In 2022, we were pleased to join the ranks of companies registered to provide specialized consulting services to agricultural producers in Québec.

We wanted to share in this report a case study of some work conducted for one of our AgriTech client who recently got in touch.

Today’s agribusiness lenders must be equipped to understand client needs in order to make sound credit and business decisions. In turn, agribusiness owners want to build financial acumen and provide financial information to current and potential lenders.

Generally speaking, financial statements are used to assess profitability and to plan for the next year. Banks, financial institutions and some government-sponsored organisations also use them as a condition of their financing. 

We assisted the client in preparing the financial information required for borrowing purposes.

We are now able to support our client in areas including:

• Assessment of agribusiness lending opportunities
• Recognizing how the accounting practices and financial statements of agribusinesses differ from those of commercial enterprises
• Performing sensitivity analyses to determine how deviations from projected performance might impact credit risk
• Cash-flow planning and financial scorecard for the agribusiness  

 Our client utilizes an innovative Québec-developed technology through horizontal rotary cultivation to optimize the plant production cycle while limiting water consumption and energy losses.
Indeed, various technologies have continually evolved making use of less land to grow more crops. For example, AI has improved a wide range of agriculture-related tasks in the entire food supply chain. Thus, we were in a position to advise our client to better understand the current challenges such as food safety and traceability, environmental impact, manufacturing performance as well as the solutions available to them. If you want to know more about the case study or discuss the range of services offered to our agribusinesses, please do not hesitate to contact us.  

Over the past decade, the rapid integration of AI has impacted our everyday lives. There has been an explosion of research and development in this field, which has influenced how we live, how we work and how we make decisions. 

On one hand, human nature has this tendency to ask for ever more. And AI can afford to push the frontiers of knowledge and scientific discovery for merely all sectors and industries. On the other hand, techno-determinism supports the idea that if a technology has been invented, then it has to be deployed. 

However, AI misuse has caused a significant number of downside risks and challenges. Where there is a unity of purpose that peace and prosperity for people and the planet are desirable outcomes, then we also have a shared responsibility to foster trustworthy AI systems.

Of AI systems that work in the service of good

It is no secret that as humans, we have greatly benefited from the vast array of AI applications. To be sure, the unfolding of the AI revolution holds out the promise for further breakthroughs and solutions to global issues, from climate change and world hunger, to chronic disease and poverty reduction, to financial inclusion and optimizing economic aid. 

It is widely documented that a hellbent drive towards AI solutions at all costs can also reduce welfare and cause unintended /negative consequences. These include law enforcement, criminal sentencing, healthcare delivery or employment opportunities given AI would replay and amplify the same existing inequalities. Furthermore, by way of numerous concerns including security of data, issues of bias and privacy, the development and deployment of AI techniques should ensure and aim to perform equally for all. 

As noted by Professor Sarvapali Gopal Ramchurn, “the pace of change in the AI field over the last decade had been too fast for those that use, operate and regulate systems that end up using AI-based solutions.”

Throughout 2021, we have seen a flurry of national and regional endeavours to push the agenda forward. 

For instance, the National Research Center of Canada is harnessing the power of AI for the global good. It aims to boost scientific discovery and engineering design to solve the most complex problems in a range of fields.

The UK government has published a transparency standard for algorithms that is geared towards managing risks and building impact. It requires a brief description of the algorithm and its use in terms of how and why, as well as a more detailed info about how the tool works, the training data and the level of human oversight. 

The European Commission released a proposal to regulate AI describing it as an attempt to ensure a “well functioning internal market for AI systems” that is based on “EU values and fundamental rights.”

In November 2021, UNESCO released a set of recommendations that speak for the range of national /international policies and guidelines to promote human rights but also contribute to the achievement of the SDGs.

These 4 archetypes are:

• Protection data 
• Banning social scoring and mass surveillance
• Helping to monitor and evaluate
• Protecting the environment 

There is broad consensus that human-centered AI approaches and regulatory frameworks are necessary to make sure that emerging technologies benefit humanity as a whole. Several guidelines have been established and many more are underway. 

Responsible AI at the firm level

According to Algorithm Watch, a Berlin-based non profit organization, at least 173 set of AI principles have been published around the world. 

At business and enterprise levels also, AI governance frameworks are numerous and diverse. Many companies have enabled AI principles to guide their actions. They have seen these as the curbs on the roadside to keep themselves out of trouble. Yet, the hurdle really lies in the execution, that is crossing the bridge that translate into everyday practice.

In this regard, monitoring requirements (in terms of transparency and explainability) and control requirements (in terms of data) both contribute to drive complexity of the algorithms/ projects. But they also reveal the limitations. 

