Tanya Long, COO Argility -retail concepts and resources

Predictive analytics lights the way

ajustus Apr 19, 2024 Data-driven Decisions, Predictive Analytics, PredictRetail, Retail

Retailers and brands can realise the immense power of data-driven decision-making by using predictive analytics to glean faster insights.

By Tanya Long, CEO – Argility Technology Group

Previous articles published on ITWeb have explored some of the challenges and solutions for retailers’ consideration to limit the impact of load-shedding on operations. The Industry Insights examined how technology, augmented by partnering with trusted suppliers, can be used to help navigate this, but there is more that can be done.

There has been a radical change in production, supply chain and consumer behaviour which cannot be ignored. And here is where we lean on the technology innovations of this century: artificial intelligence (AI), machine learning, predictive analytics and cloud
solutions.

It is true that predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

To use a casino analogy as an example: Let’s say you play roulette. In years gone by, one would watch the tables, evaluate the trends of each croupier, and then decide at which table you would take your chance with your R100. Imagine if someone had whispered to you – ‘Table 1: 65% probability on red, 35% probability on black’. What decision would you have made? And if an insight like this was given every 15 minutes, would you have been inclined to include this in your next decision for action?

Predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

This is the advantage that predictive analytics can provide. In a nutshell, it is the power of data-driven decision-making.

With machine learning, fluctuating behaviour can continuously be used to understand supply and demand patterns with a speed that is incomprehensible when compared to five or 10 years ago. The traditional software approach was to create the system rules based on the known rules; however, with the increased landscape complexity, an intelligent approach is to use machine learning to identify the patterns and provide much deeper insight.

It’s often said the beauty of AI is that it never sleeps – it works 24/7 – just what is needed to help us in our current economy.

Retailers and brands now have the ability to glean faster insights from multiple inputs relating to stock availability, pricing, placement and customer demand.

Predictive analytics uses the principle of small, compounded gains. For example:

What positive impact would be experienced by having a 2% increase in stock-on-shelf availability?
What impact would be gained by having a 2% increase in cash flow due to lowering of aged stock surpluses?
What would a 1% increase in gross profit mean, if stock prices were able to be speedily and strategically managed by considering pricing appetite and competitor pricing across a basket of products?

Now, I realise all of this takes data, and prevalent discussions in the media has raised the question: how much data do I need? When do I start using analytics? The general consensus is if one hasn’t started yet, you are already backfooted.

The advent of cloud has given the ability to store and process petabytes of data, cost-effectively and without excessive hardware investment. It has also given us the ability to scale easily with increasing/changing needs and to leverage off always-on infrastructure provided by global players.

What problems can predictive analytics solve?

Let’s start with the concept of demand forecasting. In the old days, stock and sales forecasting were generally formulated based on moving averages. Today’s retailers would be ill-advised to continue in the same vein. Predictive analytics can add immense value in this area.

Another area that predictive analytics can play a huge role for retailers is focusing on customer patterns. Not only for personalisation by getting the right product in front of the right customer, at the right time and right price, but also to evaluate the change in shopping behaviours.

Using machine learning for clustering and segmentation, identifying purchase habits and preferred products would streamline a personalised recommendations strategy.

In a credit retail environment, the additional insights on areas such as propensity to buy and customer lifetime value has the potential of elevating prospecting efforts. Anticipating customer needs would definitely not only assist retailers on building customer loyalty, but also decrease the cost of spray-and-pray marketing efforts.

Labour management is another area where data and analytics can play an important role in improving operational efficiencies. One cost pressure which may be within a retailer’s control is looking at optimal workforce levels.

Gathering data on speed and accuracy of staff performance can allow retailers and brands to streamline their operations to efficiently meet fluctuating business demands.

These are by no means where the use-cases end. I suggest commencing in an area where there is a compelling business challenge and in which one can measure and refine the results in order to take appropriate action.

Business and project sponsor support is vital for success, although I believe many stakeholders would rather be proactive and stack the odds in their favour.

Source: IT Web

Getting there: Key success factors for retailers

ajustus Jun 10, 2022 Data Analytics, PredictRetail, Retail

Success Factors: Using data analytics to become truly customer-centric is now within reach for retailers, but they need to adopt a focused approach with a clear plan − or risk failure.

