Article | June 29, 2021
When the Covid 19 Pandemic hit the world in March 2020, little did we know that it would bring the world to a standstill. When the trials of eliminating the pandemic seemed to be in vain, people started adapting to the “new normal.” Organizations from all sectors bought the best minds together to resume their functioning. Digitalization became compulsory. Thus, instead of waiting for the pandemic to end, people started finding out innovative ways to begin functioning with it.
When organizations resumed their functions, they started building flexible ways for the work to continue immaculately. From managing work from home to flexible working hours for individuals, everything was tried. Technologies in pandemic management emerged to support the changing functionalities.
1. Technology Adoption During Covid - 19
It was noted that;
● There was a 775% rise in cloud services.
● 58% of companies adopted digitalization by July 2020 (It was 36% in December 2019).
● 93% of organizations adapted remote working or collaborative technologies.
● 52% of educational institutions are operational remotely.
The above statistics are proof that technology in Covid 19 has emerged and is a catalyst for organizations to work remotely.
Let us look at some of the digital transformations and technologies that have been there for some time, but their use has been accelerated in the post covid world. And also at the emerging technologies that are here to stay for long.
2. Technologies In The Post Covid World
2.1 Generative Design
Generative Design is a manufacturing technology wherein AI & ML come together and create algorithms to produce a design and multiple iterations based on the specific requirements. When a particular design of the part is generated, an idea is fed into the system. Then the algorithm works out the best permutations and combinations of materials to be used, different designs, and specifications of the part. This assists the designers in choosing the best time and cost-saving combination.
Airbus has used generative design to build partition parts for A320 passenger aircraft. The generative design feature resulted in delivering a partition design that was 45% lighter than the previous ones.
2.2 Cloud Computing
With more and more organizations working remotely, cloud computing is on the rise. Of course, this technology has been there for a long time, but it is an accelerated technology in Covid 19.
As organizations adopted remote working, flexibility and reliability became important. Cloud services promised both at the best costs. Even small-scale businesses adopted cloud services to implement applications. Cloud services are cost-effective and easy to implement. Conferences, meetings, teaching, LMS, and work from home can be easily managed by cloud computing.
This technology in Covid 19 has seen a sky-touching rise and is here to stay for a long time.
2.3 Collaborative Tools
With work from home being so active, security of data, ease of communication, and resource management are the challenges to be handled. Organizations need tools that provide ease of access, communications, and coordination between all the departments. This is where collaborative tools play an important role in technology in Covid.
Collaborative tools assure that all the employees work on one platform. For example, communication, meetings, sales, HR, and all the departments work on one system. These tools synchronize the work of the company and make management effortless.
Microsoft has introduced Fluid Components to support their hybrid working system. Creating a one-of-a-kind meeting room experience to seamlessly streamlining all processes, fluid components will assist them in all possible ways.
2.4 Digitization of Businesses
As said earlier, the stats portray an impeccable increase in the digitization of organizations in the initial 6-month phase of the pandemic. And it increased every day. Businesses in the post coronavirus realize that an online presence is the most efficient and easy way to reach their target audience. Also, it requires limited resources.
The digital conversion of business happened in the post-Covid world, but it will not be a conversion for new companies but a compulsion for new businesses. For businesses to run profitable, technology-driven solutions are a must. This shift of businesses is guaranteed profitable and customer-centric. Digitization helps in removing geographical barriers and cater customers on a broader scale.
With the comprehensive support of AI, automation has gained momentum and promises a bright future for companies. From customer retention to generating sales, the software is developed to give an automated process. Even industries are employing AI and ML to automate their processes from manufacturing to delivery.
The automation industry was developing rapidly in the pre-Covid world but this technology in Covid 19 has seen a boom. Examples of automation are planting sensors, 3D printers, embedded metrology, etc.
China is the world leader in manufacturing due to its low labor charges, but things could change and are changing in the post-Covid world. Japanese companies have been into automation for a long time. These companies can mark their global footprints in these changing times.
IoT (Internet of Things) is a technology in Covid 19 that has gained tremendous momentum. As a result, the prices for sensors, software, and internet-connected things have gone down reasonably. IoT assists with endless possibilities to collect, transfer, and store data for a seamless working environment with minimal or no human intervention.
From home appliances to fleet management, each and everything can be managed remotely. The devices can be controlled remotely when the engineer at the other end has accurate information. IoT has proved to be a success in all sectors.
IoT has played an important role in recovering businesses while fighting the pandemic. In addition, IoT technology in Covid 19 has been implemented for smart homes, smart buildings and is paving the way for a brighter future by implementation in smart cities.
3. Advantages Of Adopting Technology In Covid 19
If you want to be a part of an industrial revolution, you need to adapt to the new ways of doing business. As the human race adapts to the ‘new normal,’ so do the businesses.