Chief among them is to acknowledge that it may not always be possible to train AI systems on all possible scenarios. There is a trade-off between efficiency and fairness. For critical systems, where there may not be time for human intervention, trust in the design stage is of paramount importance. In addition, several use cases attest that transparency is not always desirable as expected because it can lead to worse results. In other cases, we ask ourselves: does the algorithm factor randomness and human indecision? or else who is harmed when algorithms are to become unfair? For instance, even though experiments under real conditions tell that people’s reaction is mostly to freeze or do nothing, the frequently-discussed Trolley problem (linked to autonomous vehicles) and its footbridge variant – although purely hypothetical – provide evidence that modern day moral dilemmas can be highly problematic. 

The diversity of data science teams /developers is also crucial to raise awareness of any unconscious biases that stem from the integrity of data or the decision-making process.

Outside the realms of design and process, another booming area considers the ethics of AI focusing on outcomes. Impact assessments akin to human rights and sustainability reports, or audits similar to companies financials are two popular trends. Although these approaches would not iron out the algorithm issues, hard believers argue that they will enforce legal accountability and ensure the results are reviewable by the public. This helps supporting innovation while managing a range of risks such as security and compliance, as well as reputational risks including talent acquisition and employee retention.

Leadership roles and risk-based approach in the era of AI

A recent survey conducted by Edelman Research revealed that ethics was 3 times more important than competence as far as trustworthiness of companies is concerned. Therefore, leadership endeavours to encourage ethical behaviours and align them to corporate values are critical to produce positive outcomes and financial performance. According to Professor Nelson Phillips, the old leadership competencies are not disappearing, but new ones are appearing. And ethical competencies depend on leaders having deep competencies in technological competencies and organizational competencies.

The Covid-19 pandemic has blurred the boundaries between work and life. And with the power of social media and the scandals of deep fakes, it has also spurred the creation of a workforce that is even more curious and questioning. This is on top of the pressures for responsible innovation and the ethical adoption of new technology. No doubt that AI will increase the demands for effective leadership that combine a good dose of humility and confidence under uncertainty, as well as the need for transparent communication. 

Organizations may also have to design AI governance frameworks that are adequate to their risk profile and tolerance. These guidelines are particularly challenging for SMEs due to the lack of resources. In “Building trustworthy AI solutions: a case for practical solutions for small businesses”, the authors argue that a business must question where responsibility (tasks and obligations) lies within their AI governance framework and must define accountability (oversight and liability) to roles across the design, development and deployment lifecycle. Their analysis provides a mechanism for SMEs to select their own toolkits based on their current capacity, resources and ethical awareness levels focussing initially at the conceptualization stage of the AI lifecycle and then extending throughput. 

Furthermore, innovation also requires collaboration and cross-functional integration. 

Last November, we learned more in “Talks on Transdisciplinarity” organized by the Division of Arts & Humanities at the University of Kent, how the field of Transdisciplinarity was evolving among scholars. Likewise on AI ethical issues, it is gaining momentum within institutions and university circles involving research and education from many disciplines to work and collaborate with various stakeholders.

Government also play a leading role to set new standards of collaboration on ethical AI. For example, the Canadian government has developed a risk-based approach to AI adoption in the public sector which divides the AI systems in different levels. The four factors used to determine the risk-level are impact on:

• the rights of individuals or communities 
• the health or well-being of individuals or communities 
• the economic interests of individuals, entities, or communities 
• the ongoing sustainability of an ecosystem

And a study from the World Economic Forum, “Unlocking Public Sector AI, AI Procurement in a box” recommends a multistakeholder approach with collaboration from AI experts and developers, consultancies, prominent IT service providers, startups, universities, research institutes and citizen rights organizations. The guidelines aim to address specific business needs and lead to more efficient, responsible and sustainable outcomes for the public and private sectors. 

Closing remarks

There is real and proven potential for AI to transform our life and work. Thus far, scepticism surrounding algorithms although well founded do not outweigh the vast array of advantages AI applications bring to the table. The limitations of AI systems and the ethical concerns voiced are important considerations in order to channel efforts towards any future implementation success.

While there is agreement that AI needs human-centered regulatory frameworks, there is much scope for the practical details. In this new age of constant change and digital transformation where technologies in general and AI in particular are ubiquitous, purpose matters. And a new set of leadership skills are required to address the business needs in both public and private sectors. Forging a common understanding of the implications provides a massive opportunity to enable AI systems that work for all.