By Tanya Long, Chief Operating Officer, Argility Technology Group

This article builds on the argument made in my two previous articles that data analytics, and especially the emerging capability to perform predictive analytics, offer retailers a genuine opportunity to reinvent themselves as customer-centric organisations, deepening customer loyalty and enhancing profitability in a volatile marketplace.

As always, though, nirvana isn’t reached in a day, and there are numerous pitfalls along the way. In this final article, I want to look at how to successfully make the move to becoming a data-driven organisation, rather than one that relies on intuition.

Based on my experience in the industry, the following key success factors need to be integrated into the plan:

Avoid grand projects and keep an open mind. It’s imperative to move away from grandiose projects led by IT, to which some corporates appear addicted. A much better approach is to focus on iterative projects led by the business that are less risky and allow the organisation to learn as it goes. This in turn means being open to changing tack as the data dictates − we have to be open to what the data is telling us, and act accordingly.

Understand data needs. It is also important to recognise that most retailers already have all or most of the data they need − don’t waste time and money trying to get at the data you want (or think you want).

In line with my advice in the first point − take baby steps, survey the existing data and work with that, at least in the beginning. That said, it is vital to take the necessary steps to ensure the data to be used is clean and reliable; the old “garbage in, garbage out” mantra holds good.

Set goals − actionable insights are key. A related point is that it is easy to fall in love with the data and embark on data-related projects that are interesting but that don’t deliver any real benefits. A disciplined approach is vital, and a data project must be aimed at generating insights that are actionable.

Knowing everything there is to know about a customer segment for itself is ultimately counter-productive. A better approach is to identify what information is needed to reach a strategic goal or make a better decision.

A disciplined approach is vital, and a data project must be aimed at generating insights that are actionable.

It may be interesting to know a customer’s preferences in the abstract, but it is only valuable when aiming to leverage that knowledge in order to get the customer to buy while they are in the store or on the website. An important element is speed: the actionable insight needs to be generated rapidly so that action can be taken in real-time.

Change the corporate culture. If the retailer is going to become customer-centric, just acting on insights is not enough. The whole organisation has to change its focus or predisposition − everything everybody in the company does or says must be founded on the customer.

For that to occur, a vital first step is for everybody to understand the direct link between customer-centricity and the bottom line (and thus, in turn, on benefits, job security and the rest of it). Segmenting customers in terms of their lifetime value to the company, and how much it costs to acquire and then service them, will help to make the business case for data projects. It’s particularly important that the CFO is involved so the return on investment for specific data projects is well understood.

Another central part of the new culture is a shift towards making decisions based on evidence only, not on emotion.

Use the growing understanding of the customer intelligently. My main point here is to ensure the customer experience is well designed in light of this knowledge and is constantly being refined via a feedback loop that links into strategy and operations. Every interaction with the customer, including those undertaken by software, must be linked to data.

Leadership must be on board. A profound change like this will not succeed without strong leadership. It is a vital step to get the leadership team on board and motivated.

Pay due attention to talent management. The customer-centric retailer needs staff who have the right skills. Specifically, this means access to specialists like data scientists but generally an ability to solve problems and follow logic becomes critical.

Make sure the technology is in place. As should be clear by now, technology is not a silver bullet, but it does need to be in place. Storing, processing and analysing fantastically large amounts of data depends on technology, and the building blocks must be solid.

Develop a pilot to demonstrate the value of predictive analytics to the organisation. There’s nothing quite so powerful as a successful project. It remains important to make the case for data’s role in helping the company to become more customer-centric and why that would be beneficial but give the project the best chance of succeeding by carefully designing a pilot project that can be relatively quickly deployed to show what you mean.

Retailers face a set of tough challenges in the short- to medium-term. Only those that harness the power of data to help them understand the challenge and craft effective responses will survive − as will those that understand this is not a quick dash, but a journey undertaken with an open mind.

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.

Using data to discover the future of retail

ajustus May 19, 2022 Artificial Intelligence, Data, Digital Transformation, PredictRetail, Retail

As business digitalisation continues apace, more and more data is being generated. Retailers need to understand its value and how to leverage it.

By Tanya Long, Chief Operating Officer, Argility Technology Group

Digitalisation has been growing for many years, and the social and business changes driven by the global response to the COVID-19 pandemic have accelerated it.

