Cons of Integrating Artificial Intelligence in Business Operations

Abstract:

Artificial Intelligence (AI) has emerged as a transformative force in modern business operations, promising increased efficiency, productivity, and innovation. However, its adoption also presents various challenges and risks. 

This paper examines the pros and cons of using AI in business operations, offering insights into its potential benefits and potential drawbacks.

Introduction

Brief overview of AI and its applications in business operations.

Importance of understanding the advantages and disadvantages of AI integration.

Pros of AI in Business Operations

2.1 Enhanced Efficiency

- AI-driven automation streamlines repetitive tasks, reducing human error and operational costs.

- Improved speed and accuracy in data analysis and decision-making processes.

2.2 Cost Savings

- AI-powered systems optimize resource allocation, leading to reduced overheads and increased profitability.

- Predictive analytics enable proactive maintenance, minimising downtime and associated expenses.

2.3 Personalisation and Customer Experience

- AI algorithms analyse customer data to deliver personalised recommendations and experiences.

- Chatbots and virtual assistants provide real-time support, enhancing customer satisfaction and loyalty.

2.4 Innovation and Competitive Advantage

- AI fosters innovation through predictive modeling, enabling organisations to anticipate market trends and adapt quickly.

- Automation frees up human capital for creative and strategic endeavors, driving sustainable growth and competitive advantage.

Cons of AI in Business Operations

3.1 Implementation Challenges

- High initial investment and integration costs may pose financial barriers to entry for small and medium-sized enterprises.

- Complexity in deploying AI solutions requires specialized expertise, potentially leading to skill shortages and recruitment difficulties.

3.2 Data Privacy and Security Risks

- AI systems rely on vast amounts of data, raising concerns about privacy infringement and unauthorised access.

- Vulnerabilities in AI algorithms can be exploited by cybercriminals, posing significant security threats to businesses and their stakeholders.

3.3 Ethical and Social Implications

- AI biases embedded in algorithms may perpetuate discriminatory practices, undermining organizational values and reputation.

- Automation-driven job displacement could exacerbate socioeconomic inequalities, contributing to unemployment and social unrest.

3.4 Dependency and Overreliance

- Overreliance on AI technology may lead to complacency and a loss of human oversight, increasing the likelihood of system failures and regulatory non-compliance.

- Lack of transparency in AI decision-making processes hampers accountability and trust, eroding stakeholder confidence.

Mitigating the Risks and Maximising the Benefits

Establishing robust governance frameworks to ensure ethical AI deployment and mitigate bias.

Investing in cybersecurity measures to safeguard sensitive data and protect against cyber threats.

Promoting transparency and accountability in AI systems through explainable AI (XAI) and auditability mechanisms.

Fostering a culture of continuous learning and upskilling to adapt to the changing nature of work in the AI era.

Conclusion

Recap of the pros and cons of using AI in business operations.

Emphasis on the importance of strategic planning, responsible AI deployment, and ongoing evaluation to harness the full potential of AI while mitigating associated risks.

This paper provides a comprehensive analysis of the pros and cons of integrating AI into business operations, highlighting the need for a balanced approach that prioritizes ethical considerations, risk management, and long-term sustainability.

The Darker Side of AI: Why Artificial Intelligence Can Be Bad for Business

Artificial Intelligence (AI) has undoubtedly transformed the business landscape in recent years, offering unprecedented opportunities and chances for growth, efficiency, and innovation. 

But! As with any powerful tool, AI comes with its own set of challenges and potential downsides. In this blog post, we will explore why AI can sometimes be detrimental to businesses, shedding light on the darker side of this technological marvel.

Cost and Resource Overheads

Implementing AI systems can be an expensive endeavour for businesses, especially for small and medium-sized enterprises (SMEs). Acquiring the necessary hardware, software, and skilled personnel to develop and maintain AI solutions can strain a company's budget. Moreover, AI requires substantial data storage and computational power, leading to increased operational costs, which might not yield immediate returns.

Job Displacement and Employee Resistance

One of the most significant concerns regarding AI in business is the potential displacement of human workers. As AI and automation technologies become more sophisticated, routine and repetitive tasks are increasingly being automated. While this can enhance efficiency, it often leads to job losses and employee resistance. The fear of losing their livelihoods can create a hostile work environment and reduce overall morale.

Privacy and Ethical Concerns

AI systems heavily rely on data, and the collection and the utilisation of this data raise significant privacy and ethical concerns. Companies must handle sensitive customer information with care and adhere to strict regulations such as GDPR and CCPA. A data breach or misuse of data can result in costly legal actions, damaged reputations, and loss of customer trust.

Bias and Fairness Issues

AI algorithms are only as good as the data they are trained on. Biased data can lead to AI systems making biased or even flawed decisions, which can perpetuate discrimination and inequality. This not only tarnishes a company's reputation but also invites regulatory scrutiny and potential legal repercussions. Ensuring fairness and transparency in AI systems is an ongoing challenge for businesses.

Overreliance on AI

While AI can improve decision-making and efficiency, overreliance on AI can be detrimental. Blindly following AI recommendations without human judgment can lead to poor decisions and a disconnect from the customer base. It's essential to strike a balance between AI and human input to maintain a holistic perspective in business operations.

Security Vulnerabilities

AI systems are not immune to cyberattacks and security vulnerabilities. Hackers can exploit AI models to manipulate decisions or steal sensitive data. Businesses must invest in robust cybersecurity measures to protect their AI systems, which can be an ongoing and resource-intensive process.

Rapid Technological Obsolescence

The field of AI is evolving at breakneck speed. What is considered cutting-edge technology today may become obsolete within a matter of few years. Businesses investing heavily in AI must continuously adapt and upgrade their systems to remain competitive. Failure to do so could even result in a loss of market relevance and competitiveness.

Whilst it's true that AI offers tremendous potential benefits for businesses, it's crucial to acknowledge and address its potential drawbacks, too. Cost overruns, job displacement, privacy concerns, bias, overreliance, security vulnerabilities, and rapid obsolescence are all factors that can make AI bad for business if it's not managed correctly.

Businesses must approach AI implementation with caution, ensuring they have clear strategies for mitigating these risks and fostering responsible AI development. A balanced and ethical approach to AI can help companies harness its advantages while minimising its negative impact on the business and society as a whole.

Eventually, if AI holds sway it will be computers talking to computers, each mimicking real humans who got lost somewhere in the rush to modernisation. 

(Image courtesy of Gerd Altmann from Pixabay)