The Power of Education Algorithms To Drive Social Mobility

15 October 2025, Bamako, Mali  

Umaru of Mali had a sleepless night. He’d spent most of the evening trying to learn about the intricacies of international taxation so as to best prepare himself for an internal job promotion. He used the YeeHah learning platform. He was struggling especially on the double tax treaties that exist between the African Union and the European Union.

The next morning, as Umaru was boarding the solar-powered city rail in Bamako, he put on his headphones and accessed YeeHah. The algorithm powering YeeHah immediately started to provide case studies of how tax impacts trade between Africa and Europe, with expert tax practitioners sharing case examples. Umaru closed his eyes and immersed himself in the world of tax with a quiet smile of satisfaction on his face as he began to understand the intricacies of international trade.

YeeHah is an open and dynamic learning algorithm-driven platform that’s free – the ‘free’ stands not for the absence of payment, but for freedom. The freedom to create, curate and distribute content through an open-source algorithm which is highly adaptive to individual learners and provides them with knowledge and learning in a manner that is appropriate to their needs at any point in time.

Dealing with a generational challenge

In a world of rising economic inequality, technology has the potential to exacerbate the widening disparity. The pace of technological advancements throws into question the future viability of jobs and employability in their current form. Whilst there is the potential and opportunity for new jobs and roles, the fears of significant unemployment and social dislocation due to the potential inability of the human race to match technological scale and advancement remain unabated.

Greater social mobility remains of the strongest tools at our disposal to combat inequality. We need to consider how we can best improve the quality of opportunities and life chances for all. These opportunities start from educational attainment progressing on to professional development and ultimately life fulfilment. We will need to consider how we can best narrow life outcomes between those starting with disadvantaged backgrounds and their affluent peers.

Education has the potential to have a transformative impact on social mobility and be a leveller of inequality.  Access to education comes through institutions such as schools and increasingly also through digital platforms which, by their design, present few artificial barriers to attainment.

There’s a risk, however, that schools and educators may not have the necessary tools to provide the type of learning and education support desired by learners. In a world beset with technological inequality, further problems arise when those with access to technology have better educational attainment than those without.

Here’s a proposal on how we can resolve this particular challenge.

The power of open algorithms

Artificial intelligence (AI) has the revolutionary ability to hand educators the tools they need to support, guide and inspire learners the world over with ease and efficiency. How algorithms or AI differ from traditional adaptive learning systems is their ability to continuously develop deeper insights and be able to adjust their educational content delivery based on reasoning and updated decision-making. However, there remains a risk that with a proprietary approach towards building algorithms that power AI, the status quo of social and technological inequality remains broadly entrenched.

This is where a collaborative, multi-stakeholder, multinational effort to build common and open standards in algorithms pertaining to education could make a positive difference to the world we live in.

In this world, effort should be applied towards the building of an open-sourced algorithm that has the ability to remain adaptive to learner needs. Adaptive learning technologies already exist presently. However comprehensive adaptive learning algorithms can apply the powers of deep learning to best understand the native learning habits and styles of learners.  They will also be able to deliver the content to learners in ways they desire, at the time they require, and in an appropriate and contextualised manner.

This intuitive hyper-adaptive (I.H.A) algorithm will create a better understanding of the profile of an individual learner and better predict their behavioural patterns and cognitive abilities from insights continuously drawn from vast numbers of users adopting the algorithm. The scale of usage will enhance the predictive and intuitive abilities of the algorithm.

A key challenge with AI is the possibility of bias creeping into the algorithm that may result in the erosion of trust. This is why any open algorithm developed through a global network of partners will ensure a greater diversity of data and open-source development will help negate the possibility of bias.

The transformative impacts of AI

Individuals have different approaches and attitudes to learning. Our individual cognitive abilities and styles shape our approach to learning and the way we consume content. Even the way we learn different subjects can be different. For instance, we might learn about history through reading. We might learn mathematics by carrying out maths exercises (or by ‘doing’), or through reflection. We might learn physical geography through observation, video and audio. These learning preferences or styles are not static and have contextual differences that need to be reflected in the way courses are designed and created.

