What Artificial Intelligence can do for the world

What Artificial Intelligence can do for the world


Artificial Intelligence (AI) has long been used at the periphery of business and increasingly is a mainstay of our everyday lives. Be it an intelligent routing system on smartphones or virtual assistants on websites for online customer service, AI augments day-to-day processes with a view to solving problems and finding better solutions. Irrespective of its momentum in the business world, uncertainty still looms over when, where and how businesses should use AI. This warrants the close attention of leadership because it presents opportunities when applied well and risks when applied badly; these decisions ultimately affect society at large.

On one hand, some businesses are too cautious to incorporate AI to their value chain. Whereas other businesses may be too quick to adopt it with little to no understanding of how the technology can seriously metamorphosise their business, and ultimately, purpose.

This may be, in part, due to the misconception of artificial intelligence by many people, as a disruptive force as opposed to an augmentative one. Simply put, more emphasis should be placed on how it can enhance fundamentally human traits rather than devalue them.

By the same token, an overreliance on technologies with limited capabilities puts us at a great disadvantage. If we stop focusing too much of our energies on how AI will take over human intelligence, we are better poised to focus on the benefits, risks and limitations of AI today.

This begs the question of whether AI can indeed address the biggest issues that society and humanity at large faces. Furthermore, can business and technology communities shift their focus towards how such technologies can be used for greater benefit to society?

Here, we briefly outline AI’s potential to positively impact the world, some current challenges and opportunities with its application and how boards should effectively discharge their responsibilities.

How AI is and will change the world

Developing Nations
  • In 2016, Microsoft India, in collaboration with the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), developed and piloted an AI-Sowing App powered by machine learning and Power BI with a sample of 175 farmers
    • As a result, the alerts that these farmers received on their mobile phones about cropping dates, land preparation and soil test-based fertiliser utilisation helped increase crop output to nearly 30 per cent
  • Similarly, Africa’s AI community has grown over the last few years accompanied by an increasing number of university courses and educational programs dedicated to AI
    • The international community has also taken note of this, with companies such as IBM Research and Google AI helping tackle local issues such as diagnosing early stages of disease in cassava plants in Tanzania and speeding up the reporting of cancer data to the South African government
Social Welfare
  • Thorn, a not-for-profit founded by actors Ashton Kutcher and Demi Moore, aims to tackle child sex trafficking through AI. Their flagship product, Spotlight, is a cloud-based data collection and analysis service helping identify victims of sex trafficking. Since its 2016 conception, it has been able to identify 9,380 victimised children in total and has reduced law enforcement’s critical search time by 63 per cent
  • An African educational online service, Eneza Education, is a promising mobile-based platform providing lessons and assessments to almost 1.6 million subscribers through SMS and web communication
Economic Growth and Productivity
  • According to a September 2018 report by McKinsey, artificial intelligence can add 16 per cent or around $13 trillion by 2030 to current global economic input; this is an annual average contribution to productivity growth of around 1.2 per cent between now and 2030
  • Accenture research on the impact of AI in 12 developed economies reveals that AI could double annual economic growth rates in 2035
  • The impact of AI technologies is projected to increase Australia’s labour productivity by up to 40 per cent
  • In 2018, scientist Jae Ho Sohn and his associates developed a deep learning model to predict an Alzheimer’s Disease diagnosis 75.8 months, or little over six years, before the individual shows any symptoms
  • In December 2018, Intel and research firm Concentrix surveyed more than 200 decisionmakers in environmental sustainability and found that 74 per cent agreed that AI will help solve ongoing environmental challenges
  • Using DeepMind’s machine learning capabilities, Google has been able to reduce the amount of energy required to cool its data centres by 40 per cent

Challenges and opportunities

Despite the alarmist—even if warranted—views on the terrifying potential of general AI abound in the media, this future is still uncertain. Therefore, urgent attention should be placed on the narrow AI applications that exist today.

In this section, we highlight AI’s current limitations as well as the opportunities that can arise if these limitations are adequately addressed.

  1. It cannot break rules, let alone take risks

    • Limited to the certain parameters through which it is coded, AI will never be able to break rules. No matter how hard it is to distinguish between human and computer output, it can still be identified because a computer’s output is always formulaic and stems from calculations.

  2. It can be biased

    • Data is the lifeblood of AI and artificially intelligent software is only as good as the data it is trained to analyse. So, some biases held in the real world can filter into these systems. This is because some programs may rely on faulty algorithms or insufficient data to develop unfair biases.

