The AI Hype Machine: Hype, Hope, or Headache?

ajustus Feb 26, 2025 AI, AI Solutions, Artificial Intelligence, Data Analytics

By Andrej Hudoklin, Executive Head of Data & AI

Why This Article?

I’ve been in the data & analytics business for over 25 years, working on interesting AI solutions across different industries for the last decade. And yet, after all this time, I still find myself facing the same challenge – explaining to business leaders what it really takes to implement AI properly and create real value.

The problem? The hype!

With the explosion of Generative AI, many believe AI is as simple as plugging in a model and watching it work magic. But the reality is far more complex. GenAI is just one part of AI – an exciting one, yes, but not the whole story. The real question is:

How do businesses go beyond the hype and actually make AI work for them?

That’s what this article is about – cutting through the noise and giving you a practical, business-first perspective on AI.

AI is everywhere, but is it actually working for businesses?

AI is like the ultimate buzzword. Everywhere you look, companies are racing to implement AI, vendors are selling it as the magic wand, and investors are throwing money at anything with “AI-powered” in the tagline. According to Gartner, AI adoption in enterprises has grown by 270% over the last four years.

But here’s the catch – most AI projects don’t deliver on their promises!

What is AI, Really? (Simplified)

AI, or Artificial Intelligence, is about teaching machines to perform tasks that typically require human intelligence. These tasks can range from understanding language and recognising patterns to making recommendations and even automating physical processes.

AI is not just one thing. It’s an umbrella term for various technologies, each with unique applications.

AI Has Been Around for Decades

The current hype makes AI feel like a brand-new phenomenon, but in reality, it’s been working behind the scenes for decades. If you’ve ever used Google Search, relied on a credit card fraud alert, or gotten a recommendation for a show on Netflix, you’ve already interacted with AI.

Imagine AI as a diligent assistant that has quietly been working across industries long before ChatGPT made AI feel “human-like.”

Manufacturing: AI-powered robotics and automation have optimised assembly lines for years.
Retail & E-Commerce: AI has long powered demand forecasting, warehouse automation, and personalised recommendations.
Finance & Banking: Fraud detection systems and algorithmic trading have relied on AI-driven pattern recognition for decades.
Healthcare: AI has helped analyse medical scans and optimise hospital operations long before the rise of ChatGPT.

So why all the hype now? Because AI is finally stepping out of the background, is more powerful, more visible and accessible to everyone.

Why is AI Different Now? The Big Shift

Several key factors are making AI more disruptive today than ever before:

Computing Power & Cloud Infrastructure: Advances in GPUs and cloud computing have enabled faster AI model training and deployment at scale.
Big Data Availability: AI thrives on data, and businesses now have access to vast amounts of structured and unstructured information.
Breakthroughs in Deep Learning & NLP: Models like GPT-4 and DeepSeek can now generate human-like text, create images, and even automate creative work.
Low-Code & No-Code AI Tools: AI is no longer limited to data scientists. Business users can implement AI solutions without deep technical knowledge.

Why the “ChatGPTs” of the World Created a Misleading Perception

One of the biggest misconceptions about AI today comes from the rise of generative AI models like ChatGPT.

These models make AI feel easy, instant, and ready to use – just type a prompt, and you get a response – and most people just take it as 100% truth.

Generative AI (like ChatGPT): Already trained on massive datasets and available as ready-to-use services, making AI seem effortless.
Traditional AI & Machine Learning: Requires months (or even years) of data collection, model training, and integration before it delivers business impact.

This difference is critical for business leaders. AI-driven fraud detection, predictive maintenance, or supply chain optimisation cannot be deployed overnight. They require strong data, alignment with the business, and continuous iteration. Expecting every AI project to be as fast as ChatGPT sets unrealistic expectations and leads to failed AI initiatives.

The Two AI Races: Innovation vs. Tangible Value

Right now, we are witnessing two AI races happening simultaneously:

Tech Vendors: Companies like OpenAI, Google, and DeepMind are pushing AI capabilities to new heights, constantly releasing new models that are smarter, faster, and more capable.
Businesses: On the other hand, many organisations are still struggling to implement AI in ways that deliver real business value.

