The new paradigm of Artificial Intelligence (with ChatGPT)

ChatGPT-The new paradigm shift

Network Science March 01, 2023

The phenomena!
2022 was a watershed year in evolving the way people interacted with Artificial Intelligence. DALLE-2 captured the imaginations of everyone in the world at a scale which would have made its namesake painter proud. ChatGPT wrote movie scripts, party planning ideas, code and explained millions of concepts to people without having to read 10 websites for understanding. In 4 months, the world found alternatives for Google search, photoshop, Stack Overflow, and script writing. I personally used some of these tools to get inspirations for pitch decks and feature sets for enterprise technologies and occasioned an interesting conversation between Shakespeare and Douglas Adams in the style of their characters.
This excitement goes beyond acceptance of new paradigms of search and creation. Proof of its popularity is evidenced by its incredible rate of adoption which till last count was about 100 Million monthly active users just on ChatGPT. The insanity compounds with the enormous flow of investments from Microsoft in Open AI with an aim to disrupt Google search. To keep pace with the new paradigm, Google has backed Anthropic as its own horse in the race.

Source:https://www.statista.com/chart/29174/time-to-one-million-users/
Shifting paradigms
Though the introduction of Artificial Intelligence on a global stage is not new, it feels a little different from what the past has shown us. In the age of AR/VR, we saw companies focusing bringing AR and VR to the forefront. Similarly, with blockchain and drones, the technology was at the center of the value proposition. However, AI/ML was playing the old sage, hiding its hard work behind an outcome that many believed could have been done with some basic coding and hard work themselves. In engaging with a website chatbot, or outputs from a predictive engine, users often viewed the output of complex analysis as something that can be created with enough time on excel or by searching hard enough. However, moving to generic text and image has changed that. AI’s abilities seem closer, and at times beyond, finished products of professionals. Search is not about narrowing your universe anymore, but finding exactly what you are looking for. Likewise, creation is now focused on bringing fantastical ideas come to life where the limiter is your ideas. This paradigm shift is powerful beyond what we can comprehend today. What seems like joyous tools to complete your job swiftly can open paths of infinite possibilities leading to democratised creation.

The Tomorrow
Today tools such as ChatGPT or Chatsonic are giving you the scaffolding around complete solutions which can be easily configured or edited to your designed results. However, getting more contextual results and providing the ability to ad-hoc dynamically will move the usage of these tools from starting points to being complete solution suits. Social media is already flooded with bundled AI tools for advertisers, marketers, designers and more. In the coming weeks, we will see more specialised uses of AI bundling. This will lead to a complex network of AI’s talking to each other possibly through a single interface. I know this might sound a bit like steps towards Terminator coming back to kill Sarah Conner, but till that happens, this is good news for creation. Such paradigms of AI can expand the scope of who we call creators. Lack of specialised software skills will not equate to a lack of quality. Tomorrow a diligent college student might be able to win an Oscar, or a grandmother set up a sustainable D2C brand. I obviously wax lyrical to a fanciful future, but stranger things have happened.
Even if I don’t think robot from the future is likely, it doesn’t mean there are not concerns regarding this new paradigm. This form of AI will limit growth of millions of medium to even high skilled jobs. There is a growing notion that many jobs will be replaced by skilled people who know how to use AI rather than AI’s on their own. There is truth to the notion, however one does question the value you would assign to expert work. There are other issues such as biased training, questions around ownership, ethical dilemmas, lack of transparency, and privacy concerns, but each of these points deserves an in-depth study on it’s own.

The Conclusion
Artificial Intelligence is here to stay and its adoption will have good and bad consequences. However, arming yourself with the right skills to harness the potential seems like the best course of action, at least in the near term. AI is already disrupting my job by getting me access to specialised information for which I would have to spend a couple of hours studying. However, using it without adding my experience and knowledge will also lead to my downfall. After all, DALL-E 2 can make beautiful paintings in the style of Eduard Munch, but, at least today, it can’t convey the raw inner turmoil behind The Scream. That still needs some ‘human’ editing.