However the myriad of factors that impact learning preferences (including culturally-specific and contextual backgrounds) mean that it can be very difficult for an educator to cater to the individual and unique needs of the many. Attributional diversity can often be greater than the similarities amongst learners. This could mean opportunities are missed to create a community of learners who are significantly more motivated, engaged and inspired.

This is where the power of algorithms, machine learning and AI can step in to provide an indispensable pillar of support to educators to improve educational attainment and the performance of their students.

Through the development of a heuristic model underpinned by data collection on how individual learners prefer to consume educational content, it will be possible for algorithms to build individualised learning profiles. These intuitive hype- adaptive (I.H.A) learning systems will continue to learn about how individuals educate themselves; understand the commonalities across different individuals who share similar behavioural characteristics; and continuously refine and adapt the way educational content is made available to them.

The machine learning abilities offered by AI allow for not just merely a descriptive set of analytics which provide a current state view of the world but more crucially predictive as well as prescriptive analytics. Predictive and prescriptive analytics will provide an analysis on how other learners in aggregate are learning different subject areas and predict and prescribe and suggest content and educational material offered to learners in a way that will be both impactful and relevant.

The data insights and models developed by the AI which forms the basis of an intuitive hyper-adaptive learning algorithm and platform will ultimately help drive both effective educational attainment as well as a continuous improvement of the learning interface.

This tailoring of education is based on the algorithms’ abilities to not just pre-empt or detect an individual’s learning styles but also to anticipate their on-going educational needs as they go through their educational journey. With advances made in the volume of relevant data collected as well as enhanced computing power, algorithms will be able to rapidly create a detailed model of student profiles based on deeper understanding and interpretation of learner’s cognitive levels, their learning approaches and affective states.

Educators can reap significant benefits by providing knowledge in ways that are best positioned for their students. There is significant research evidence[i] to support the notion that relevant instructional and educational interventions that are matched to learning preferences will ultimately lead to enhanced outcomes.

How educational content could look in the future

In order to support hyper-personalisation to learners through a dynamic learning interface, it will also be crucial to consider how educational content is developed and delivered.

In order for an algorithm to serve the needs of learners most effectively, it will be important to have content in an omni-format approach across multiple cognitive levels. Educators will need to consider developing content that can be delivered in multiple formats (interactive, audio/video-based, text-based – i.e. omni-format) so that the algorithm can serve the right content at the right time in the format learners need it in.

The learning behaviours on a dynamic learning platform can only be supported with the availability of the relevant content set in different forms and built at different levels. Over time, the algorithm will be able to identify an individual learner’s optimal path to educational attainment and achievement and provide educators with the time and guidance required to enhance their own teaching.

Through this approach, for educators have the potential to make a positive impact on student learning outcomes at scale. It will also ensure that all students are able to achieve mastery of similar learning outcomes but through an adaptive and intuitive process that takes into accounts students learning preferences and styles.

The creation of the same content in an omni-format manner will mean that learners will not see all versions of the same content as it will be bespoke to their specific needs and demands. These needs are identified through the diagnostic and predictive abilities of the algorithms to best meet learners’ needs.

The algorithm will also be able to model the affective states, cognitive and educational levels of the students as well as their behavioural traits to accurately establish individual profiles. This will help educators shape their own pedagogical approach to their students and also help all learners starting at different levels arrive at the same learning outcome through a hyper-personalised journey of support and engagement. It will also over time allow for educators to build a view of how long it takes for educational attainment to be achieved for the various cohorts of learners and in the process develop more meaningful planning.  

These developments will also allow for employers, working closely with educators, to play a role in helping define key educational outcomes and helping deliver the employability promise to learners in a dynamic workplace. Employers can provide details of key employability needs and skills as emerging requirements appear and the relevant content could be developed jointly and subsequently distributed to learners in the ways that will best suit their needs and support them in the workplace.

Delivering broader impacts for all

There is an opportunity for the above education model to drive benefits to wider society globally rather than just specific segments. Whilst other broader factors such as targeted government policy and societal efforts play a big role in supporting enhanced social mobility, access to education and enhancements to educational delivery is a significant tool and enabler of social mobility.

An algorithm delivered through a dynamic learning interface can ultimately help create much more efficient and flexible pathways to support education attainment and can provide educational access to underserved learners.

