‘Humanistic’ AI-powered Business

Prelude – The ‘S’ Curve of Business

Every company—regardless of business type or business model (i.e. product, SaaS, service, media, etc.)—will go through a highly predictable cycle of growth and maturity called the “S Curve of Business” summarised well by the infographic below (ref: RocketSource).

The “S” Curve of Business. Courtesy of RocketSource.

As we’ve seen time and again, impressive growth in the short term may be achievable for many companies, but sustaining that momentum without facing moments where growth stalls simply doesn’t happen. The market fluctuates. Competitors make adjustments to stay relevant and spearhead new campaigns targeting and drawing on your customer base. Your ground-breaking product is no longer as impressive and unique as it used to be.

Put another way, from the point of view of transformation and business change,

  • A project typically starts with high energy and limited resources. Think Tuckman’s “forming–storming–norming–performing” model. 
  • Every investment for growth reduces profitability until anticipated benefits are realised.
  • Lot’s of projects and companies fail because they can’t afford to fund the dip. This often includes sustaining backfill resources to compensate for people seconded from the business to help support change. Typically, this includes hiring contractors. However, cross-training employees or having an employee referral platform in place are also options — regardless of which model is chosen, there is a cost.
  • Eventually the organisation reaches peak performance and then requires the next level of investment to go to the level beyond.
  • Add another project … and add another ‘S’ Curve. And the cycle continues. For some companies, this may feel like “boom and bust” cycle with alternate periods of high and low levels of profit and loss.
  • According to InspiredCEOs, 96.4% of businesses NEVER get past the £1m revenue and 10 staff.

So why spend much of the beginning of this article talking about the above?

In short, embracing AI will require navigating the ‘S’ Curve and sustaining funding, executive sponsorship and Board-level support. Each of these challenges present unique challenges and hurdles on their own; they will distract most companies from focusing on topics relating to fairness, accountability, transparency and ethics and perhaps other social issues relating to availability, affordability, equity, bias, etc.

Taken together, all of these ultimately affect the overall human experience of both

  1. employees, engaging in AI projects to create products, services and solutions, and
  2. customers, expecting a positive “human” experience when using AI-powered products & services.

The biggest mistake a business can make is jumping straight into a major change initiative that is akin to “a square peg, round hole” scenario which may lead quickly to the significant failures. Take note of this Forbes article which catalogues lessons learnt from companies that failed at Digital Transformation.


Artificial Intelligence (AI) — along with one of its most popular sub-components, Machine Learning (ML) — is a key technology that is radically transforming our world. Let’s pause to reflect on the differences between AI and ML (picture courtesy of Ronald Van Loon).

Machine Learning is NOT AI

We also experience the convenience offered by AI and ML in our daily lives, for example in the form of intelligent search engines, translation algorithms, navigation systems, on-line e-commerce stores, chatbots that automatically respond to questions and complaints, and algorithms that make recommendations to us or even develop tailor-made products that meet our personal needs. AI (and ML) can also be combined with robotics or unmanned systems, for example in the manufacturing industry. AI owes its huge societal and economic potential to the fact that this key technology can be applied in almost all domains and sectors.

From start-ups and scale-ups to small and medium-sized enterprises (SMEs) or large corporations, AI initiatives are crucial due to their innovative and competitive power.

That said, businesses should consider taking a “humanistic” approach to AI adoption by

  • working together in public-private partnerships to realise societal and economic opportunities of AI.
  • focusing on “Explainable” and “Responsible” AI — to uphold fairness, transparency and equity.
  • focusing on equitable hiring which supports diversity and embraces remote working.
  • establishing AI applications that serve the interests of people and society — whilst avoiding #codedbias and other situations that lead to division.
  • opting for an inclusive approach that puts people first whilst we strive for reliable AI.

But this is not a straightforward task for any business. There are some key areas that every company should consider when embarking on their AI/ML journey, namely,

  1. Investing in an AI-enabled Operating Model
  2. Building (and sustaining) an “AI” Talent Pool
  3. Establishing collaboration structures to support “AI” Product & Service implementation.

In short, there are no shortcuts. Additionally, it is not just about technology. You also need to develop strategies that relate to how your organisation will manage data (to support analytics), people (to encourage collaboration) and processes (to support governance).