The Scream by Edvard Munch

ChatSonic prompt: Make a painting depicting anxiety and inner turmoil in the style of Edvard Munch

This blog has been contributed by Nikhil Mahen, Deep Tech Consultant at Network Science. You can write to Nikhil at nikhil@networkscience.ai for any queries regarding this blog.

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Activate and optimize content dynamically across channels and platforms using the Tenovos-native content distribution network (CDN).

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How does Big Data Analytics help in constant innovation?

Network Science February 08, 2023

What are DAM Solutions?

DAM solutions are platforms that help organizations manage, store, and distribute their digital assets such as images, videos, audio files, and documents. These solutions typically include features such as metadata management, search and discovery, access control, and analytics.

What is the Market Size for DAM?

    • Global:
      The DAM solution market size is expected to grow significantly in the coming years. According to a market research report by MarketsandMarkets, the global Digital Asset Management market size was valued at $3.4 billion in 2020 and is expected to reach $7.4 billion by 2025, growing at a compound annual growth rate (CAGR) of 17.8% during the forecast period.
      The increasing adoption of digital technologies, growing need for efficient management of digital assets, and rising demand for cloud-based solutions are some of the key factors driving the growth of the DAM market. Additionally, the increasing need for compliance and security, as well as the growing adoption of artificial intelligence (AI) and machine learning (ML) in DAM solutions, are also expected to boost the market growth.
      The market is segmented into solutions, services, deployment models, organization size, verticals and regions. The solutions segment is further divided into solutions such as digital asset management, video management, and digital rights management. Services segment is divided into professional services and managed services. Deployment models are segmented into on-premises and cloud.
      North America is expected to hold the largest market share, while Asia Pacific is expected to grow at the highest CAGR during the forecast period.
    • India:
      In India, the market for Digital Asset Management (DAM) solutions is growing as more organizations recognize the need for efficient management of their digital assets. Industries such as media and entertainment, advertising, and manufacturing are some of the major adopters of DAM solutions in India. DAM is a very relevant tool for the Pharmaceutical & CPG industry where digital asset creation and management is an extensive requirement. The market is also driven by the increasing use of digital channels for marketing and the growing demand for content creation and distribution in India.
      There are a number of companies that offer DAM solutions in India, including global players such as Tenovos, Adobe, Widen, and Bynder, as well as Indian companies such as InData Labs, PixelVault, and Webkul. These companies offer both on-premise and cloud-based solutions, catering to organizations of different sizes and industries.
      However, the market for DAM solutions in India is still relatively small compared to more developed markets such as North America and Europe. This is likely to change in the future as more organizations in India adopt digital technologies and recognize the need for efficient management of their digital assets.
      With the awareness about DAM still being relatively lower in India, adopting it can give a unique First-Mover advantage to a lot of organisations in this competitive market.

      • Competitive Analysis:
        According to a study by Forrester conducted in early 2022, Aprimo, Adobe, Widen, Bynder emerge as the long-established market leaders for DAM globally. However, the above solutions are not very cost effective and aren’t the first choice in the Indian market.
        There is a classification of other “Strong Performers” which are newer, intuitive solutions driven by AI and ML. These include the likes of Tenovos, OpenText and Sitecore, which have a decent market share and are also easier on the pockets for clients. These are solutions which have proven more acceptable in the Indian Market.

Why Use DAM?
Digital Asset Management (DAM) solutions can provide a variety of benefits in terms of return on investment (ROI), including:

  1. Increased efficiency: DAM solutions can help organizations streamline their workflow, reducing the time and effort required to find, retrieve, and use digital assets.
  2. Improved collaboration: DAM solutions can make it easier for teams to share and collaborate on digital assets, resulting in more productive work and faster time-to-market.
  3. Better asset utilization:DAM solutions can help organizations more effectively use their digital assets, leading to increased revenue and reduced costs.
  4. Enhanced security: DAM solutions can provide robust security features that help organizations protect their digital assets from unauthorized access or use.
  5. Improved brand consistency: DAM solutions can help organizations maintain consistent branding across all their digital assets, resulting in a more professional and cohesive image.
  6. Better tracking and reporting: DAM solutions can provide detailed analytics and reporting on asset usage and performance, which can help organizations make more informed decisions about their digital assets.