The development of a relevant algorithm alongside a response learning platform is a significant undertaking and the risk of them remaining proprietary is that it could only further exacerbate the inequality gap and place further constraints on social mobility.

A proprietary algorithm will limit high-quality education to only those with the means to access it. This is why an open-sourced approach will allow various institutions and educators (regardless of their socio-economic backgrounds) to understand the underlying technology, tweak it to meet their requirements and populate it with content that’s relevant to their learners’ needs. This will allow for societal benefits to be delivered at scale. It also allows for the algorithms to be customised to meet cultural and contextual needs and ensure localised learning needs and demands are met.

An open-algorithm, developed through global partnerships and a shared vision, which provides educators with the freedom to create, curate and distribute content to meet their learners’ requirements, has the potential to enhance the overall quality of education delivered, support access to education and attainment, be more efficient, and ultimately improve student outcomes.

Educators can also focus their energy and time towards shaping learners through their craft of teaching and inspiring them towards educational success in a way that is meaningful and personalised. A global delivery of the algorithm with its own dynamic platform will help establish a global community of educators delivering significant local impacts.

Emergent technology has a vital role in ensuring education remains accessible, supports the global attainment of knowledge, paving the path towards greater social mobility, and a more equal world. Ultimately this will allow for progress to be driven in the broadest possible way and allow for high-quality education, underpinned by AI, to deliver greater opportunities for all.  

15 October 2035, Addis Ababa, Ethiopia

Umaru, now serving as a senior policy maker at the African Union, is drafting a series of recommendations to help improve governance and business environment across Africa. He is also started sharing his views and knowledge through the YeeHah platform which helps tax administrators, not just in Africa but globally as well, to understand key issues and developments in the area of tax policy and administration. His deep insights have been invaluable for everyone seeking to learn more about tax regimes in Africa and are open to all who seek knowledge.

One thing still perplexed him. Why call this fantastic educational platform YeeHah and what’s behind its name? As he started searching for answers, a smile spread across his face as he started learning about the intuitive hyper-adaptive (I.H.A) algorithmic-driven platform and read out the acronym aloud.


[i] Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2015). Continued Progress: Promising Evidence on Personalized Learning. RAND Corporation.

Book review – “Robot-Proof” by Joseph Aoun.

Robot-Proof: Higher Education in the Age of Artificial Intelligence by Joseph E. Aoun

This extremely insightful book by President Aoun of the Northeastern University in Boston on the future of higher education in an age of significant technological evolution driven through advances in robotics, artificial intelligence and data analytics provides much food for thought.

This (very) brief summary provides a useful overview of some of the points raised in the book which will be useful for educators everywhere to mull over. Anyone interested in the future of education and learning should get a copy and read it!

A key hypothesis laid out in the book is that we’ve reached an inflection point in time where machines (coupled with big data and underpinned by deep learning systems) have reached a position where their full transformational impacts will be felt by society.

There changing employment landscape (from the rising levels of automation and introduction of robotic algorithms) coupled with changing consumer behaviours will require higher education to introduce transformational changes that ensures relevancy and societal cachet.

The books lays out a new model of higher education that will ensure individuals progressing through the institutions will be able to thrive and prosper in an economy and society transformed significantly through technology.

President Aoun describes the need for higher education to “refit (their) mental engines, calibrating them with a creative mindset and the mental elasticity to invent, discover, or otherwise produce something society deems valuable.”

A framework for a new discipline called “humanics” is set out in this book which calls for education to develop individuals who can work alongside machines.

Humanics will consider the following two areas:

  • The building of ‘new literacies’ such as data literacy (the ability to read, master and analyse and drive insights from data); technological literacy (the ability to code and understand engineering principles); and human literacy (the development of what some consider to be ‘soft skills’ such as communication and design). These will need to add on from old literacies such as reading, writing and mathematics.
  • Development of cognitive capacities or higher order mental skills which will include: systems thinking (the ability to view businesses, technology and machine in a holistic way and considering them in an integrated manner); entrepreneurship (applying a creative mindset to an economic or business sphere); cultural agility (which allows for individuals to adapt to a global environment); and critical thinking (which fosters discipline, rational analysis and judgment).