New cyberthreats with data proliferation

If you have worked or are familiar with the banking industry, you would know that onboarding courses or training in AML (Anti-Money Laundering), KYC (Know-Your-Customer) and Compliance & Risk mitigation are integral parts of the work carried out by bankers, from tellers to executives. Bank employees and their customers are made aware of the cyberattacks with regular communication and information of the potential dangers. Nevertheless, there is an ongoing threat for financial institutions. 

Cybersecurity is a hot topic for risk managers and company leaders. Reports from the Global Risk Institute and the CSFI identified financial crime and cyberattacks/ data breach as the most important risks and challenges financial institutions and risk managers are currently facing. 

“Cyber risk remains a top concern as a result of the pandemic with more staff working from home, more devices accessing digital banking environment and more people shopping online”  says Ms Sonia Baxendale.

Also, in the Banking Banana Skins 2021 – CSFI report, Andrew Warren notes that “banks are having to implement new tactics and to develop defensible strategies that leverage big data & analytics to help tackle this rapid evolution of financial fraud”.

An article from the IET ¹ shows that money laundering is estimated to amount to around 2% to 5% of global GDP. This equates to about $800 billion to $2 trillion. And IBM found that Automation and security AI, when fully deployed provided the biggest cost mitigation, up to USD 3.81 million less than organisations without it ².

Indeed, the proliferation of data has brought up opportunities for AI innovations as well as challenges. We may recall that IBM reported a few years ago that 90% of all the data in the world was created in the 2 years just prior to 2017. Yet, it’s estimated that around 90% of digital data is not used and 90% of data is never accessed again just 3 months after it was first stored. This causes a major problem insofar as poor data quality leads to less-than-optimal decision-making.

Professor David Hand at Imperial College London coins the term “dark data”, saying that missing or overlooked data can be more important than the data we draw on. This lack of awareness he explains, can unfortunately lead to inappropriate or dangerous actions.

That is to say that as much as companies are eager to augment client intimacy (understand their needs, customise products and services, mining data to exceed expectations) through the various channels made available, they are confronted with certain challenges in order to take – depending on either side of science, art or humanities one stands – the signal from the noise, the needle from the haystack, or to separate the wheat from the chaff.

Machine Learning and processing power

Banks have ramped up their AI capabilities and use AI for trading and investment strategies, risk management, stress testing, capital optimisation, etc.

Insurers are taking advantage of AI tools in various ways. AI is used for example for mining purposes, to streamline processes in areas such as policy administration, data extraction and claims processing. For Life & Health in particular, AI is used to price insurance policies as well as to up-sell or cross-sell investment/ wealth management products. 

In other industries, like Manufacturing or Oil & Gas, precision control and monitoring are crucial to ensure safety and availability of systems. AI is used to regulate fluctuations in temperature, humidity and other sensors in order to prevent human or environmental disasters. 

To be sure, the key AI innovations have to do with statistically sound decision-making. Machine Learning (ML), a key subset of AI teaches a machine how to react to some type of data. It’s a fairly simple process, yet it gets complex when more data is added. For a company of a given size, if for example

   – you are concerned that your organisation is too far from becoming AI or data-driven

   – you have too many human errors because large data has made predictions, optimisation and insights difficult

   – you have trouble to develop and deploy models into production and add value to your business

Then you may need some kind of ML.

ML can automatically detect unusual or irregular activities (in systems and applications), to identify email spam or malware (from a virus definition), to spot fraudulent credit-card transactions (from past occurrences). It does so by having labelled data sets, annotated classification from a database. This particular technique is called supervised learning. It relies upon large amounts of training data to deliver on its promises. 

At the other end of the spectrum, there is unsupervised learning used for producing descriptive models, and which operates without any labelling of the data. Here, the algorithm makes associations for “clusters” of data that exhibit similar behaviours.

There are a number of ML techniques. However, one should not be unmindful that ML has intrinsically a certain percentage of error. If the training data is biased, the result will subsequently be an AI system that will make unfair decisions. 

A survey on bias and fairness in ML, 17 September 2019 from Mehrabi et. al identifies up to 20 possible statistical fails. And accuracy of the system is a measure of how large the percentage of correct answer is, while transparency will aim to enhance accuracy, reliability and overall performance. 

This can give rise to issues such as adverse or discriminatory impact from the decision-making process. To aid with this challenge, Deep Learning (DL) which is a type of ML, creates models that simulate the way neurons interact in the brain. It departs from the traditional ML approaches in that it does not require as much data input by humans. More importantly, it is meant to produce more accurate results. 

From the seminal work on ML of French mathematician Adrien-Marie Legendre in 1805 to the team of Geoff Hinton at the University of Toronto achieving high accuracy at the ImageNet contest in 2012, it is this breakthrough in DL plus the data proliferation, coupled with improvements in hardware (computing power and storage) that has finally turned AI into the disruptive technology it is today.