Overall, a well-implemented Digital Asset Management (DAM) solution can lead to increased productivity, improved collaboration, reduced costs, and enhanced security, all of which can contribute to a strong ROI.
In our bid of “Changing the World with DeepTech Innovation”, Network Science, is proud to add another solution in our portfolio. Digital Asset Management (DAM) opens up avenues to collaborate with previously unexplored Industry Verticals: Advertising, Media and Entertainment.

This blog has been contributed by Swapnil Bang, Deep Tech Consultant at Network Science. You can write to Swapnil at swapnil@networkscience.ai for any queries regarding this blog.

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mobilous-Network Science

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Network Science

How does Big Data Analytics help in constant innovation?

Network Science July 25, 2022

The concept of big data has been around for years; most organisations now understand that if they capture all of the data that flows into their businesses (potentially in real time), they can apply analytics and derive significant value from it. Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from diverse, high-volume, high-velocity data sets. These data sets may come from a variety of sourcesoffice365 torrent, including the web, mobile, email, social media, and networked smart devices. They frequently contain data that is generated at a high rate and in a variety of formats, ranging from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files).

Big Data Analytics Processes

By analysing data sets, analytics solutions gain insights and predict outcomes. However, before the data can be successfully analysed, it must first be stored, organised, and cleaned by a series of applications in a step-by-step preparation process:

  1. Gather Data: Every organisation’s approach to data collection is unique. With today’s technology, businesses can collect structured and unstructured data from a variety of sources, including cloud storage, mobile apps, in-store IoT sensors, and more. sketchup crack 2018 itaSome data will be stored in data warehouses, where it will be easily accessible by business intelligence tools and solutions. A data lake can be used to store raw or unstructured data that is too diverse or complex for a warehouse.
  2. Process Data: Once data has been collected and stored, it must be properly organized in order to produce accurate results on analytical queries, especially when the data is large and unstructured. Data availability is increasing at an exponential rate, making data processing difficult for organizations. Batch processing, which examines large data blocks over time, is one processing option. When the time between collecting and analysing data is long, batch processing comes in handy. Stream processing examines small batches of data at once, reducing the time between collection and analysis and allowing for faster decision-making. Stream processing is more difficult and frequently more expensive.
  3. Clean Data: To improve data quality and get stronger results, all data must be formatted correctly, andkms activator any duplicate or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, resulting in flawed insights.
  4. Analyse Data: It takes time to convert big data into usable information. Once ready, advanced analytics processes can transform big data into big insights. Big data analysis methods:
    1. Data mining searches large datasets for patterns and relationships by detecting anomalies and forming data clusters.
    2. Predictive analytics makes future predictions based on an organisation’s historical data, identifying upcoming risks and opportunities.
    3. Deep learning mimics human learning patterns by layering algorithms and finding patterns in the most complex and abstract data.

Different types of Big Data Analytics

  1. Diagnostic Analytics: Diagnostic analytics is an advanced type of big data analytics that can be used to investigate data and content. Using this type of analytics, you can answer the question, “Why did it happen?” So, by analysing data, you can understand the reasons for certain behaviours and events related to the company you work for, their customers, employees, products, and so on.
  2. Descriptive Analytics: Descriptive analytics is one of the most common types of analytics used by businesses to stay current on current trends and operational performance. It is one of the first steps in analysing raw data by performing simple mathematical operations and producing statements about samples and measurements. After identifying trends and insights with descriptive analytics, you can use other types of analytics to learn more about what causes those trends.
  3. Prescriptive analytics: Prescriptive analytics uses descriptive and predictive analysis resulcs6 master collection serial number generatorts to find solutions for optimising business practises using various simulations and techniques. It employs data insights to recommend the best course of action for the company.

Why is Big Data Analytics so important?
Big data analytics assists businesses in harnessing their data and utilising it to identify new opportunities. As a result, smarter business decisions, more efficient operations, higher profits, and happier customers follow. Businesses that leverage big data and advanced analytics gain value in a variety of ways, including:

  1. Cost-cutting measures: When it comes to storing large amounts of data, big data technologies such as cloud-based analytics can significantly cut costs (for example, a data lake). Furthermore, big data analytics assists businesses in finding more efficient ways to conduct business.
  2. Making faster and more accurate decisions: The speed of in-memory analytics, combined with the ability to analyse new data sources, such as streaming data from IoT, allows businesses to analyse information quickly and make informed decisions.
  3. New product and service development and marketing: Analytics enables businesses to provide customers with what they want when they want it. With big data analytics, more businesses can develop innovative new products to meet the changing needs of their customers.