    • Case in point, COMPAS is a risk assessment program used to forecast which criminals are most likely to reoffend in the United States. ProPublica, a not-for-profit media organisation based in New York City, conducted a metanalysis on 7,000 people who were incarcerated in a Florida county. They found that the system predicted higher risks of reoffending amongst black defendants and lower risks for white defendants; even if the white defendants went on to commit other crimes.

    • For AI to be unbiased, computer scientists will need to work hard at finding mathematically precise definitions of ethics that also account for the changing needs of society.

  3. They are incredibly slow learners

    • Consider videos games, as an example. When approaching a new game, we, as humans know that we have control over the protagonist and how it interacts with certain objects or obstacles. This is because we have prior knowledge of certain objects or things, being good, bad, useful, and so forth.

    • Conversely, a machine has no prior knowledge on the specific properties of certain objects. In the case of a video game, the machine would have no prior knowledge that an object, like fire, may be an obstacle. A machine needs to be fed huge amounts of data to learn more effectively.

    • In 2018, Rachit Dubey and colleagues at the University of California, Berkeley studied the way humans interact with video games to determine what kind of prior knowledge we depend on to make sense of the games. Drawing upon various developmental psychology theories, Dubey and his associates found that much like young infants learn to recognise properties of objects, the subjects in their study employed similar knowledge to the way they solved video games.

    • Notwithstanding these current limitations, such research could pave the way forward for computer scientists working on artificial intelligence; applying the same basic knowledge that humans pick up in infancy to program their algorithms.

  4. They have no consciousness

    • While most computer scientists believe that consciousness will emerge as technology develops, AI cannot replace fundamentally human traits, such as creativity, empathy and lateral thinking at this stage.

    • Academics from various disciplines grapple with differing definitions of consciousness, and whether artificial intelligence could ever achieve it. Some believe that it comprises a process of accepting new information, storing and retrieving it to form perceptions and actions.

    • Should this be the case, machines could be the ultimate consciousness whereby being able to gather more information than humans and therefore compute it into more complex, more logical decisions.

    • On the other hand, other scholars suggest an inexplicable quality about human behaviour that simply cannot be computed by artificial intelligence. For example, creativity or anything connected to the emotion centre of the brain, such as hunger, revenge, or love, do not necessarily derive from logic or reason.

    • One of the thorniest questions confounding scientists and philosophers alike, it is safe to say that long are the days before AI can surpass our own consciousness. It can, however, improve or augment it.

  5. It cannot explain its decisions

    • At times, we may want an explanation as to why a particular outcome is what it is. Although there is some progress in this area, the current parameters with which AI software operates can not necessarily justify its reasoning behind its conclusions, most especially in more complex circumstances.

    • Over the past few years, business leaders have been prompted to further understand the technology. Knowing its limitations allows us to not only understand its possibilities, but also its implications across the entire business.

What do boards need to consider?

In order to successfully implement AI into a business, leaders must embrace a hands-on approach and more granular understanding of the specific techniques of AI. This is what distinguishes leaders and companies at the frontier versus those who are merely paying lip service.

Whilst many leaders are concerned about the impact of AI on their organisation and its industry, fewer consider how they can use AI within the boardroom to process larger data sets, test management assumptions, run more scenarios around capital allocation decisions and gain greater insight into real-time organisation culture.

A 2017 survey by McKinsey not only showed the greater potential for revenue and market share growth when more companies use AI, but also mentions that the successful adoption of AI in businesses requires strong leadership support.

Below are some questions that boards can consider before executive teams incorporate AI into their organisation.

  1. Is artificial intelligence right or even necessary for our business?

    • Only if the benefits exceed costs should a business move forward, because AI tools will most likely require a business to reform its data architecture and governance.

    • This reformed data architecture and governance needs to demand standards of accountability, transparency and recourse in deployed AI systems.

    • Simply put, decision makers need to ensure that the potential AI solution is addressing a problem or gap. This is because without a clear strategy, the return on investment is far less likely to be maximised.

    • A good starting point could be for organisations to automate most of their menial work, in order to focus on providing tailored, personalised products and services that not only satisfy customers but meet growing expectations.

  2. Is our data governance adequate?

    • Data is a company’s longest-lasting asset, as it lasts beyond people, devices and facilities. Combining analytics with governance requirements, data governance is often mistakenly aligned with IT departments. On the contrary, data governance aligns with business initiatives.

    • Regardless of which department generates data, the three most important earmarks of data governance are content, accuracy and timeliness. Low-quality data usually has a negative impact on the corporation and its operations. Data governance teams should construct a system that supports quality data.