President Aoun’s hypothesis is that this will enhance individuals’ ability to prosper in a world of highly sophisticated machines powered by AI.

The fourth transformational force

 The first three transformational forces that have shaped society and the economy have been fire (a point raised very eloquently by the good Bishop Michael Curry in his speech at the latest royal wedding!), steam and electricity.

The fourth transformational force shaping society is information (the life source fuelling the sophistication of machines and algorithms).

Aoun touches on the history of how technological advances have often caused great societal divisions and consternations, from the rise of the Luddites to John Maynard Keynes’ view that machines will cause “technological unemployment” to letters by academics to President Lyndon Johnson warning him that technology could undermine the value of all human labour. There is also a further description of how universities approach towards education has been shaped by societal changes from the rise of government funding to build a robust educational ecosystem in the 1800s to the introduction of the G.I. Bill (or the Servicemen’s Readjustment Act) to provide tuition support to returning soldiers from WW2 to the increased levels of federal funding to support university research and development.

There is also a good discourse in the first chapter to consider different views to how technology and automation may shape the future of work. Although the gig economy has been shown to be an increasing alternative to traditional work regimes, there is a suggestion that individuals who rely on the gig economy do not make enough to support themselves and only earn supplemental income.

Given this current state, Aoun postulates that it will be (wo)man’s innate ability for imagination and creativity that will help her/him in thriving in the new world and the power of higher education to further develop and build on this ability.

The view from employers

 With the rise of robotics and advanced machines, even traditionally ‘safe’ knowledge sectors such as law and finance are being impacted. However, despite the increasing demand for individuals with skills in computer science, algorithms and data science, one of the skills most desired (from a 2016 survey) by employers is that of leadership followed by ability to work with people – both very social skills.

Whilst companies need engineers, software developers and data scientists, the area where there will be a significant demand is for someone who has the ability to integrate all the various areas and adopt a holistic ‘systems thinking’ approach.

As employers expect the technical knowledge to be a minimum requirement, they look for other softskills or a high dose of ‘human literacy’ such as the ability to collaborate well, take a team-based approach to development, or demonstrate ‘deep listening skills.’

As professionals begin working with sophisticated algorithms and machines, there needs be a further demonstration of cognitive capacities by employees. Employees need to be able to better observe, reflect, synthesize and analyse information better.

Aoun’s conclusion is that critical thinking and systems thinking need to be instilled into the students of today and tomorrow to ensure they remain relevant for the workplace of the future.

A learning model for the future

A LinkedIn review shows that the top ten most desirable skills center around technology. Even though technology cannot provide jobs for everyone, it has also given rise to new industries and employment opportunities. Aoun argues that education has always had a central role in ensuring that people are elevated to the next levels of economic development. His view is that as the workplace of tomorrow demands more of individuals, there is a greater demand on how education supports people.

Aoun argues that the learning of the future needs to consider not just what technology can do but rather what technology cannot do and how a robot-proof education can further nurture’s man’s unique capacities.

There is a need for education to inculcate and cultivate the creative spirit of people. This necessitates an increasing focus on divergent thinking (as opposed to convergent thinking which leads to a single result). Divergent thinking focuses on the multiple outcomes to issues and stimulates creativity, curiosity and willingness to take risks.

Aoun states, “We need a new model of learning that enables learners to understand the highly technological world around that and that simultaneously allows them to transcend it by nurturing the mental and intellectual qualities that are unique to humans – namely their capacity for creativity and mental flexibility. We can call this model humanics.”

Here Aoun explores the new literacies that education should imbue into learners. Fredrick Douglas says that literacy is the path from slavery to freedom and Aoun argues that the deficit of literacy will lead to a slide into powerlessness.The new literacies include firstly, technological literacy (knowledge of mathematics, coding and basic engineering principles). He argues that as coding is the lingua franca of the digital world (a similar point was made by President Obama who incidentally was the first American President to write code) and the need to be conversant in the language of code.

Next the need for data literacy is made which is about the ability to understand, analyse and utilise data to drive insights. Finally, there is elaboration on the importance of human literacy which is about imbuing in learners the ability to collaborate, communicate and interact with one another and the world around them so that they make the right choices in life.

