AI – Fad or Revolution?
AI is clearly not just another fad to resolve a specific work issue in a long line of acronyms offered by management consultants.
It purports to cover most issues for most areas of work in all sectors, public and private.
However, at present, we don’t fully know how effective it will be.
Experts have made extravagant claims for past fads and then clients found they did not live up to expectations, so how is AI different?
Consider what has led up to now:
- In the 1930s, WS -Work Study -and O&M – Organisation & Methods – appeared and proved effective, first at such companies as Ford and Bethlehem Steel in the USA, then worldwide
- In the 40s to 80s, OR – Operational Research – employed maths, computer modelling and statistics, first in WW2, then the steel and coal industries, then widespread, to solve complex management problems and find optimum solutions – (n.b. many Apps now do much the same)
- In the 60s, SA – Systems Analysis – mapped existing manual processes before being computerised and found huge waste simply by questioning the value of much useless activity that had become ‘custom and practice’
- In the 70s, at least in developed economies, brawnwork continued to be overtaken by brainwork, and manufacturing by service industries – the demand for WS and O&M was fading fast
- In the 80s, TQM – Total Quality Management – attracted a headlong ‘big business’ rush, at least to ‘keep up with the Joness’, until they realised it was just a confusion of :
- Quality Control plus SPC/ 6 Sigma – i.e. waste reduction again
- Plus attempts at upfront Culture Change which rarely worked
- The 90s saw not just widespread use of laptop computers, databases, word processors and spreadsheets, and then the internet, but also:
- Balanced Scorecards of essential KPIs– Key Performance Indicators – which had been very much needed by many organisations but sadly led many to collect far too many with costly results
- BPR – Business Process Re-Engineering – which was essentially Proudfoot’s famous ‘brown paper’ mapping system and then a common sense review of existing processes so as to best meet customers’ needs
- More recently, new fads have included:
- Lean which is yet another version of waste reduction
- Agile which offers ways for organisations to better respond to change
- All the above fads included much good sense, despite being accompanied with OTT verbiage, but many of their targeted benefits did not live up to expectations so they fell by the wayside
- In particular, none addressed both top and bottom of any organisation’s productivity ratio – panaceas they were not
- Thus welcome to the 2020s and the potential revolution of AI – Artificial Intelligence:
- Some experts – i.e. the select group of leading-edge wizkids who know more than us (but not everything) – tell us AI will act like Alexander Fleming’s penicillin for human problems – however others, equally qualified, liken AI to the perils of Damoclese’s sword
- Their every word is pored over by flocks of admirers, yet even they admit they don’t really know where this new extraordinary AI ability will end up taking us, for good or ill
- It’s certainly a lot more sophisticated than the Expert Systems of the 60s/ 70s/ 80s when computer programmes were available to help diagnose breakdown problems, whether suffered by humans or other complex machines
- And it clearly offers we humans unbelievable speed and ability to search a vast amount of data/ information/ ideas – sieve it all – select, combine, compare and predict – and then spew out results, hallucinations and all, with stuff that chimes with our human ways of thinking
- And most aspects of work – much brawnwork, most brainwork – will be affected
- As a result, many jobs will disappear whilst others will be changed – some will be improved, some made more interesting, some more effective – others will become less important, less time needed – 35 hour, 5 day weeks may disappear as did 7, then 6 then 5 1/2 day weeks – Keynes’ forecast of 15 hour working weeks will appear from over the horizon, albeit bringing other problems in its wake because many jobholders will not know what to do with the extra spare time released
- Overall, if the experts are right, several organisations will become much more efficient and profitable – but many more will go under – ‘creative destruction’ writ larger than ever
- And, no doubt modern-day Ned Ludds will appear everywhere if significant job losses arise
- At present, it seems we may well be at a major watershed – perhaps the advent of a new revolution
- But what if AS and its offshoots turn out to be just another fad, albeit a big one, bigger than all others?
- We aren’t qualified to say
- Hence it was good to find the following excellent article summarising the current position of AI – written by Safwan Sobhan and published by Entrepreneur.com
- Hope you enjoy it – see what you think
AI Has Taken Over Every Industry
Here’s How Your Business Can Actually Use It
- The architects of artificial intelligence (AI) have been named Time Magazine’s 2025 “Person of the Year,” reflecting the far-reaching impact AI has on every industry, company and nation.