    • Aligning board governance and data governance requires good communication between the board, management and the data governance team. The better the data governance team understands the board’s strategic planning, the better the team will be able to align their objectives with it and share those objectives with members of the C-suite.

    • The key to a useful and healthy approach toward data governance is for the entire organisation to have a mindset of data being an asset. Moving forward, best practices for data governance are likely to move in the direction of a new focus on managing data in ways that create value or generate revenue as a value-added outcome of the data governance committee.

  3. Is our business AI mature?

    • Organisations lacking the requisite foundations to support AI adoption must build it beforehand. Many companies, particularly those outside tech industries, are still at the beginning of their journey. For this reason, using pilot programs can be effective, as it helps companies build skills and learn as they go.

    • Notwithstanding this, strong executive leadership helps promote AI adoption and deployment. Therefore, a more malleable relationship between technology executives and the board is strongly recommended.

  4. Do we have the right talent?

    • With the expertise scarce and the competition fierce, AI leadership is quickly becoming a necessary investment across all industries.

    • Once a discrete need for AI is identified, bringing in AI experts to conduct a pilot project can achieve big wins.

    • To determine the right cultural fit for your company, creating a profile of the ideal candidate would include a combination of organisational maturity and strategy, as well as the right ratio of AI knowledge to executive experience.

    • As opposed to executive reports to the CIO or CTO, a direct line to the CEO signals that AI is a top strategic priority.

  5. Does our board composition factor in technological deployments?

    • Having decisions made by a narrow demographic with a narrow perspective makes it difficult to adequately respond to complex problems that affect an increasingly diverse population, in a fast-changing world.

    • Whilst reviewing board succession planning, considering candidates who have the right expertise to oversee AI adoption is strategically critical.

    • As such, younger people are a large, fast-moving demographic that almost every business needs to embrace. Although younger directors may not necessarily be ready to take on such a big responsibility, perhaps allowing them the resources to develop their suitability to be appointed.

    • Three times more likely than other age groups to be online, millennials are unarguably a necessity to consider for boards today. 

    • Alternatively, having board advisors with AI expertise would be beneficial.

  6. What resources do we already have that we can draw upon?

    • Once more aware of the capabilities of artificial intelligence, remember to bring your attention back to the existing tools and resources that your business has. Then you can question how various AI tools can augment or add onto your existing operations.

    • By the same token, being aware of the constraints with your existing tools and resources will allow you to make better-informed decisions about AI that can tailor to your specific needs.

  7. Do we have a robust innovation engine that can cultivate AI initiatives?

    • Good ideas are everywhere in an organisation, not only among employees at all levels, but also customers. Building the right innovation engine to convert ideas into tangible outcomes stand to improve the value chain.

    • Within the organisation, this means allowing other C-suite executives to report on AI advancements and/or considerations to the board, as opposed to just the CEO. Not only does this cut the red tape, but also better informs the board about the day-to-day tasks.

    • Leaders should not be pressured to be AI experts. To extract real value from AI, employees at all levels of the organisation need to be empowered to make final decisions guided by AI, and then act on them. Reaching out to staff at the frontier of this area can solidify the can and can’t dos of the technology. As such, using self-service analytics will output more analysis than professional data scientists. Lowering the barriers to AI adoption stands to have a transformational impact on the way processes are carried out in various departments within your organisation.

    • Notwithstanding this, there needs to be solid training on how to interpret and analyse data properly. Before building these tools, make sure that you understand your own business’ biases, as well as data biases. Once again, this highlights the importance of robust data governance to ensure that the data being used is reliable.


The world stands to benefit from AI if the right decisions are made by leaders.  Successful businesses of the future will have vast amounts of tangible data and despite its vast benefits for productivity, the economy and society at large need to do more careful and strategic thinking about how to utilise AI appropriately. Thinking about the risks associated with an overreliance on AI remind us that there is still much to understand about this technology.

The value of AI is not to be found in the models themselves, but in our ability to harness it. Business leaders will need to prioritise and make careful choices about how, when and where to deploy them.

There is no doubt that big data and artificial intelligence will bring about important advances in the realm of management, especially as it relates to being able to make better-informed decisions.

While true artificial intelligence is some way off, we are taking advantage of intelligent automation, like machine learning, to improve business operations, drive innovation and improve our everyday lives

Whilst there is an importance of considering the longer-term implications of any technology, focusing too much on longer-term ramifications can shroud the benefits and risks associated with these technologies in the shorter term.

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