While vendors are in a race to push the limits of AI, businesses are facing a different challenge:

How do we move beyond AI experiments to actually making money or improving efficiency?

The “AI Outcomes Race” – Setting the Right Pace

Businesses don’t need to keep pace with AI research labs; they need to focus on outcomes. Organisations should set their own AI pace based on:

Maturity of their data infrastructure: Do you have clean, structured, and accessible data?
Internal AI expertise: Do you have the right people to implement AI effectively?
Business problem alignment: Are you solving real challenges, or just chasing AI for AI’s sake?

McKinsey Insight: AI adoption is moving past the hype phase into a period where businesses must focus on execution, scaling, and driving real returns. Those who prioritise operational integration over experimentation will benefit most.

Cutting Through the AI Hype: What Actually Works

So, how can businesses avoid falling for AI hype and instead drive real impact? Here’s a pragmatic approach:

1. Solve Real Problems, Not Just “Do AI”

AI should be a tool to enhance existing processes, not a project for innovation’s sake.

Start with clear objectives!

2. Focus on Data Quality Before AI

If your data isn’t AI-ready, fix that first. Invest in data management, master data governance, and integration.

3. Start Small, But Plan for Scale

Rather than jumping into complex AI, begin with focused use cases. But always have a roadmap for scaling successful pilots.

4. Engage Business Teams Early

AI adoption isn’t just an IT project. Sales, marketing, operations – everyone must be involved from the start to drive success.

The Future of AI in Business: Hype or Hope?

AI is neither a miracle nor a disaster; it’s a tool.

Companies that approach AI pragmatically, with a focus on data, business impact, and adoption, will see real returns! Those chasing hype will burn budgets with little to show for it!

If you’re thinking about AI and not sure where to start, let’s connect and figure it out together – data@smollan.tech

Linda Mandyu, Client Success Manager, ATG

ATG’s demand forecasting as a service – secret weapon for retail category managers’ toolkits

ajustus Oct 7, 2024 AI, Category Managers, Demand Forecast, Machine Learning, Predictive Analytics, Retail

Demand forecasting is a well-established and important field in retail, but accuracy and speed are becoming challenges in a fast-changing and ever evolving market. Category managers need advanced support to stay ahead in modern retail.

This is according to Linda Mandyu, Client Success Manager at the Argility Technology Group, specialist retail predictive and prescriptive analytics business within global enterprise, Smollan.

Mandyu says most category managers still attempt to manage demand forecasting on complex Excel workbooks, while some trailblazers are trying to push the envelope by integrating statistical analysis and BI tools. “It remains onerous and highly specialised, and requires very skilled statistical analytics, commercial and retail subject matter expert teams. These skills are hard to find, and when you do find them, they are generally experienced with industry and vertical-specific tools,” he says. “You might need a team of ‘unicorns’ to deliver what modern retail needs.”

As a specialist in the retail sector, Argility combines highly mathematical and statistical expertise with the most advanced predictive and prescriptive modelling, as well as leveraging modern cloud technologies, complete with a skills pool with over 40 years of retail experience.

“We’ve taken that deep experience, combined it with scientific, technical skills and built a product that is fit for the purpose of demand forecasting directly on the world-class Google Cloud platform, with the ability to analyse petabyte-scale data. Then we apply machine learning tools and AI to supercharge those capabilities,” he says.

Mandyu confirms that it is essentially “demand forecasting as a service” that allows retailers to, firstly, improve stock holding and distribution, and then to support price optimisation initiatives, thereby driving an increase sales and retention of your much-valued customers. “It’s like a secret weapon for category and brand managers alike, that arms them with highly valuable intelligence to inform precise decision-making.”

Mandyu explains that Argility’s Predict Retail services deep dive into enterprise data to uncover valuable insights. “For example, if they want to grow market share in a particular category, we look at what competitors have done, matched with their product-price elasticity boundaries, then give them high-level intelligence on where they are over or under priced. We also focus on recommended order levels so they are not over or understocked and can optimise their supply chains and better meet customer demand pre-emptively. This gives value out of the box much faster. It also frees up their skilled resources to do higher value human-specialised analysis that may fall out of the capability of artificial intelligence or machine learning efforts. They don’t have to do the number crunching, but rather pass it to a machine to do the heavy lifting of the incredibly large data sets stretching back over longer time series.”