Without introducing strategies that address the areas listed above, your company may end up trying to fit “a square peg in a round hole” — which will ultimately affect your bottom line and allow your competition to “leap frog” ahead of you.

AI-enabled Operating Model

For any company, the “Operating Model” is HOW a business runs itself. It is how it uses its people, processes, data and technology to execute its “Mission and Vision” and bring value to its customers which may extend to its employees and workforce.

In contrast, the “Business Model” is a strategy that a company sets to drive how it will create and grow value, and WHAT it shall do to achieve it. It is a model used in strategy and planning. It fails when it doesn’t achieve its projected targets such as a particular revenue stream or profitability.

In today’s world of big data, large corporates have access to a wealth of information about their customers, as well as about how they do business and deliver value to their customers. By using AI and ML to build technology that can model and predict these customer and user journeys, or logistical challenges, companies can really leverage their data to find new sources of value.

Building new technology that streamlines a company’s logistics and powers its Operating Model is valuable. When a company has AI assets that can be licenced and sold to other businesses whilst also serving its own challenges, the value (i.e. revenue and profit) of the company has the potential to increase.

Types of Operating Models

When embarking on your AI journey, there are essentially THREE types of Operating Models that should be considered.

Each type needs to be underpinned with a data capability that most likely will need to be established in your organisation; this will be very different from building a digital capability. Many organisations are mistakenly shoehorning AI initiatives into digital transformation — which, in my view, may be a bit short sighted given they are very distinct topics.

1) Centralised model

Centralised AI Operating Model

This model will underpin your analytics strategy. The AI and analytics talent is unified and located in one centralised department that behaves like an internal consulting firm for the rest of the organisation on topics related to AI and ML.

2) Decentralised model

De-centralised AI Operating Model

This model pushes embeds the analytics team in individual lines of business (LoBs); working very closely with SMEs (Subject Matter Experts) or potentially act as SMEs themselves. There might be an IT (or R&D) department centrally working with them, or the IT team might also be inside the business unit.

3) Hub-and-Spoke model

Hub-and-Spoke AI Operating Model

This model aims to provide the best of both worlds, looking for a way to get the benefits of having a centralised team, while keeping analytics talent embedded within the business. It has one central team working in coordination with the AI and analytics talent that is purposely “scattered” throughout and across the organisation.

AI initiatives need more than a “Competency Centre” (CC) or “Centre of Excellence” (CoE) which both require new skills, organisational design structures, and executive support. Being able to adopt AI in a business starts with choosing the operating model coupled with clear business objectives, budgets and real-world use cases — and, dare I say it, upholding “humanistic” values and priorities as part of the operating model so that it is stitched into the fabric of new ways of working.

Whether your operating model is centred within a line of business (LoB) or centred at the heart of the organisation, the people charged with its success must ensure AI and analytics-powered products and services are created with “humanistic” values embedded throughout the design and development process rather than bolted on as an after-thought.

The “Humanistic” AI Team

Globally, companies are adopting artificial intelligence to solve their business problems to become AI-driven enterprises, however, the route is full of challenges/obstacles like

  • No “pipeline” of business-driven use cases to feed pilots or prototypes
  • Lack of data science resources
  • Losing valuable time with manual processes
  • Being not able to trust in artificial intelligence processes

Whether an AI team is an outgrowth of an existing analytics team or an entirely new group, there are many different activities that it can and should pursue. Some of these — like developing AI models and systems, working closely with vendors, and building a technical infrastructure — can be done in collaboration with an IT organisation; others will involve working closely with business leaders.

Building “Humanistic” AI Talent

Demand for skilled AI professionals is a major priority for countries like — the US, China, India, Israel, Germany, Switzerland, Canada, France, Spain and Singapore — in that order.

The US at 100% penetration of artificial intelligence skills, is the benchmark for other countries like China with about 92% of AI talent found here. India, Israel and Germany come close third, fourth and fifth with 84%, 54% and 45% of AI talent found in these countries.

The US stands a clear winner in terms of skilled AI professionals across various categories including research, followed closely by China at number two and the UK at number third. Australia and Canada are the other two countries with highest penetration of AI talent at number four and five respectively.