Hybrid working models that involve increased working from home get a lot of the limelight, but equally significant has been the definitive growth in online retail.

For example, e-commerce in the US increased from $431.6 billion in 2020 to $469.2 billion in 2021, with shopping habits looking like they have permanently changed to include a growing online proportion.

The move towards digitalisation across all industries has resulted in record levels of IT spend. Gartner forecasts that worldwide IT spending will jump by 5.5%, its biggest increase in more than a decade, to reach $4.5 trillion in 2022.

Unsurprisingly, given that data is the major by-product of digitalisation, a hefty proportion of that spending is going on big data and business analytics. Investment was projected to reach $215.7 billion at the end of 2021, with a compound annual growth of 12.8% until 2025.

These are all big numbers but, as always, much of that spend does not necessarily result in bottom-line benefits. There’s a lot of focus on collecting data but turning it into insights that generate business benefits is rather more hit and miss.

Effecting that transformation requires not only a profound understanding of the technology involved but, even more critically, how the particular industry sector works.

Goldilocks technology

It seems as though we’ve been talking about data warehouses and business intelligence forever, and business is littered with projects that never really lived up to expectations.

What’s changed is the emergence of new technologies that make it possible not only to collect and store vast amounts of data, but also to process it to extract valuable information.

Thus far, predictive analytics is not a crystal ball, but if one believes half the hype one reads, that could happen in the distant future.

Perhaps the most important of these is the cloud itself, because it provides a way for companies to access the plentiful and cost-effective storage they need to deposit huge and increasing amounts of data, and also the sheer computing power needed to process and analyse it − think artificial intelligence (AI) and machine learning (ML), both used to turn data into information.

As always, though, technology is only part of the story − and not necessarily even the most important part. The real point is that the cloud’s resources make it possible to mine data for patterns, deploy sophisticated statistical models and create models on which to base decisions.

In short, the real story is data and what it can be used to do. There are basically three ways in which data can be used, namely: to describe, to diagnose and to predict. The first two are backward looking: using data to establish what happened (descriptive), and then to understand why it happened (diagnostic). Both are obviously very valuable and can be used profitably to improve future performance.

However, the real benefits come when one starts to be able to use data to look to the future − what is usually called predictive analytics. It uses AI, ML, data modelling and the rest of them to analyse current data and make predictions about the future.

Of course, one has to understand this approach is predicated on the assumption that the future derives from the past, and thus that the past contains the seeds of the future.

Predictive analytics thus becomes less useful when change is highly disruptive, and also the further into the future one goes. The further forward in time one goes, the greater the tendency to regress to the mean, to become more and more generalised.

Clear benefits

Thus far, predictive analytics is not a crystal ball, but if one believes half the hype one reads, that could happen in the distant future.

But it does offer an evidence-based − or data-driven, to quote the industry jargon − way to identify how the near future is likely to unfold, and what actions a specific business should be taking to defend its current position, or to take advantage of new opportunities.

In this way, it can help an organisation to move away from the challenge of choosing between a large number of 50% probabilities, to choosing between a small number of high probabilities.

This is an extremely valuable strategic tool given the growing complexity of the business environment, and the intense competition fuelled by the globalisation of markets and a volatile economic environment.

All of these combine to create what is known as a VUCA world − one that is volatile, uncertain, complex and ambiguous − and so the company that is able to develop a likely-to-succeed strategy based on evidence rather than intuition is already better positioned than competitors.

In certain environments, organisations have experienced that business intelligence delivers a return on investment of 80%, which just goes to show the importance of data-driven decision-making − but the return can shoot up to 250% when predictive analytics is used.

The reason for this startling increase is that decisions are taken based on the rigorous analysis of fact and not on intuition.

The frustration for CFOs when considering the investment in AI and ML is that actual guarantees on ROI is not set in stone, the statistics come from passageway talk about successful projects. But there is no doubt the success is there and that the benefits abound.

Retail is a particularly VUCA sector because it straddles both the real and digital worlds, and consumers are growing ever-more demanding. For them, as I will explore in my next article, predictive analytics is especially attractive.

Source: IT Web

Tanya Long, Chief operating officer, Argility Technology Group.

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.