Big data analytics can ultimately fuel better and faster decision-making, model and predict future outcomes, and improve business intelligence. A scalable analytics solution can benefit any organisation that works with large amounts of data, which is why many major industries, including retail, entertainment, and healthcare, already use big data to establish strategy, lower costs, and predict customer needs.

Unleashing Machine Learning: The Rationale Behind Smart Predictions

Network Science July 11, 2022

Machine Learning (ML) & Artificial Intelligence (AI) albeit recent technologies, are by no means new. Historically, machines have been used for several tasks and functions that were otherwise deemed as trivial and dirty for humans. Over time, these machines have evolved to take on more complex & sophisticated activities including decision making and strategy formulation. However, as complexities increase, there is a growing need for people to trust in the power of these machines rather than question their abilities.

A recent study by Accenture Labs has revealed that Machine Learning, especially deep learning, is quickly seeing an upsurge in its adoption in workplaces across industries. In healthcare, for instance, hundreds of companies are using Machine Learning algorithms and predictive analytics to reduce drug development time and diagnose ailments from medical images. Similarly, in the transportation sector, self-driving cars using ML are expected to become a norm within the next couple of years, with commercial applications of these automobiles being close behind.

Machine Learning Algorithms Industrial Uses

What is Machine Learning?

These intelligent systems take on low-level pattern recognition tasks like image recognition, speech recognition and natural language processing to help companies churn large volumes of data for making specific recommendations. ML allows software systems to provide users with accurate predictions with minimal uncertainty.

The internal algorithms involved in this decision-making process, however, are often not visible to company personnel, making ML systems operate as ostracized “black boxes”. This makes organizations unwilling to allocate core competencies to machines due to higher risks of poor decision making and related costs.

Research indicates that in the upcoming years, machines will be compelled to explain their reasonings and recommendations in a deeper manner. As the next stage of human augmentation by machines, this interaction will enable people to understand and act responsibly. It will work towards creating an effective team between humans and machines.

Source – https://www.statista.com/chart/17966/worldwide-artificial-intelligence-funding/

Machine Learning Synergy

Intelligent systems powered by ML are now here to work alongside their human counterparts. By utilizing smart machines for responsibility, fairness, and transparency, organizations can enforce collaboration & efficiency within their workplaces. These advanced intelligent systems of the future, however, will not replace people. They will complement and support humans in a manner that allow businesses to make smarter, better and more accurate decisions.

There are 3 main market drivers for advanced ML-led systems. First, the growing need for transparency, as required by laws such as the EU’s GDPR, makes it essential for companies to disclose how personal data is being used for selection and other decision-making. Second, a growing need for trust between AI and human beings mandate that systems are able to effectively explain the rationale behind their decision-making. Third, the need for better machine-human synergy. With machines being better at recognizing minute patterns in large volumes of data and people being more efficient at connecting the dots among high-level patterns, the businesses of tomorrow are going to increasingly need both resources working hand-in-hand.

So, how exactly can Machine Learning convey the rationale behind outputs better? Research has identified three different methods to achieve this:

    1. Data-level explanation – Through this method, ML-based systems can provide evidence of the modeling and its results using comparisons made with other examples. This allows the system to justify the decision taken around any particular issue or targeted prediction.
    2. Model-level explanation – This approach focuses more on the Machine Learning algorithms. Through this method, the explanation provided makes the logic more understandable to humans by adding a layer of domain knowledge on top. Compared to the other methods, model-level explanation abstracts most from the data through rules or by combining it with semantics.
    3. Hybrid-level explanation – This approach works the best and is most useful if the data being studied is particularly large, complex or packed. The method uses a high level of abstraction by refactoring data at a metadata level. Rather than using the data as an evidence as in the case of other methods, the hybrid-level explanation offers explanation for every feature at a metadata level.