One of the most critical factors for a business looking to acquire and develop an AI Competency Centre (CC) is recruiting, attracting, or building talent. It is no secret that leading-edge AI engineers and data scientists are difficult to hire — even in Silicon Valley. Most organisations will require a few people with the ability to develop and implement AI algorithms—say, a Ph.D. in AI or computer science. But many of the business-focused tasks of a CC can be carried out by graduates and MBA-level analysts who are familiar with AI capabilities and who can use automated machine learning tools. Alternatively, to get started, businesses may consider hiring consultants or vendors to engage in the early stages of AI projects. In this case, these resources should be combined with internal employees to enable a level of “shadowing” and “on the job training”.

Companies should start now in building and nurturing AI talent. This should not be limited to quantitatively-oriented employees but rather all employees who express interest in “career pivot” opportunities; checkout this link to the excellent work that Sudha Jamthe from Stanford Continuing Studies and BusinessSchoolofAI is leading.

Additionally, more and more companies are partnering with universities to provision employees with access to modular “block programmes” in AI and Data Science disciplines leading to certification as part of Continuing Professional Development (CPD) initiatives. Read more about this in one of my recent postings available here.

Organisational Re-design .. Enabling Better Collaboration

Since AI talent is scarce, it is difficult to develop critical mass if it is scattered around the organisation. Regardless of what Operating Model our business settles for, the organisational structures should incentivise (and reward) cross functional collaboration that is baked into roles and responsibilities.

To avoid excessive bureaucracy, a centralised group should embed or assign its staff — at least some of them — to business units or functions where AI is expected to be common. That way the centre staff can become familiar with the unit’s business issues and problems, and develop relationships with key executives. Rotational programmes across business units can improve knowledge growth and transfer. As AI starts to become pervasive, these embedded staff may move their primary organisational reporting line to business units or functions.

As with many technologies today, AI projects are best conducted in an “agile” fashion, with many short-term deliverables and frequent meetings with stakeholders. If there needs to be substantial system development or integration, more traditional project management approaches may come into play.

At a minimum, the following key roles should be included in every AI Team :-

AI Engineer Roles

  • Focus on engineering and problem-solving skills (not “research” driven);
  • Create technology and data architectures that scale;
  • Create AI applications, products and services;
  • Practice “Humanistic” values (i.e. ethics, bias, equity, etc.) when designing, developing and testing algorithms, applications, products and services.

AI Data Governance Roles

  • Adapt existing Governance mechanisms, policies and processes to be “AI” friendly;
  • Engage in the selection of data sources used to address an AI question or problem;
  • Ensure data sources are representative and uphold diversity and inclusivity principles;
  • Monitor how data is used in algorithms in “humanistic” ways;
  • Assess data quality (and subsequently, oversees data cleansing initiatives);
  • Review and approves data usage by all technical / development / operational teams.

AI Translator Roles (i.e. “business partners”)

  • Has an excellent understanding of the organisation, its strategy and customers.
  • Member of a business function e.g. Finance, IT, HR, Legal, External Relations, who act as an internal liaison or translator, linking functions and business units (at all levels including the “C-suite”);
  • Help a business area to deliver its product and/or service strategy by bringing together the right expertise;
  • Scan AI solutions in the market and used by competitors;
  • Questions and challenges others to ensure “humanistic” values are upheld when designing, developing and deploying AI applications, products and services.

Business Leader Roles

  • Work closely with AI Ethicists
  • Ensure the business’ values, principles, codes and culture are aligned with an ethically and socially responsible business operation;
  • Help protect the brand and reputation of the business by working to prevent potentially unethical designs and bias in products, and consumer backlash resulting from the design and application;
  • Manage any negative legal consequence and liability as a result of biased algorithms — in a “humanistic” way by taking proactive steps to learn lessons and adjust any internal policies and governance frameworks;
  • Prevent any negative financial impact on the business due to one of the above and a resulting decline in market share. 

AI Ethicist Roles

  • Work closely with all of the above roles
  • Advocate against algorithmic bias and unethical behaviours that impact AI products & services;
  • Protect against unintended consequences of AI (via policies & processes);
  • Advise on the impact on consumers of AI applications, products and services;
  • Establish AI frameworks & policies that uphold company standards and codes of ethics.