Mandyu cites the example of an FMCG and perishable goods field service, which is expected to reduce millions of rands in returns through improved demand forecasting capabilities. “While the client closely manages stock holding and distribution and checks shelves several times a day, the process is flawed because it is based on human estimation. The business was incurring over R100 million in product returns each year. We have recommended improved order values to narrow the gap between ordering too much and ordering ‘just right’. The customer is now on track to achieve our conservative target, which is to save anywhere from 2% to 5% in returns, which in this case will amount to millions saved,” he says.

“Complex retail analytics business problems remain a source of endless excitement to the ATG team, a fact that may often seem intractable to our clients. But solving these problems present a perfect opportunity for converging the elusive ‘man and machine’ duo to achieve impactful success exponentially faster, and more accurately, from data-driven initiatives,” concludes Mandyu.

Source: IT Web | Intelligent CIO

Is customer-centricity in sight for retailers at last?

ajustus May 31, 2022 AI, Customer Centricity, Data Analytics, PredictCustomer

Data analytics—and particularly predictive analytics—can give retailers a significant edge in an industry that is rapidly changing.

By Tanya Long, CEO, Argility Technology Group

In my first article, I looked at data and the opportunity it offers to businesses and other organisations in general terms.

I argued that by learning to use data effectively, companies could not only understand the past better, they could also develop strategies with a much higher chance of success because they are based on extrapolations from real facts and not feelings or gut instinct.

I also made the point that retail is a sector in which this kind of data-driven decision-making holds particular promise. In this article, I want to explore why this should be, and what the benefits are.

First, let’s take a look at some of the retail trends and how they are in turn driving the shift to a data-centric approach.

As we emerge from the COVID-19 pandemic, it’s becoming clear that the move online which we have seen building over the past decade or more has considerably strengthened. Four of the five most valuable retail brands in the world are now online merchants – Amazon, Apple, Google and Microsoft, with the fifth, Visa, also heavily involved in the online world.

Real-world shopping remains important, of course, but the enforced move to digital channels during lockdown has had an effect: in the past six months, 65% of consumers made a purchase from a physical store – but 78% had purchased from Amazon, 45% from a branded online store, 34% from eBay and 11% from, of all things, Facebook.

The move online looks irreversible, especially when one considers these amazing figures relating to each generation’s preference to buying goods from a physical store: Baby Boomers 31.9%, Millennials 31%, Generation X 27.5% and Generation Z 9.6%.

In other words, Generation Z represents a step change, and it’s hard to imagine that the cohort that follows it will not continue along the same trajectory.

Blurring boundary

Retailers can draw two major conclusions from these and similar findings. The first, obviously, is that they had better have an online strategy; the second is that the line between the virtual and real is blurring in consumers’ minds.

By this, I mean that consumers not only want to move seamlessly between real and virtual channels and have the same experience on all, they no longer see a reason why the convenience and personalisation they are already getting online should not be replicated when they visit the store.

In the end, the retail experience can slowly become a collaboration between retailer and customer.

Among the multiple implications of all this, data emerges as a common thread. The digitalisation of the retail environment online means that a considerable amount of data is generated, which can be used to refine the customer experience and to improve the business processes.

But the same information can be used by retailers with real-world stores to adapt steadily their in-store environment by leveraging the lessons of online.

For example, beacons can be used to make the in-store customer experience much more personalised, mimicking to some extent the online experience. Beacons are essentially communication points that prompt customers to log in via their mobile devices to receive useful information such as where to find a certain item, or what specials are running.

Mobile phones have a crucial role to play in the digitalisation of real-world shopping and in bringing the virtual and real together. Research shows that half of shoppers use their phones as an in-store research advisor.

Mobile devices can also be used to offer more convenient, highly personalised ways to pay—it’s a fact that a large percentage of customers (almost 30%) abandon the purchase during the payment phase, so improving it makes a lot of sense.

A more sophisticated approach would be to allow customers to upload their shopping list so that the most efficient routing through the store can be prompted via the beacon, offering special deals at appropriate points.