This chapter also considers how the new literacies need to be coupled with cognitive capacities that will help students participate more effectively in a digital world. These cognitive capacities include critical thinking, systems thinking, entrepreneurship and cultural agility. The role of educators in building these cognitive capacities is crucial.

Higher education provides an opportunity for learners to learn these cognitive capacities in a safe environment which allows them to understand context better and crucially fail and develop their resilience before they apply the lessons in the real world.

President Aoun stresses that the role of higher education is not to merely provide information and content but to help teach the new literacies and cognitive capacities. He argues that teachers need to be more explicit in what they are teaching to students and help students demonstrate to students how each area of their syllabi helps nurture each of the literacies or cognitive capacities. Students will need to be taught how these skills will support their own ambitions in life and contribute in the modern workplace.

A great quote from this chapter attributed to Desh Deshpande that bears thinking about is, “There are three types of people in the world. There are some people who are oblivious to everything, some people who see a problem and complain, and some people who see a problem and get excited to fix it. The difference between a vibrant community and an impoverished community is the mix of those people.” This is a quote that applies to any business as well!

Experiential learning

Here president Aoun underscores the need for classroom learning to be coupled with experience so that the learning retains its immediacy and relevance. Mastery of the literacies and cognitive capacities along will not help learners be robot-proof, they need to be able to synthesise humanics with experience. Students should be able to apply their knowledge in real-world situations and understand and reflect on the implications and outcomes.

In essence, Aoun describes this as flinging ‘open the gates of the campus and making the entire world a potential classroom, library or laboratory.’ It will be important for learning to follow a structured sequence and indeed the cognitive apprenticeship model describes how in order to master any complex subject, learners need to first acquire component skills. These skills must then be practiced in given context and finally apply them to different contexts.

This sequence of acquisition, integration and application leads to expertise. Aoun explains how students are first in a stage of unconscious incompetence (where they don’t know what they don’t know) to then progressing to a stage of conscious incompetence (knowing what you don’t know) to a stage of conscious competence (where they perform well but with deliberation) to a final stage of mastery (where they instinctively operate at the highest level in their domain).

Aoun shares how one of the most direct forms of experiential learning is “cooperative education” – an education model in which students alternate their classroom learning with sustained, full-time immersion in the professional workplace and then integrate with the two. The co-op model is different from internships in that it is much more sustained and go deeper into the learning by experience approach.

This model ultimately leads to greater employer satisfaction and there is a statistical significance in the levels of satisfaction displayed.

Educators also play a crucial role in helping students and learners understand their own experiential learning to maximise the impact of the lessons learnt through their experience in the real-world.

Aoun touches on the effectiveness of apprenticeship models (strongly prevalent in Austria, Germany and Switzerland) and how the education-employment collaborations have helped benefit both employers and learners.

Lifelong learning

President Aoun discusses how higher education can serve learners in a personalised and customised manner and will over time be compelled to serve people throughout their careers rather than at specific points in students’ lives. This will therefore require higher education to bring lifelong learning into the center.

Aoun also argues that it is lifelong learning that will help further drive down social inequality as it ensures everyone has the opportunity to develop and maintain valuable skills throughout their careers.

Higher education institutions need to see themselves as not just education providers towards undergraduate, post-graduate education or research but more as being in the business of lifelong learning. There has been a rise of for-profit education institutions and of “corporate universities.” These corporate universities have seen large corporate employers such as AT&T working with MOOC partners to deliver corporate training.

Aoun argues that these developments demonstrate that higher education is sidelining lifelong learning to its detriment. He also strongly encourages higher education institutions to partner with employers to create the relevancy required by the employment sector.

There will be a need for universities to customise courses to ensure they are designed and delivered in a manner that most appropriately provides education to learners, regardless of where they are on the career journey using the full extent of technology available to them.

The above developments will mean universities will need to consider how they package the content and learning and offer it in a way that allows for universities to consider how they award degrees or credentials. Universities may need to consider developing smaller blocks o knowledge that can be stacked in a way that may be suitable for traditional degrees but to offer it in a way that offers much more permutation, customisation and combinations.