Ultimately, all members of the AI Team should think and act as “stewards” of AI algorithms, applications, products and services.

Concluding Thoughts

Most artificial intelligence focuses on mimicking or exceeding the cognitive intelligence of humans often lacking any notion of emotional intelligence or “humanistic” values.

Typically, ethical and legal dilemmas arise because of their dominance, combined with ownership by a few big companies. They have assumed essential social and community functions and now set norms in society. From Trump’s screeds on Twitter, to Greta Thunberg’s warnings on Facebook, to YouTube performances by the poet Amanda Gorman, social media is the primary platform for information, free speech and artistic expression.

If your business wants to embrace AI and embark on its own journey towards intelligent systems and automation, you need to create a vision for AI in your company. This will inevitably require new business models and strategies. Otherwise it may be sub-optimal and impact your business, your culture, profitability, and future growth.

Starting your AI journey requires significant commitment – embarking on a change programme, disrupting “ways of working”, migrating to new operating models all while developing skills and initiatives that build long term value.

To build a great house you start with an architect. You imagine what’s possible and then you make plans. You don’t dig a trench and pour concrete into a hole and then think about where the walls should go, what kind of windows you like, what kind of roof you like.

To do it properly, you imagine it. Design it. Calculate the materials. Map out the plans. Hire the best people for the project etc etc. There is an order to success and much of it can be expressed in numbers that help you work it out.

It’s the same with your company when initiating your own personal AI journey.

Start with the end in mind (ref: Stephen Covey) and plan backwards to build forwards.

Key Take-Aways

  • Being specific on what kind of AI capability you want to build or harness.
  • Be clear about what you want to achieve and when.
  • Take your time. Hurried implementation will result in pitfalls that may ultimately cost you more time, money and effort.
  • Companies don’t have to do this on their own – learn to delegate (internally AND externally), and get people in to do stuff you are not good at.
  • Focus on moving from the “here and now” and beyond that.

Above all .. enjoy your personal journey building a “humanistic” AI-powered business.

Reach out to me if you would like some advice, guidance and/or support to get started.

About the Author #aboutme

Over the past 25 years, Salim has built a career in consulting, working both client and supplier side as an interim CIO/CTO and a Business Change / Transformation Consultant.

Salim has engaged in, and led, digital and technology transformations and programmes involving rescue & recovery (“turnaround”), process optimisation & improvement and organisational change — globally across the UK, Central Europe, Nordics, Turkey, UAE, US, Asia and Australia.

Salim is an Oxford University alumni and an author in the field of Artificial Intelligence. Key interests include the role of AI for the betterment of people and society.

Quantum Computing and AI


Quantum computing is the area of study focused on developing computer technology based on the principles of “quantum mechanics”, which explains the nature and behaviour of energy and matter on the quantum (atomic and sub-atomic) scale.

Wikipedia describes “quantum computing” as

“use of quantum-mechanical phenomena such as superposition and entanglement to perform computation”.

While a standard computer handles data in an exclusive binary state of 0s and 1s, quantum computers use quantum bits or “qubits”, which can take any value between 0 and 1. And if you “entangle” the qubits, you can solve problems that classical computers cannot. A future quantum computer could, for example, crack any of today’s common security systems – such as 128-bit AES encryption – in seconds. Even the best supercomputer today would take millions of years to do the same job.

Entanglement is a property of qubits that allow them to be dependent of each other that a change in the state of one qubit can result and immediate change in others. more than one state during computation. Superposition states that qubits can hold both 0 and 1 state at the same time.

According to Shohini Ghose, Professor of Quantum Physics and Computer Science, at Wilfrid Laurier University in Waterloo, Canada

“Quantum computers are not just a faster version of our current computers. They operate on the laws of quantum physics. It’s just like a light bulb compared to a candle.”

Quantum computing and Artificial Intelligence (AI) are both transformational technologies. Today, AI using classical computing enables “Artificial Narrow Intelligence” (or ANI). Quantum computing will significantly accelerate the journey towards “Artificial General Intelligence” (or AGI) imitating how the human brain functions and, perhaps, pave the way towards “Artificial Super Intelligence” (or ASI) which may surpass the human brain and mimic levels of self-awareness and self-consciousness.