Indeed, AI innovations have only been made possible thanks to exponential increases in processing power. The big shift occurred when the capabilities of Graphics Processing Units (GPUs), the high-performance chips running in video games consoles were utilised to serve computation-hungry DL algorithms. Scaling up AI, by training these massive models will then require state-of-the-art hardware and advanced physical infrastructure. 

Although these hyperscale computing powers could only be built by deep pockets in the tech industry, dominant players have made them available to the wider research community. The major actors are also willing to make their complex models accessible to SMEs, civil society, government – all for more innovation, collaboration and transparency, notwithstanding issues such as market competition and algorithm misuse. And on the subject of a trustworthy deployment, Dr Adrian Weller, Programme Director for AI at the Alan Turing Institute identifies 3 requirements as a framework that aims to:

• Identify requirements: Engage with users, practitioners, and stakeholders: What do people need? What are their concerns?
• Build requirements: Theoretical and technical foundations to build in requirements into AI systems
• Check requirements: Enforceable governance which is legally sound and technically feasible.

Thus, there are various potential challenges organisations should consider when formulating their AI strategy. With every rising tide, there are also troublesome undercurrents, so the saying goes. And from the algorithmic trade-off between accuracy and fairness to the affordability of computing power to the quality of data, AI innovations make no exception. 

In closing

Cybersecurity is one of the biggest threats organisations have to tackle in the new remote and online work. These threats are increasing in number and sophistication, with no signs of abating. Yet, intelligent automation provides a tool for mitigation.

This piece has provided an overview of the challenges and issues that derive from the implementation of AI. However, neither ignoring the risks nor pushing off advanced technologies is a sensible option in this new competitive environment where business ecosystems are forming, with partnerships and collaboration growing in popularity; and where urgent calls to foster a responsible AI are becoming ever more pressing economic and societal needs.

Having previously worked for many years with refugees and asylum seekers, I have seen several accounts and aspects of their hardships and ordeals, and it’s humbling.

The payback for volunteering is wide ranging: social connections, networking opportunities and a sense of purpose. However, since the outbreak of COVID-19, the world has lived in unprecedented times, a litmus test for leaders. It is as true in the for-profit world as it is in the non-profit.

Below is a reflection of the leadership journey at my social club. 

Finding the successes …
First and foremost, a leadership experience is and should always be one more chance to rekindle oneself with a personal vision, discovering who you really are and the desire to find or refine a purpose. Yet, the discovery is a field of experimentation and continuous improvement.

An expression of this is being authentic. In one hand, this can be exemplified by values and beliefs, but more specifically behaviours and attitudes that can influence and inspire. On the other hand, being authentic paves the way to fruitful social exchanges and mentorship when many of us feel vulnerable or face stressful and difficult situations in our personal or professional life.

Moreover, a private victory opens the route to public victories as Stephen R. Covey explains in his book ‘’7 habits of highly effective people’’. Acting as a VP Membership, you promote the club and manage the process of bringing in guests and converting them into members. As a club promoter, you are the entry point to prospects willing to join the club. In this regard, effective and timely communication was key to a successful mandate while at the same time upholding the image of the club. Converting guests into members resulted internally in building tools that could be shared with the executive committee, as well as leaning on best practices in a manner that encourages teamwork for us and accountability for each. 

… And the lessons learned.
Next to the achievements, there were also lessons learned along the way. Chief among them is the belief that leaders are born with some innate charisma. There are few leaders who certainly do have that capacity. Charisma, however, is rooted in Ethos, Pathos and Logos. As Jo Owen shows in his book ‘’How to lead’’, you do not have to be perfect. Instead, one has to learn skills and behaviors to be an effective leader.

These (soft) skills are even more important in times of uncertainty like the present pandemic. And a lot of them depend on the growth mindset as Asli Derya highlights in her publication: https://www.imperial.ac.uk/business-school/blogs/executive-education/empathy-skill-can-be-learnt/

And for someone like I who initially joined a social club to become a better communicator, the bridge between improving communication skills and leadership development turned out to be even more concrete. 

Providing feedback is also an important aspect of a leadership experience. When doing so, I often say that the essence of feedback is not to make the recipient comfortable today but to ensure that s/he leaves with material to work on and to improve tomorrow. But it is also a hidden gem because you develop active listening skills and learn how to be tactful when giving feedback.

Parting remarks
In a nutshell, the road to leadership is not a riddle, wrapped in mystery inside an enigma. Volunteers in a social club could be a link in a chain, a bond of connection between persons to do good by doing well while becoming the best version of themselves. And they should not lose sight that a leadership journey is one for growth – an opportunity to serve and to learn.