There is also an opportunity for universities to consider how they should engage with their alumni and offer potentially subscription based opportunities towards learning.

Finally, Aoun discusses here the advent of multi-university networks which has universities adopting a multi-campus, multi-modal, multi-national approach to provide students with different learning experiences and environments, and enhancing their own cognitive capacities and contribute better to the world they live in.

Personal conclusion

The book touched on some of the important changes universities must consider as they seek to retain their relevance in being institutions that help societies adapt to an emerging and evolving world.

Education has a place in equipping society with the skills needed to thrive in a future which will look fundamentally different to present day. In a world where we see rising social and income inequality, education becomes a key driver towards social mobility and plays an instrumental part in alleviating the inequality we see in the world today.

The future of ABC (audit, blockchains and code)

The views and opinions expressed in the text belong solely to the author, and not necessarily to the author’s employer, organization, committee or other group or individual.

The audit profession has undergone numerous iterations in the last few decades with the onset of greater regulatory requirements, stakeholder needs and technological changes.

There is also a greater level of concern being expressed across all quarters about the quality of audits. For instance, half of all audits monitored by global regulators had deficiencies according to the International Forum of Independent Audit Regulators (IFIAR).

In a world where trust is eroding, it will be important to understand what the introduction of blockchain technology coupled with AI powered by stable algorithms supported by machine learning could mean for the world of audit and assurance in a broader sense.

Without going too much into the description as to what a Blockchain is (essentially a distributed ledger that can only append and not delete transactions posted by different parties or peers in a network), I will attempt to provide a view of what the vision of the future could look like.

It will also be important to note that there are different types of Blockchains. You have on one hand a public or permissionless Blockchain which allows anyone to participate in it – Bitcoin runs on a public Blockchain. You also have a private or permissioned Blockchain which is only open to a defined group of individuals or entities (and where the consensus approach to proof of work may not be required).

You can read more about the underlying principles of Blockchain here or learn more about it here (edX).

 

Imagine

It is the year 2022 and the world’s largest FMCG company, Duopivot, has a permissioned Blockchain where all of its financial and operational transactions are recorded across all of its numerous entities globally.

Duopivot’s auditors, MonTian, gets its partner to run an algorithm, with a series of conditions, assumptions and principles, agreed mutually by Duopivot and Montian, on the permissioned Blockchain. Rather than a sample of tests, a complete test of all transactions is made by the algorithm resulting in queries or anomalies which are raised automatically by the AI built within the algorithm to Duopivot’s finance team

The finance team respond and make the necessary adjustments and the financial statements are declared true and fair and the audit is complete. Within 3 days. With no other interventions from auditors MonTian other than the audit partner running the code on the Blockchain.

In addition to a test of balances and figures provided by Duopivot, the algorithm also tests the stability of the Blockchain and detects any abnormal transactions raised across any of Duopivot’s entities by cross-referencing them to external datasets (such as consumer behaviour, sentiment, overall sales figures for the industry and GDP growth of the multiple markets Duopivot operates in). The AI is able to apply the principles of scepticism to the data provided by Duopivot using its own massive datasets and get a high level of assurance on whether the transactions recorded on the permissioned Blockchain is valid or not.

Following the review and sign-off by MonTian, Duopivot’s largest institutional investor WhitePebble, decide to run their own algorithm on Duopivot’s permissioned Blockchain to test the figures themselves and to also assess future investment potential in order to aid them for points to highlight during the AGM which takes place within 30 days of year-end.

MonTian’s algorithm and AI also are able to assess based on Duopivot’s data whether they have utilised the optimal tax channels to achieve greater tax efficiency, as the AI has access to the datasets of every single tax regulations globally and can therefore chart the route to ideal tax positions.

 

Implications

This is the world we could face where audit teams are reduced to individuals running code and managing code rather than managing audit team members and delivering results near instantaneously. It also allows for institutional investors to test the financial statements on their own without relying solely on auditors and also using the information for their investment decisions and approach.

The AI built within the algorithm could also provide recommendations for business decisions on areas such as game theory and provide a probabilistic approach to decision making that will be more attuned to market needs and conditions.

Changes in legislation or reporting standards can also be applied almost instantaneously (even where judgment is required) and be tested to a very high degree of certainty.