As in the online world, this data can be used to refine the whole environment, making it more efficient and profitable for the company, and much more engaging for the customer.

In parenthesis, I want to emphasise the important role that data can also play in running the business, thus indirectly contributing to serving them better. The use of digital technologies across the supply chain is another topic, but again will generate a lot of useful data that can be used to improve the business in all sorts of ways.

Another avenue to explore would be the BOPIS (buy online, pick up in store) model, which some customers prefer.

Becoming customer-centric

All of these digital innovations have one thing in common: they generate large amounts of data about customers that offer retailers a golden opportunity to learn more and more about how to serve them better and deepen their loyalty.

In the end, the retail experience can slowly become a collaboration between retailer and customer.

At a very practical level, data analytics will enable much more accurate segmentation, moving away from the crude living standards measure (LSM) to customer lifetime value (CLV), customer acquisition cost (CAC) and cost to serve.

The latter three measures allow not only a more granular understanding of customers but also of their value/ potential value to the business, and so how much to invest in them.

Once one starts thinking about data and how it can help retailers truly transform the way they do business, to put the customer at the centre of everything, it’s clear that the sky is the limit.

However, as always, there are some caveats to bear in mind and I will look at those in my final article.

Published: IT Web

Tanya Long, Chief Operating Officer, Argility Technology Group.

Long has 30 years of industry experience. Her career in the IT sector started in 1988 in IT support for point-of-sale solutions. She moved formally into software development with UCS/Argility in 1989 as a developer and progressed through to team leader, account management, project and development management roles, which led her into various industries and corporations.

In 2017, Long returned to Argility (having previously worked at the company in a technical capacity) as human capital executive, where she reunited her retail, IT, leadership and HR knowledge to drive her zeal for transformation.

In her capacity as COO, Long is responsible for overseeing operations, with a specific focus on human capital, sales and marketing, and ensuring the company culture and vision shows up daily for customers through an engaged technical team of experts.

The likely effect of AI on the global job market

ajustus Mar 15, 2022 AI, Artificial Intelligence, PredictRetail

About 30% of all tasks are currently done by machines, with people performing the rest, but it’s believed the balance will change to 50-50 by 2025.

By Marko Salic, CEO of the Argility Technology Group

Artificial intelligence (AI) is no exception to the well-documented concerns surrounding new disruptive technologies and their effect on labour requirements. Gloomy predictions of job losses may well be correct.

Indeed, research supports this premise. However, the other side of the coin is that automation may lead to the creation of better jobs and reduce the need for physical labour – but there is a while to go before that happens and there are tangible business benefits to be gained right now by automating and optimising to remain competitive.

It is scarcely a few decades since the internet raised similar concerns as it grew. However, as it turned out, technology created millions of jobs and according to Forbes, comprises 10% of US GDP, with AI poised to create even greater growth in global economies.

It is interesting to note that PwC’s 22nd Global CEO survey revealed that 63% of CEOs surveyed believe AI will have a larger impact than the internet.

COVID driving the urgency for change

Of course, nobody foresaw the extraordinary events of 2020 with the declaration of a pandemic that threatened lives and livelihoods around the world. The economic fallout from the pandemic has been devastating, with millions out of a job due to the necessary closures of entire industries, hospitality, travel, etc, to contain the spread of the virus.

The arrival of COVID-19 has resulted in an acceleration of technological advances because of the urgent need to automate routine tasks – from contactless cashiers to robots delivering packages. In this environment, many are concerned that AI will drive significant automation in the coming decades, leading some to fear it will take their jobs.

It is reported that the US shed around 40 million jobs at the peak of the pandemic, and while some have come back, others will never reappear. One group of economists estimates that 42% of the jobs lost are gone forever.

Without a strategy, AI solutions risk never making it into production.

So, it is understandable that there is concern that this will be followed by more job losses as machines take away even more jobs from workers. The World Economic Forum (WEF) reports that automation will supplant about 85 million jobs by 2025.

However, this report is anything but gloom and doom as it goes on to note there is little to be concerned about as its analysis predicts the future tech-driven economy will create 97 million new jobs.

The WEF report reveals that currently, approximately 30% of all tasks are done by machines − with people performing the rest. However, by 2025, it’s believed the balance will dramatically change to a 50-50 combination of humans and machines.