Enhanced ML will allow sophisticated systems to:

      • Explain the reasoning behind their results and how they arrived at them.
      • Characterize the system’s strengths & weaknesses.
      • Compare their performance & output with those of other intelligent machines.
      • Convey results in a comprehensive manner that showcases the potential of future technologies.
      • Make the decision-making process in businesses smarter.

Why Zero-Code App Development is important?

Network Science June 28, 2022

We are powered by code in our daily lives. Programming is what allows us to do things like check our bank accounts, like our friends’ photos on social media, and shop for new clothes on our favorite e-commerce sites. But what if we told you that you didn’t have to write any code to create such apps? It is entirely possible to accomplish this using Zero-Code/No-Code application development platforms.

So, what is Zero-Code or No-Code? Zero-Code is a technique that allows you to create software, web applications, smartphone apps, and other applications without knowing how to code. You can create your ideal software by dragging and dropping readymade/pre-existing building blocks. What distinguishes no-code is the ability to automate processes, digitize operations, and bundle them into everyday apps without manually writing code.

No-code is simply an abstraction layer that sits on top of code. It translates the fundamentals of code into simple drag-and-drop solutions, allowing creators to build modern apps and websites visually. AppExe, by Mobilous, is an AI-based Rapid Mobile Application Development (RMAD) platform that provides all of the functionality of HTML5, CSS, and Javascript, but you don’t need to know any of these programming languages to get started. Where once only skilled programmers could create apps and launch web applications, no-code development platforms and a wealth of tutorials can get anyone on their way to getting their ideas out there. Being a non-programmer is no longer an issue.

Why is zero-code app development the future of application development?

    • Drag-and-drop interface: The ease of use is one of the primary reasons why no-code development platforms have gained popularity and expanded rapidly. This is primarily due to the fundamental feature of drag and drop. You can simply drag and drop the feature you need to create for your application using this feature. It allows you to complete the task quickly.
    • Data connections: Most no-code application development platforms either come pre-configured with database and server-side software or provide a simple user interface for connecting to your preferred database. A good no-code platform not only assists you in developing user-facing visuals but also in developing data management and processing capabilities that run behind the scenes to assist you in completing your business workflows.
    • User Interface Builder and Visual Modeling: Most no-code platforms include a user interface builder that allows users to quickly assemble preconfigured elements to create a website and application. To create functionality in no-code platforms, simply drag and drop components in a logical sequence. Basically, no coding is required; everything has already been created or visually modelled; all you have to do is drag, drop, and arrange!
    • Integrations: Every day, the world becomes more connected. Most businesses use a variety of applications and seek connectivity in order to improve and automate collaboration and workflow. Most no-code platforms provide a number of integrations with other software, allowing you to connect your data and processes without having to duplicate or perform manual work. API integrations allow you to easily integrate the apps you create with a no-code platform with a variety of web applications such as YouTube, Google Maps, WhatsApp, Slack, Twitter, and others.
    • Reduced cost: Developers are not cheap. No-code solutions allow you to avoid this, removing much of the overhead associated with keeping a skilled team of developers on staff at all times. In the long run, you can build apps faster and cheaper.
    • Easily changeable: The issue with traditional hand coding is that you can’t change functionality or feature at the drop of a hat, especially if you’re coding in a foreign language. You can do it without a code. If something needs to be changed, you simply implement new logic and have your change ready in a matter of hours.

With no-code development, Organisations can create a plethora of internal and external-facing tools without involving IT at all. There is no maintenance burden, no additional IT overhead, just pure productivity and hopefully a bit of fun building it. Businesses that prioritise agility and simplicity over other factors are the best candidates for no-code platforms. They can begin developing new applications in minutes with little or no customization to complete their tasks.

What is Deep Tech and why it is a catalyst for change and innovation?

Network Science June 14, 2022

A great wave of innovation has come in the technological world and the aspect that is riding this wave and going to greater heights is Deep Tech. So, what is Deep Tech? Deep Tech can be defined as the technology behind tangible engineering innovation through scientific discoveries. Its primary goal is to pioneer new solutions to society’s most pressing problems, such as chronic disease, climate change, clean energy, and food production. Deep Tech firms differ from traditional tech firms in that technologists and scientists typically work together to achieve a common goal. We can set Deep Tech apart from other technological innovations because it acts as a catalyst to change and profoundly enables advancement.