 

What is the likelihood of this?

Very high.

We already see how PwC’s GL.ai is an innovation made of algorithms. It analyses billions of data points in milliseconds and applies judgment to detect anomalies in the general ledger.

Northern Trust (Nasdaq: NTRS) also currently do this in a similar vein where they launched their Blockchain technology for private equity and allowing for audit firms to carry out audits through access on their own blockchain node, providing access to relevant fund data. Northern Trust currently allows for audit firms to obtain the master records to their own systems or to complete the audit on their blockchain itself. You can find out more here.

The ability of algorithms supported by machine learning , particularly augmented data learning and deep learning models that account for uncertainty (such as utilising the Bayesian approach to machine learning), the role, shape and future of audit and assurance as we know it will change dramatically.

 

Control content, control data, control the world – the AT&T buyout of Time Warner

AT&T’s takeover of Time Warner makes strategic sense for the shareholders of AT&T. The only surprise is that early rumours of Apple buying over Time Warner did not come to pass.

AT&T are primarily a telecommunications company. They already control the data flows and analytics and understand all the little things that make people/customers tick. However, what they’ve not had is the content that their customers require and monetise the flow of content to the people who need it most.

Through the acquisition of Time Warner, it reduces AT&T’s transaction cost of providing the content to customers which is supported by superior data.

It’s akin to an infrastructure company laying pipes to bring water to households actually now providing the water along with the pipes they already have rather than have a separate company providing the water.

Why content matters

You have data on the information and content your customers require. However, you cannot act on the data yourself if you do not control the development of the content and intellectual property (IP). You can either try and create the content on your own or simply buy the largest available content provider available for sale.

This is what AT&T have done and it allows them to suddenly use the data and deliver even larger profitability to their shareholders by giving their customers the data they seek.

HBO (think Game of Thrones, Curb Your Enthusiasm, The Sopranos, etc), CNN, DC Comics (Superman, Batman, and the new UN ambassador, Wonder Woman), Hulu (Netflix’s rivals) are all now going to be under AT&T’s control.

This will allow them to control the entire spectrum of services they provide to customers and create an ecosystem (of both infrastructure and content) that may be difficult or unfeasible to leave for any customer.

Big data just gotten bigger

You know HOW your customers access information. You now know WHAT information your customers seek. Bring the two together and you create superior propositions for customers which rivals are unable to match.

The advertising potential also has now grown exponentially as AT&T monetise the data analytics and provide superior insight to advertisers.

Bringing the fight to the competition

The moment Google and Facebook moved from being search engines or networking platforms to becoming media and content companies with their own telecommunications infrastructure, the fight was on.

Facebook and Google are already providing Internet and call facilities. They also started buying or developing content facilities (Youtube acquisition by Google or Facebook Video/live).

This mean either existing telecommunications companies get into the business of content development or acquisition or they themselves get acquired. I suspect this was a major impetus for AT&T in their decision to buy Time Warner.

What next?

It’s always easy to bite, but it’s important to be able to chew and swallow. It remains to be seen how well the merger itself works. Most mergers are fraught with complications, from realising business benefits to cultural differences.

It will be interesting to examine Apple and Google’s next reactions. Google have developed their own hardware (Pixel) and Apple have long wanted to get into the business of content and IP.

Perhaps a takeover of Netflix by Apple in the offing?

Can this be Apple’s future roadmap?

In a world where the lines between sovereign nationhood and corporations are increasingly being blurred (roughly half of the top 50 economies in the world belong to that of corporations!), I was keen to thinking about the future of the world where Apple is concerned.

Apple have remained the foremost innovators and designers, creating that ultimate blend of functionality, art, design and user experience that has allowed for a far greater take up of technology than previously envisaged. The ease of use of Apple products has democratised the usage of technology by people, of all backgrounds, ages and capabilities.

I started considering where Apple may be heading over the next decade and half and thinking about how Apple, both as a company and as a global corporate citizen of the world, may evolve.

The image below best describes my own thoughts on where I think Apple will head towards. I have also provided a brief narrative to provide greater context.