All reports appear to agree on one thing and that is that AI will create more jobs than it destroys. AI is incredibly efficient at automating and optimising, but it does not do judgement – the latter requires human intelligence.

The PwC report notes that AI, robotics and other forms of smart automation have the potential to bring great economic benefits, contributing up to $15 trillion to the global GDP by 2030, but with a high human cost. PwC says this extra wealth will also generate demand for many new types of jobs.

So, it is apparent that the emergence of ever-new AI-driven technologies are not only powering the fourth industrial revolution and changing the way we work and live, but are predicted to create more jobs than they displace. This in turn will require new skills and necessitate significant commercial and governmental investment in upskilling and reskilling of workforces.

Productivity gains

In the short-term, it appears the biggest economic positive to come out of automation and AI will come from productivity gains due to the automation of routine tasks. This is said to augment employees’ capabilities and free them to focus on more stimulating and higher value-adding work.

Capital-intensive sectors such as manufacturing and transport are, therefore, likely to see the largest efficiency outputs from AI because much of their operational processes are ripe for automation.

I would like to conclude by listing firstly what companies are likely to gain by implementing AI programmes, and secondly, what they need to have in place to get their AI strategy off the ground.

Productivity gains from businesses automating processes (including the use of robots and autonomous vehicles).
Productivity gains from businesses augmenting their existing labour force with AI technologies (assisted and augmented intelligence).
Increased consumer demand resulting from the availability of personalised and/or higher-quality AI-enhanced products and services.

Has the company performed a readiness assessment? One source notes that without the following, the AI programme will hit a stall button:

Culture of innovation − businesses must be fearless towards embracing innovation and taking risks.
Buy-in throughout the organisation – from all levels of management and staff − is crucial.
Sufficient understanding of the quality and quantity of data on-hand – data is available in a myriad of forms, but without all types of data being considered, the algorithms cannot learn and interpret successfully. Data is the fuel that powers all AI.
Strategy − without a strategy, AI solutions risk never making it into production. Having the winning lottery numbers is pointless if you don’t own a ticket.
The right technologies − cloud-based computing and storage technologies are critical for AI programmes to produce effectively. AI workloads require an enormous amount of processing power.

I would like to endorse the foregoing as essential to the implementation of an AI programme. Therefore, start with an AI readiness assessment because if the company is not ready and launches into it, the odds of success diminish significantly.

Lastly, seek advice from an expert source – either by sourcing an AI strategist to work in-house or partnering with specialists in the arena.

Source: IT Web

Marko Salic is CEO of the Argility Technology Group, a software development group with a history that spans almost four decades, predominantly in the retail and supply chain sectors.

Salic has over 20 years’ industry experience, stemming from software development and architecture through to business development, management and strategy. He spent 15 of those years at Argility creating some of the company’s most innovative solutions and products.

His passion lies in the focus on new technologies and next-generation innovative solutions that will take various cross-industry players to the next level. Coming from a strong technical background, Salic’s expertise lies in understanding the challenges modern businesses face and the best way to solve them by applying modern technology and innovation.

In his leadership of Argility, he is shaping next-level client solutions that will address the need for businesses to digitally transform and provide a hyper-personalised omni-channel customer experience to manage digital disruption and stay relevant.

How to build AI into your organisation

ajustus Mar 1, 2022 AI, Artificial Intelligence, PredictRetail

Artificial intelligence engineering brings together various disciplines to tame the AI hype, while providing a clearer path to realising value.

By Marko Salic, CEO of the Argility Technology Group

As I noted in my previous article, artificial intelligence (AI) is driving many questions from business leaders as they seek to explore the full impact it can have on their companies.

A lot of concern has also been expressed about AI disruption. My advice on this point would be to prepare for disruption – plan for it. Before embarking on an AI implementation journey, assess the organisation’s readiness for it.

PwC advises that cutting across all these considerations is how to build AI in a responsible and transparent way in order to maintain the confidence of customers and wider stakeholders. But how to achieve this?

Research indicates that few businesses are ready to overcome the essential tasks necessary if they are to prepare correctly for such a radical change. For example, only 38% reported they can currently provide employees with reskilling and training opportunities in the face of technology disruptions.