- AI is reshaping business strategies to build a smarter future, significantly improving efficiencies, value and the customer experience.
- Those who fail to incorporate AI into their business models will ultimately lose relevance and competitiveness.
- Generally speaking, AI performs human tasks, enabling machines to learn from data and experience to help businesses make better decisions, offer personalised experiences, streamline operations, enhance productivity, create new capabilities and generate more growth.
Generative AI
- Generative AI, such as ChatGPT, Google Gemini, Microsoft Copilot, Meta AI and others, learn from billions of data points and generate content based on human input.
- It helps businesses create marketing strategies, full-scale marketing campaigns and content for blogs, websites, social media posts and email campaigns.
- Tools such as Perplexity AI are used for research, and other AI tools are used for coding and software development, product design and prototyping.
- These AI tools save you time and money by helping you develop a marketing plan, research the competition, launch new products, create collateral material and boost customer communication and engagement.
- What once took hours of mining consumer data, conducting research, writing and creating visuals can be done in a fraction of the time with AI and human input.
- Generative AI is also being used by businesses to automate customer service with chatbots and virtual assistants that handle complex queries and provide personalised support, offering tremendous operational efficiency and helping ensure requests, complaints and other service-related issues don’t fall through the cracks.
- At the same time, the service team has more time to focus on growing the business.
Machine learning and prescriptive AI
- Machine learning (ML) is a subset of AI that uses algorithms and statistical models to analyse raw data, identify patterns and generate insights.
- It processes searchable data, like names, purchase histories and website behaviour, and analyses unstructured content, including images, videos and social media posts.
- Prescriptive AI builds on those insights so you can take action.
- On the retail side, for example, businesses gain insight into customer preferences and motivations from their behaviour, enabling them to personalise marketing messages that will resonate with individual needs and create data-driven journeys and experiences that make customers feel seen and valued.
- Retailers know who will open an email, click through and make a purchase based on known behaviours and shopping trends.
- In real estate, ML and prescriptive AI are helping developers leverage insights from datasets that include historical sales, demographic shifts, economic indicators and foot traffic to identify the next high-growth neighbourhood or determine where properties are undervalued for purchase.
- Property owners use AI to assess potential development sites, with zoning laws, environmental data and local damage data at their fingertips to choose strategic locations and avoid delays or fines.
- Additionally, ML powers automated valuation models which provide instant data-backed property valuations by assessing various property attributes and market conditions.
- This allows for faster, more consistent appraisals and minimises the impact of human bias.
- Manufacturers use ML data and prescriptive AI to predict maintenance schedules, forecast equipment failures and optimise supply chains and production processes.
- Manufacturing operations can use real-time data to make quick, informed decisions to minimise errors, reduce waste and enhance productivity.
- The Insurance industry uses historical data to help determine underwriting risk patterns, whether an account is likely to have a loss, and if it financially makes sense to insure it.
- For example, one company used machine learning to develop a crash-prediction model for the trucking industry:
- By correlating insured data, claims and millions of records from government and other proprietary data sets, the company can predict whether a trucking account will have a crash.
- Using machine learning, it can accurately predict the highest-risk accounts. It can ultimately avoid writing business with customers who have high crash scores or can provide credits or discounts to trucking clients with low scores, ultimately resulting in better loss ratios.
Agentic AI and autonomous decision-making
- Agentic AI analyses unstructured data to enable real-time decision-making without ongoing human prompting, understands complex workflows to improve execution and results and interacts with other systems to adapt to evolving conditions.
- For example, agentic AI can automate payroll logistics, adjust supply chains based on real-time data to improve efficiency and margins, help healthcare organisations interpret physician notes and medical images to adjust treatments in real time and reduce misdiagnosis rates across healthcare, auto care and other service organisations.
- Law firms are using agentic AI to autonomously review unstructured legal documents and contracts to support preparation and risk identification.
Conclusions:
- AI is thus helping businesses build a better future, but it’s important to use it responsibly and with intention.
- Prioritise security, transparency, fairness and accountability.
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