Quantum Computing Timeline

It was the unorthodox theories of quantum mechanics, born out of the 20th Century, which were later to spawn quantum computing. The concept of using quantum entities to process data and solve complex problems, much like a classical computer, can be traced back to the 1980s – the era of the “God Fathers” of Quantum Computing.

1980 – Paul Benioff described the first quantum mechanical model of a computer, showing that quantum computers are theoretically possible. His idea of a quantum computer was based on Alan Turing’s famous paper tape computer described in his 1936 paper.

1981 – The next year, physicist Richard Feynman, proved it was impossible to simulate quantum systems on a classical computer. His argument hinged on Bell’s theorem, written in 1964. Feynman did propose how a quantum computer might be able to simulate any quantum system, including the physical world in a 1984 lecture. His concept borrowed from Benioff’s quantum Turing computer.

1985 – David Deutsch, a physicist, published a paper describing the world’s first universal quantum computer: a way to mathematically understand what is possible on a quantum computer. He showed how such a quantum machine could reproduce any realisable physical system. What’s more it could do this by finite means and much faster than a classical computer. He was the first to set down the mathematical concepts of a quantum Turing machine, one which could model a quantum system.

1994 – Peter Shor developed “Shor’s algorithm”, which would allow a quantum computer to factor large numbers much faster than the best classical algorithm.

The timeline below summarises what has happened since and imagines the future.

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Key Players

Quantum technology is still at an early stage of development. The first commercial devices have started to emerge in recent years, capable of performing a few hundred operations with tens of qubits. This early hardware was already sufficient to demonstrate quantum supremacy by solving a specific problem intangible for classical supercomputers.

Google, IBM, and a handful of start-ups are competing to create the next generation of supercomputers. The emergence of quantum computing might help solve problems, such as modelling complex chemical processes that the existing computers cannot handle.

D-Wave Systems Inc., a Canadian company, became the first to sell quantum computers in 2011, although the usefulness of quantum computers is limited to certain kinds of math problems. IBM, Google, Intel, and Rigetti Computing, a start-up in Berkeley, California, have collaboratively created working quantum computers for businesses and researchers.

Intel has started shipping a superconducting quantum chip to researchers. It has also created a much smaller, but so far, a less powerful quantum computer that runs on a silicon chip, which is not all that different from those found in normal computers.

Microsoft initiated a well-funded program to build a quantum computer using an unusual design that might make it more practical for commercial applications. Airbus Group also established a team in 2015 to tackle quantum computing at its site in Newport, Wales. Airbus’ Defense and Space unit’s main objectives was to study all technologies related to quantum mechanics, ranging from cryptography to computation.

The infographic below highlights the role of collaboration to support advancement of Quantum Computing technology.

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UK-based Innovation

Whilst the US and China may be dominating, the UK and Europe are not far behind.

France can justifiably claim to be one of Europe’s leading lights in quantum computing with national plans building upon a January 2020 report entitled: « Quantique : le virage technologique que la France ne ratera pas » aiming to make France a global leader.

The German government set aside two billion euros in a stimulus package, and just recently a panel of experts from research and industry presented a roadmap to quantum computing. 

In the UK, £1bn has been set aside which includes a 10-year investment by the UK’s National Quantum Technologies Programme, which was launched by the UK government in 2013. This has resulted in more than 30 quantum start-ups including a national network of quantum technology hubs in quantum sensors and metrology (Birmingham), quantum communications (York), quantum enhanced imaging (Glasgow), and quantum IT (Oxford).

Thanks entirely to a £93m investment from UK Research and Innovation (UKRI), the new National Quantum Computing Centre (NQCC) is being built at the Harwell lab of the Science and Technology Facilities Council in Oxfordshire. When it opens in late 2022, the NQCC will bring together academia, business and government with the aim of delivering 100+ qubit user platforms by 2025, thereby allowing UK firms to tap fully into this technology’s potential.