Obviously, this is a serious misconnect and needs to be addressed. With the right strategy in place, it is possible to prepare the organisation and its staff to not only survive automation but thrive on it.

AI technologies permit businesses to mine data, generate insights, create operational efficiencies, provide stronger experiences, and close the gap between information and action in ways previously not possible.

All companies want to avail of this potential power, which also unleashes the ability to disrupt, innovate, enhance, and in many cases, totally transform an organisation. AI is an umbrella term encapsulating machine and deep learning, image and video recognition, predictive analytics, process automation, speech recognition, biometrics and natural language processing. It can apply to practically every industry sector.

What is AI’s value proposition in 2022?

Forrester’s predictions for the use of artificial intelligence in 2021 and going forward are clear on how AI will influence business development in 2022.

The research company says AI and machine learning will permeate new use cases with companies pushing it to new frontiers, such as holographic meetings for remote work and on-demand, personalised manufacturing.

It goes on to highlight how AI is expected to boost workplace automation and augmentation needs.

It is possible to prepare the organisation and its staff to not only survive automation but thrive on it.

In 2021, a third of companies in adaptive and growth mode looked to AI to help with workplace disruption for location-based, physical, or human-touch workers and knowledge workers operating from home. These businesses were reported to be applying AI to intelligent document extraction, customer service agent augmentation, return-to-work health tracking, or semi-autonomous robots for social distancing.

Gartner predicts a robust AI engineering strategy will facilitate the performance, scalability, interpretability and reliability of AI models, while delivering the full value of AI investments. It notes AI projects often face issues with maintainability, scalability and governance, which makes them a challenge.

It states that AI engineering offers a pathway that makes it part of the mainstream DevOps process rather than a set of specialised, isolated projects. It brings together various disciplines to tame the AI hype, while providing a clearer path to value.

Gartner highlights the fact that due to the governance aspect of AI engineering; responsible AI is emerging to deal with trust, transparency, ethics, fairness, interpretability and compliance issues – in other words, AI accountability. This drive towards trusted data and how it is used is particularly pertinent in SA.

Artificial Intelligence and POPIA

No discussion on AI in South Africa would be complete without examining POPIA – which came into effect on 1 July 2021 − and its impact on the use of AI systems.

South African businesses will be remiss if they do not familiarise themselves with a deep understanding of the regulations pertaining to the use of personal information required for AI or acquired through it.

The Act has made provision for the governance of automated decision-making and strictly directs how pronouncements or resolutions may be arrived at through information gleaned from AI profiling techniques. It prohibits financial institutions, for example, from granting or rejecting loan applications solely based on profiles created by AI systems.

Ignorance will not be accepted as an excuse if businesses inadvertently put themselves at risk of breaking with regulations. Analytics lies at the heart of AI systems that produce information which may be deemed valuable to the system but may not have been compliantly acquired as directed by POPIA.

Add to that the risk of a security breach which today is being cited more as a certainty than a possibility. In a case where personal information is compromised and data is stolen, the organisation that gathered it will be left with a fine and possible staggering loss of business due to breach of trust with its customers.

Do these constrictions mean AI cannot be used to tap into the unparalleled value it holds for businesses today? No, it certainly does not, but it does require partnering with AI specialists who can guide companies through the regulatory minefield to ensure compliance.

In my final article in this series, I will discuss the biggest fear with AI: the possibility of job losses due to automation and artificial intelligence.

Source: IT Web

Marko Salic is CEO of the Argility Technology Group, a software development group with a history that spans almost four decades, predominantly in the retail and supply chain sectors.

Artificial intelligence is a big game-changer

ajustus Feb 15, 2022 AI, Artificial Intelligence, PredictRetail

A deeper look at where artificial intelligence is set to go, as it disrupts business strategies and economies around the globe.

By Marko Salic, CEO of the Argility Technology Group

There is a significant amount of both circumspection and excitement surrounding artificial intelligence (AI). However, despite the fact that it already touches almost every aspect of our lives, it is in many ways still in its infancy.