Many Deep Tech companies are currently attempting to establish themselves as innovators and succeed. What distinguishes these companies is that they focus on fundamental and defensible engineering innovations rather than incremental delivery of standardised technologies or solely on business model innovation to create opportunities.

Deep Tech spans various technologies on the tech front and can impact diverse applications. This often includes processing and computing architecture innovations, artificial intelligence and machine learning, vision and speech algorithms and techniques, advances in semiconductors and electronic systems, power electronics, haptics, and more. Let us take a look at the various technologies that Deep Tech comprises and how they are impacting innovation.

Deep Tech

    • Artificial Intelligence

AI is a broad tool that allows people to reconsider how we integrate information, analyse data, and use the insights gained to improve decision-making. Wearables powered by AI can help patients keep track of their health problems. The automotive industry is gradually transitioning to a tech-driven culture by adopting and implementing AI. AI fraud detectors, applications, and techniques have been implemented to detect abnormal traffic and prevent fraudulent practices.

Machine Learning (ML) transforms traditional computing by allowing machines to learn from data. All of these voice assistants use Natural Language Processing (NLP), which is powered by a machine learning algorithm. Dynamic pricing employs ML technologies to alter the price dynamics of taxis, products, and services. Machine learning is critical in providing security at large gatherings. The technology can help avoid false alarms and detect things that human screener might miss.

Platforms powered by blockchain enable unified secure access to cloud, on-premises, and connected IoT devices. The use of blockchain technology in the media industry has the potential to reduce piracy while also improving content security. Blockchain technology enables real-time, lightning-fast transactions. This has already benefited the BFSI sector by saving time and money. Blockchain can help to eliminate security bottlenecks by safeguarding intellectual property and streamlining project management.

    • Internet of Things

Technology-enabled security solutions are specifically designed to protect the Internet of Things’ connected devices and critical infrastructure. Factories use IoT-enabled machines with sensors, which makes mapping machine workloads, inputs, and outputs simple. Asset management powered by IoT improves real-time visibility of assets and assists businesses in optimising their resources. Public energy grids can be optimised using IoT-enabled machines to balance workloads, predict energy surges, and more equitably distribute energy to customers.

AR is a technological advancement that can help businesses gain more customers and improve overall sales and retention by giving businesses a competitive advantage when engaging customers and acquiring leads. From interior design to architecture and construction, augmented reality is assisting professionals in visualising their final products during the creative process. AR-enabled wearables in manufacturing can help measure changes, identify unsafe working conditions, and visualise design components and structures. AR technology has the potential to improve the depth and effectiveness of medical training in many areas, from operating MRI machines to performing complex surgeries.

    • Robotics

Robotics is used because of their high precision, speed, dependability, quality, repeatability, and low operating cost. These robots are programmed to carry out specific tasks. Robots can help to make repetitive tasks easier, such as assembly lines in a factory or collecting large amounts of mundane data. Cobots (robots that work alongside humans) can help call centres streamline operations and handle high volumes of incoming phone or internet traffic, ensuring that communication channels remain open. Robots are force multipliers that have the potential to dramatically transform heavy industries such as equipment manufacturing. They can lift heavy objects more safely and quickly, and they can work without stopping.

Successful Deep Tech ventures assemble multiple talents (including scientists, engineers, and entrepreneurs) to unravel a controversy. They often develop brand-new technologies because no existing technology fully solves the matter. Deep Tech harnesses cutting-edge technologies to form tangible societal shifts, and never has it been more relevant. The world pandemic, the urgency of the climate crisis, and therefore the rapid expansion of worldwide populations has placed added strain on already fragile systems, and it’s these fundamental issues that deep tech is meant to deal with.

Leadership Perspective on Navigating Crisis

Network Science April 26, 2022

In this edition of the co-create.ai series, Nahla Khaddage Boudiab, COO at AM Bank engages in an intriguing conversation with Robert Webb, Advisory Board Member , Network Science and Founder at TBM partners about leadership perspective on navigating crisis. Nahla is currently based in Beirut, Lebanon, and prior to working with Al-Mawarid Bank, she was also partnered with consulting firm, EY.