A possible roadmap of where Apple may be headed towards
A possible roadmap of where Apple may be headed towards

Some additional context

Apple started the creation of their wider ecosystem with the development of OS X in 2000. The iPod, iPhone and iPad (along with associated products such as the iPod Touch, etc) relied on the iOS, which formed part of the greater ecosystem for Apple users.

Apple’s launch of the Apple Watch was the start of Apple’s foray into wearable technology (following at least two years of speculation).

Apple have also introduced Apple Pay, Apple HealthKit, and Apple Music (which nicely ties in with their multi billion acquisition of Beats) over the past few months.

2016 – the year of maturing of new initiatives

My view is that in 2016, Apple will support the development of additional personal and wearable technology (including iRing, Apple Glasses, etc) and we will see further launch of similar products (alongside newly launched products such as the new Apple TV). We will also see a further maturing of the Apple Pay system and greater application development for Apple HealthKit, utilising the Apple Watch and other related wearable technology.

2020 – The Apple Car zooms in

In about five years, we can expect to see the launch of the Apple Car. Possibly a completely electric car, with both driving and driverless functions, it will seek to revolutionise traffic control and movement. We can also expect to see financial aspects of the car, including insurance, leasing or hire purchase, supported by Apple Pay (or through connected bank accounts).

There could also be a potentially different model where Apple directly manage and maintain a fleet of driverless Apple cars, and passengers who seek transport can get in and by pre-booking through their Apple phones and/or other Apple products, can pay directly to Apple using Apple Pay (and taking on uber in the process).

Apple Insurance

We can also expect to see Apple providing direct insurance services to their consumers and users. Apple HealthKit can detect the health readings of an individual user and in the process price an appropriate insurance premium.

An individuals driving patterns can also provide data to help Apple price an appropriate auto/car insurance premium.

There could be a further maturing and take up of Apple Pay and related financing and banking products

2022 – Apple iBank is established

In the wake of the additional banking and insurance facilities provided directly by Apple, we can expect the iBank to be established which will allow for Apple to develop additional banking and finance capabilities, whilst also making better use of their cash hoard.

The iBank becomes the investment arm for Apple as Apple expands its product range into mortgages and fund management.

As Apple increasingly expands its portfolio of products, they start contemplating the development of Apple homes and flat

2025 – Apple the property developers and managers of societies

Apple starts producing smart iNtelligent homes to support increasing government demands for affordable, smart housing to meet the growing population demand.

The Apple Home becomes an all-encompassing home that is fitted with Apple sensors, that demands Apple usage by the users/residents and incorporates all other elements including mortgage, insurance, and electronics/appliances which syncs itself automatically with the user needs and demands.

Having a smoke at home – expect the Apple sensors to pick this up and send you an add-on to top up your health insurance with, for which you can make payment through Apple Pay – connected to your Apple iBank account.

Your fridge stacked with fizzy drinks, sugar-laden donuts and you are consistently frying food? Expect to receive a notification that your health insurance premium may be compromised and that you may need to top up!

2026 – Apple starts funding governments

As Apple Bank expands further, we can expect to see Apple Bank participating directly in funding campaigns led by the IMF, the AIIB (Asian Infrastructure Investment Bank) and the World Bank, amongst other multilateral agencies. This participation may allow for Apple to obtain additional concessions to sell their products or services.

We can also expect to see Apple participating in various UN and international conferences to support their aims and ambitions

2030 – All hail the Apple Nation

In 2030, the first Apple Citizen is naturalised. The Apple Citizen received an Apple passport, which allows him to travel to a large number of countries (all of the visa requirements are pre-met through Apple’s existing data). Apple’s virtual citizenship is supported by a comprehensive and robust Apple foreign policy backed by a deep monetary policy (exercised by Apple’s iBank) which also means the launch of the iDollar (Apple’s virtual currency backed by the Apple Central Bank).

Apple’s predictive AI (artificial intelligence) also can predict individuals who may be about to engage in subversive activities and detains them for their own benefit and reduce crime and a state of lawlessness. It also forces health lifestyle habits.

Apple starts running lives and tells people how to dream and what to dream.

The era of Apple

This could be the era of Apple. Some may welcome it as it could lead to a more efficient world. Others will resist. The resistance will be led by the men and women wearing old school Casios and using Nokias!