A PwC study notes AI can transform the productivity and GDP potential of the global economy, with the proviso that strategic investment in different types of AI technology is needed to make that happen.

The report says labour productivity improvements will drive initial GDP gains as firms seek to ‘augment’ the productivity of their labour force with AI technologies and automate certain tasks and roles.

PwC research also shows that 45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety, with increased personalisation, attractiveness and affordability over time.

The research firm predicts the greatest economic gains from AI will be in China (26% boost to GDP in 2030) and North America (14.5% boost), equivalent to a total of $10.7 trillion and accounting for almost 70% of the global economic impact.

McKinsey estimates AI may deliver an additional economic output of around $13 trillion by 2030, increasing global GDP by about 1.2% annually, and notes this will mainly come from substitution of labour by automation and increased innovation of products and services.

From a macro-economic perspective, there are opportunities for emerging markets, like South Africa, to leapfrog more developed counterparts.

I think a figure like $15.7 trillion is enough to get any business leader’s chair into the upright position and paying attention to what AI can do. The PwC study shows exactly just how big a game-changer AI is likely to be, saying “just how much value potential is up for grabs”.

PwC reports that AI could contribute up to $15.7 trillion to the global economy in 2030 – that’s more than the current output of China and India combined. Of this, $6.6 trillion is predicted to be likely to come from increased productivity and $9.1 trillion likely to come from consumption side effects.

It will always be the case that some economies, markets, sectors and individual businesses are more advanced than others; however, as I mentioned at the outset, AI is still at a very early stage of development overall.

From a macro-economic perspective, there are, therefore, opportunities for emerging markets, like South Africa, to leapfrog more developed counterparts. The PwC study endorses this supposition. It is quite possible that within any business sector, today’s start-ups, or a business that hasn’t even been founded yet, could be the market leader in 10 years’ time due to its utilisation of AI.

What exactly is AI?

Definitions abound on the internet; for example AI is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

I like PwC’s broad definition, which is that AI is a collective term for computer systems that can sense their environment, think, learn and act in response to what they’re sensing and their objectives.

The different forms of AI in use today include digital assistants, chatbots and machine learning. According to Gartner, during the pandemic AI came into its own with chatbots answering the flood of pandemic-related questions, computer vision helped maintain social distancing and machine learning models were indispensable for modelling the effects of reopening economies. All of this would indicate that AI is beginning to deliver on its promise.

Gartner’s 2020 Hype Cycle for AI revealed that despite the global impact of COVID-19, 47% of AI investments were unchanged since the start of the pandemic and 30% of organisations planned to increase such investments.

It added that only 16% had temporarily suspended AI investments, and just 7% had decreased them. This shows that businesses around the world have AI front and centre of their growth strategies.

AI can be further segmented under various headers, including automated intelligence, which is the automation of manual/cognitive and routine/non-routine tasks; assisted intelligence − helping people to perform tasks faster and better; and augmented intelligence, which helps people to make better decisions.

However, the pièce de resistance aspect of AI − that Hollywood has used to scare us all over the years − is autonomous intelligence, which is the automation of decision-making processes without human intervention. This latter aspect is mind-blowing for many business execs as they consider the fact that as humans and machines collaborate more closely, and AI innovations come out of the research lab and into the mainstream, the transformational possibilities are limitless.

The reality is, most applications of AI in this day and age fall under the umbrella of narrow AI where the focus is on being extremely effective at one task or problem; for example, a chabot.

General AI is still a relatively theoretical concept and describes AI applications that can solve multiple problems across multiple domains, in an autonomous manner.

However, the sheer depth of potential and opportunities opened by AI are also driving many questions from business leaders as they try to capitalise on it. PwC says C-suite executives are seeking to know what impact AI will have on their companies and worrying if their digital commercial model will be threatened by AI disruption.

Where should they target investment, and what kind of capabilities would enable them to perform better are also common concerns. There is so much to consider, especially how to get AI into the fabric of the business.

I will unpack this last question in my second article in this series on AI and discuss how to go about merging it into the business in a manner that inspires the confidence of customers and all stakeholders.

Source: IT Web

Marko Salic is CEO of the Argility Technology Group, a software development group with a history that spans almost four decades, predominantly in the retail and supply chain sectors.