With the onset of a health crisis like the Coronavirus, coupled with the economic and political crises in Lebanon, stress levels in the country were mounting. As a dollarized economy, when Lebanon started facing a lack of availability of cash, citizens experienced its adverse effects strongly. Nahla outlines how the leaders in the banking industry navigated such difficult times and accusations by users who felt that the banks were to blame for this problem.

  • Ensure that you are not adding to the existing pressures of your employees
  • Leadership must be focused on keeping the organization and all its members healthy and pumped with good energy that allows achievement of objectives
  • From an organizational perspective, culture is the immune system. Leadership skills must be honed to keep this culture healthy and thriving

Transformational Leadership Manifesto for Emerging Leaders
AM Bank has been awarded the title of the “Strongest Bank in Overcoming Crises” by the International Union of Arab Bankers. Nahla highlights that the key success factor for AM Bank has been its recognition that, “leadership can no longer be managing robots. Leadership can no longer be the solution providers…because there is no way one individual will have enough ideas or energy to actually provide solutions for all the risk doors…”

  • It is important to break biases from people’s minds regarding what characteristics make up a “good leader”
  • Leadership is less about power and control and more about trust, care and love
  • The most important leadership skill today is building resilience. Leaders must harness the powers of team members and direct them toward the same organizational goals
  • Don’t separate the ‘person’ from the ‘professional’. Address the one set of needs that a individual holds irrespective of the environment

If organizations focus more on things like discipline and rules, leaders will be able to draw out only around 50% of an employee’s full potential. On the other hand, if the focus is on human needs, compassion, trust and love, it can be possible to get even 150% from your employees. Innovation can only happen in a firm that practices love and compassion.
Spirituality in the Organization
“If you enable feelings of spirituality in the organization, you are going to improve drastically,” says Nahla. Here, the spirituality of the individual refers to their sense of belongingness—to the organization, to the world, to the universe, as one integrated life. To achieve this, there needs to be certain critical processes in place:

  • The structure of the organization – In order to have technical excellence and optimization of resources along with compassion and care, as a leader, you need to ensure that you have visibility of what the organization looks like. A layered structure makes this difficult. A flat structure is conducive to achieving this objective.
  • The recruiting strategy of the organization – While technically excellent people are abundant, the ability of an employee to contribute to the culture of the organization is what makes a difference. The leadership skill of recruiting the right people is extremely crucial, especially if these people have the ability to influence others. If mistakes are committed, the organization must attempt to fix them.
  • The Evaluation – Leaders have to understand that systems and processes can often trigger certain human behaviour inside the organization. Before evaluating managers, leaders should assess whether any processes are leading to negative behaviours. Evaluations should be focused on triggering positive behaviours and encourage training, nurturing and collaboration in the organization.

In a love-compassion culture, transformational leadership involves having conversations with employees that are unhappy or feel dissatisfied. It is important to drive the message of teamwork and unity for such a culture to survive. If any negative behaviour is repeated, a more formal conversation should be undertaken to make the employee understand the repercussions of such continued action.

Advice for Young Leaders

  • Any successful leader is one who has put their ego aside. Excessive pride can often become a primary cause of failure in medium or large organizations.
  • Remove the mindset of “I don’t know how to do this” or “This is how we’ve been doing things” by being yourself. Leaders need to stop focusing on how to control others and instead become unafraid of doing new things.

Supply Chain Disruption in a Post-Pandemic World

Network Science
February 17, 2022
Written by Nicolas Weatherill

Disruption is inevitable.

Indeed, the last few years have been a challenging and highly disruptive time in the logistics industry. The COVID-19 pandemic quickly highlighted pre-existing vulnerabilities within international supply chains, with various national lockdowns and restrictions leading to a global shortage of raw materials and goods that significantly reduced manufacturing output, in turn putting increased pressure on supply chains as firms began to seek alternative suppliers (and subsequently) logistical solutions.

Companies operating in the life-sciences sector saw business boom (the largely essential status of their goods creating a market in which supply was largely able to keep up with demand due to an overall shift in national priority toward the delivery of those essential products), however business slowed in most other sectors – particularly in areas such as construction and retail, as workplaces shut down and people retreated into their homes.