Another major achievement is the launch of the world’s first cloud-based Quantum Random Number Generation (QRNG) service built using an IBM quantum computer envisioned by Cambridge Quantum Computing (CQC) – impossible with classical computing.

It’s great to see such a co-ordinated and visionary programmes in the UK right now.

Quantum AI

Quantum technology has an immense power. It will allow us to do computing tasks that are outside of the reach of even the best computers today. Artificial intelligence, which is designed to analyse huge amounts of data, could benefit from this, as could materials and pharmaceutical research.

The term “quantum AI” means the use of quantum computing for computation of machine learning algorithms, which takes advantage of computational superiority of quantum computing, to achieve results that are not possible to achieve with classical computers, the following are some of the applications of this super mix of quantum computing and AI.

This allows industrial and academic researchers to perform simulations for solving ever-more complex design and optimisation problems, and ultimately leads to the development of better products and services. Still, many economically, technologically, and scientifically relevant problems (e.g. computational chemistry, drug design, biological processes, route optimisation) remain out of reach for modern and even future supercomputers, assuming the computing power will continue to grow at the present rate. As a result, countless approximate methods have been developed over the years, characterised by various trade-offs between accuracy and computational cost.

Benefits of Quantum Computing

The following are some of the advantages of Quantum Computing that make it so desirable for our world.

  • Quantum Computers will deliver enormous speed for specific problems. Researchers are working to build algorithms to find out and solve the problems suitable for quantum speed-ups.
  • The speed of quantum computers will improve many of our technologies that need immense computation power like Machine Learning, 5G (and even faster internet speeds), bullet trains (and many other transport methods), and many more.
  • Quantum computing is important in the current age of Big Data. As we need efficient computers to process the huge amount of data we are producing daily.

Applications of Quantum Computing

The following are some of the fields of quantum online application benefits that can be applied to make them more efficient than ever.

Artificial Intelligence

Artificial Intelligence (AI) is a key and one of the best technologies of quantum computing. The base of AI is on the concept of learning from experience. it is becoming more accurate depending on the feedback until the computer program begins to show “intelligence.” This feedback is base on estimating the probabilities for many possible choices. Thus, AI is an ideal candidate for quantum computing. It aims to change many industries. From cars to medicine, and in the future, AI will be what electricity was in the twentieth and twenty-first centuries.

Machine Learning Algorithms

Machine Learning (ML) and AI technologies are the two key areas of research in the application of quantum computing algorithms, giving rise to a new discipline that’s been dubbed Quantum Machine Learning (QML).

Currently, most industrial applications of artificial intelligence come from the so-called “supervised learning”, used in tasks such as image recognition or consumption forecasting.  With quantum computing, we are likely to start seeing acceleration – which, in some cases, could be exponential.

Hardware and Software Error Simulation

Large software programs with millions of lines of code or hardware systems with billions of transistors can be difficult and expensive to verify for correctness. Billions or trillions of different states can exist and it is impracticable and impossible for a classical computer to check and simulate every single one. Not only do we need to understand what is happening when the system operates in a normal manner but we also want to know what happens if an error occurs. Can our device identify it and has a coping mechanism to reduce any potential problems? Through the use of quantum computing to assist with these simulations, one can hope to provide much better coverage of their simulations with an improved time.


Most online security systems nowadays depend on the complexity of factoring large numbers into primes. While this is possible by using digital computers to scan through every possible factor. The enormous amount of time needed makes it expensive and impractical to “crack the code.” Quantum computers can compute these factors are more efficient than digital computers. This means such methods of security will soon become obsolete. There are also innovative methods of quantum encryption that are base on the one-way nature of quantum interdependence. Networks across cities have already been deployed in various countries.

Data Analytics

Quantum computing has the ability to solve problems on impressive scales by engaging with complex material that might otherwise ignore. A particular field of study called “topological analysis” helps to identify how certain geometric shapes behave in specific ways. In doing so, it describes computations that are more or less impossible to conjure onto conventional computers.

With the introduction of a topological quantum computer, one can do simple calculations. Hence, making the process that much easier.


Through the introduction of quantum dots, researchers hope to further improve their standards of nanotechnology. The ultimate goal is to improve health conditions in developing nations, while also introducing purification processes for various industries.