The Perfect Storm

In the UK in particular, the stresses on supply chains brought by the pandemic were only further exacerbated by Brexit (an ongoing process predating the pandemic), particularly at a human/individual level, with companies already struggling to operate with reduced staffing as a result of the need for many to self-isolate. Prior even to the pandemic, the UK was estimated to be facing a shortfall of around 76,000 drivers (source: Logistics UK), however this specific workforce issue was largely negated due to access to pools of workers from the EU – which incidentally saw an increase in driving staff of 450% between 2010-2017.

Access to this pool of EU drivers subsequently became a vital lifeline in the UK, subsequently damaged irreparably by Brexit as many of these drivers chose not to pursue settlement in the UK in favour of higher wages in mainland Europe. One of the defining moments of this crisis came during the much-publicised fuel shortage in 2021, with the British Army being drafted in to assist with the distribution of petrol supplies from port to pump.

As well as the workforce shortages, Brexit also led to broad increase in workload across the UK logistics space as new customs procedures and requirements were suddenly enforced. The terms of our exit from the EU were at best vague as negotiations with Brussels wore on, and its last-minute ratification and publication resulted in a scramble for logistics companies to make sense of new export rules and regulations and put in place solutions to ensure continued cross-border operation.

This represented a huge challenge to the logistics sector, as companies of all sizes struggled to comply with complex import/export paperwork and regulation existing where there had previously been little-none – with many companies having to invest in teams of customs agents, and quickly creating often overwhelmingly manual processes in order to cope with the change.

The perfect storm created by both Brexit & the Covid-19 pandemic resulted in the Logistics Industry moving towards the forefront of public attention – with companies such as Amazon cutting their commitment to next-day deliveries, supermarkets displaying empty shelves, and demand far outstripping supply across almost all sectors.

While we should all be proud of how the Logistics Industry quickly adapted to help the national effort against Covid 19, it is however clear that the industry at large was unprepared for the significant problems faced in the last few years, and the consequences of an overall failure to negate the disruption have never been so apparent.

Disruption vs Disruption

More than anything else, the last few years have proven that technology and innovation are fundamental toward creating supply chains that are resilient and supple, rather than fragile and brittle.

There is far too much reliance on antiquated processes, often involving an unnecessary amount of manual/human input. Data streams are too often segregated from one another, with siloed systems working in isolation from one another, rather than in tandem. Paperwork existing only in physical form, despite our existence in an increasingly digitised world.

The combined disruption to business caused by the perfect storm of Brexit and the Covid 19 has however resulted in a surge in technology investment, with tech investment growing by 230% in the UK in 2021, with the UK also accounting for more than a third of all tech investment in Europe: which at least demonstrates that most businesses have realised that they would rather be proactive rather than reactive in the face of future challenges, whatever they may be.

Tech start-ups, leveraging the power of Deep Technology, will play a critical role in the innovation renaissance currently underway in the UK, and particularly in Supply Chain.

Advanced AI can be harnessed to provide intelligent and powerful data analytics, converting the quantifiable into qualitative, and vice versa. Machine learning can be used to steer systems, resulting in increasingly effective and smart IT infrastructure. Robotics implemented in warehousing and transportation, reducing the need to rely on human operators and in turn allowing you to better utilise your staff.

Deep Tech represents a new frontier in the innovation world, and the possible applications of these technologies stretch as far as the imagination. At its core, Deep Tech is disruptive – built to deliver meaningful and high impact change, rather than shallow innovation that can only produce incremental improvement without ever addressing the fundamental flaws in a traditional business process.

We need only look to the example of Biontech – pioneers of the ground-breaking MRNA vaccine – to see the value we can all gain from disruptive deep technology: it’s very likely to have saved either your own life or the lives of those around you.

With the benefit of hindsight, investment in technology and innovation has never been more critical to the future success of supply chains, and their ability to adapt to an ever-changing world. Logistics firms now need to choose between securing their place in the supple supply chains of the future or becoming ever helpless in the crisis of tomorrow.

Choose the right kind of disruption. Choose Deep Tech disruption.