While this is a field of research that scientists are into already, there still exists a wide gap that needs to look upon to quite by the introduction of quantum algorithms that can ease the research process and also speed up results.

Digital Security

In today’s digital world where almost every individual has massive amounts of personal data uploaded onto the cloud, there exists a growing need to improve security standards in an attempt to help make the data more secure.

According to Shohini Ghose,

Quantum offers a way to encrypt information that can never be hacked, no matter how good the hackers are.

Quantum Key Distribution (or QKD), which implements a cryptographic protocol involving components of quantum mechanics, is being put forward as a secure mechanism to tackle the issue of security by helping users encrypt data while also enabling them to share that with a limited number of resources. So not only can messages/data is secure but also distributed among personnel thus helping with secure distribution.

Future Applications of Quantum Computing

Quantum computing is a promising technology which will change our lives in many ways. As research gets more attention from government, industry, and academia, more uses are expected to be found.

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Source: Futurebridge

Concluding Thoughts

Quantum computing is no longer just for physicists and computer scientists, but also for information system researchers.

According to a report published by Inside Quantum Technology (IQT), the quantum computing market will reach $2.2 Billion, and the number of installed quantum computers will reach around 180 in 2026, with about 45 machines produced in that year. These include both machines installed at the quantum computer companies themselves that are accessed by quantum services as well as customer premises machines. The report is available here.  

Cloud access revenues will likely dominate as a revenue source for quantum computing companies in the format of Quantum Computing as a Service (QCaaS) offering, that will be accounting for 75 percent of all quantum computing revenues in 2026. Although in the long run quantum computers may be more widely purchased, today potential end users are more inclined to do quantum computing over the cloud rather than make technologically risky and expensive investments in quantum computing equipment. 

Today, amongst the financial institutions using quantum computing, none have quantum computing as part of day-to-day operations. Some appear very close and are hiring staff at a level that makes one think they are on the verge. IQT Research expects that by 2026, revenues from cloud access to reach circa $410 million, making financial institutions the largest single end-user segment of the quantum access cloud market.

In a parallel track quantum software applications, developers’ tools and number of quantum engineers and experts will grow as the infrastructure developed over the next 5 years which will make it possible for more organisations to harvest the power of two transformational technologies quantum computing and AI and encourage many universities to add quantum computing as an essential part of their curriculum.

Artificial Intelligence (AI) and Machine Learning (ML) are today’s latest buzzwords, and when you mix that with ‘quantum’, these terms become a “mega-buzzword”. This lends itself to dystopian fears such as those previously raised by the late Stephen Hawking.

“The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.” — Stephen Hawking (2014)

There’s little doubt that quantum computing could help develop revolutionary AI systems. However, there’s is much more that needs to be done before it becomes mainstream.

Switching to a more positive note, I’d like to quote Brian Solis, Global Innovation Evangelist at Salesforce and a world renowned keynote speaker. 

“Now is the time to start building the vision, the expertise, dedicating teams and resources” for quantum computing. The stepping stones to get there are building a Center of Excellence (CoE) around AI”.

This will help make AI the focal point of an organisation’s efforts to become more agile and innovative.

Additionally, Solis adds “it forces you to get better data, clean the data, and build expertise and key capabilities around the data. Complement that with a smaller set of resources and a Center of Excellence for quantum computing”.

Inspired by Solis, I will end with this empowering quote from F. Scott Fitzgerald.

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Further Reading

Be sure to checkout this video “Quantum Computing Expert Explains One Concept in 5 Levels of Difficulty” hosted by IBM’s Dr. Talia Gershon who explains quantum computing to 5 different people; a child, teen, a college student, a grad student and a professional.

About the Author #aboutme

Over the past 25 years, Salim has built a career in consulting, working both client and supplier side as an interim CIO/CTO and a Business Change / Transformation Consultant.

Salim has engaged in, and led, digital and technology transformations and programmes involving rescue & recovery (“turnaround”), process optimisation & improvement and organisational change — globally across the UK, Central Europe, Nordics, Turkey, UAE, US, Asia and Australia.

Salim is an Oxford University alumni and an author in the field of Artificial Intelligence. Key interests include the role of AI for the betterment of people and society.