The technologies that have emerged in the post covid world promise the answer to most of the challenges. These new technologies promise a more innovative and profitable business with minimum flaws.
Here are some of the advantages of introducing the technologies in your business.
● Cost reduction, speed, and resilience
● Top-notch crisis management
● Top graded data security
● 100% customer satisfaction
● Unprecedented revenue growth
It sounds unbelievable but adopting emerging technologies does deliver more than it asks. For example, the pandemic tried to bring life to a standstill, but the alternate routes to survive proved more fruitful.
4. To Sum It All Up
Technology in Covid 19 addresses all the challenges from planning to execution. Employees are adapting quickly to the new trends as they are employee-centric. These technologies provide the necessary transparency and comfort for employees. Employers benefit as they have the best ROI, and the management of employees is no more an issue.
The adoption of technology in Covid 19 promises a brighter and more innovative future. These post covid technologies already have a host of success stories.
Thanks to the innovation of the above technologies, functions of collaboration, communication, and interconnectedness of devices are stable, continuous, and consistent. However, when all the sectors are required to work simultaneously, which is critical in moving forward, specific changes have to be implemented.
It is high time that companies accelerate their digitization process and implement the required technologies to benefit the employer and the employees.
5. Frequently Asked Questions
5.1 What technologies are used in business?
Businesses use technology depending on their operations and uses. But collaborative tools with implementation of IoT, AI, and other productivity tools are used in collaboration.
Every sector has its set of technologies to be used. So there are technologies for computers, software, networking, manufacturing systems, and more.
5.2 Why should businesses use technology?
Businesses should use technology to accelerate ROI and improve operations. Technology eases the day-to-day operations of the organization and promises minimum errors. In addition, there is productivity, transparent communication, and guaranteed security.
5.3 What are the most important types of technology?
AI is the most crucial type of technology that is groundbreaking and promising in challenging times. AI & ML, combined, can create wonders for any organization.
"name": "What technologies are used in business?",
"text": "Businesses use technology depending on their operations and uses. But collaborative tools with implementation of IoT, AI, and other productivity tools are used in collaboration.
Every sector has its set of technologies to be used. So there are technologies for computers, software, networking, manufacturing systems, and more."
"name": "Why should businesses use technology?",
"text": "Businesses should use technology to accelerate ROI and improve operations. Technology eases the day-to-day operations of the organization and promises minimum errors. In addition, there is productivity, transparent communication, and guaranteed security."
"name": "What are the most important types of technology?",
"text": "AI is the most crucial type of technology that is groundbreaking and promising in challenging times. AI & ML, combined, can create wonders for any organization."
Article | June 2, 2021
Artificial Intelligence is empowering business leaders to make better, data-driven, and insightful decisions. It has undergone several evolutions since it burst into the business scene in the 1950s, to the point where several thinkers have already painted a machine that replaces human scenarios for the future. Our view on the future of work has evolved into a zero-sum game, where the result is an either-or.
In my opinion, the view that AI will play a dominant role in the workplace is a little extreme. The fundamental assumption around AI replacing human workers is that humans and machines have the same characteristic. Totally untrue!. AI-based systems may be fast, consistently accurate, and rational, but they are not intuitive, emotional or culturally sensitive. Humans possess these qualities in abundance, and it is one of the reasons why we continue to surprise the world with our advancements.
Intuition is the Mother of Innovation
If we are living comfortable lives today, it’s because some business leaders chose their gut feeling over data analytics on numerous occasions. Some historical examples have been:
1: Henry Ford, facing falling demand for his cars and high worker turnover in 1914, doubled his employees’ wages, and it paid off.
2: Bill Allen was the CEO of Boeing in the 1950s, a company that manufactured planes for the defence industry. One day, he woke up to the idea of building commercial jets for a sector that was non-existent – civilian air travel. Allen convinced his board to risk $16 million on a new transcontinental airliner, the 707. The move transformed Boeing and air travel.
3: Travis Kalanick faced serious pushback when Uber instituted surge pricing. His move seemed to anger and alienate everyone. Travis stayed the course, and Uber modified its surge policy whenever appropriate. Now, dynamic pricing is an accepted aspect of this business and many others.
So the question is, should a competent professional trust their gut feeling or make data-driven decisions?
DATA V/S GUT
Top professionals have repeatedly confirmed that gut feeling is one of the main reasons for their success. Leadership often gets associated with quick responses in unprecedented situations and lateral thinking. Experienced leaders are not only fearless about their instincts but are also proficient at making others feel confident in their judgment. Also, going with our instinct can help us make decisions quickly and more accurately since we tend to make choices based on experiences, values, and compassion. Malcolm Gladwell calls this ‘thin slicing’ in his book, “Blink”. Thin-slicing is a cognitive manoeuvre that involves taking a narrow slice of data, what you see at a glance, and letting your intuition do the work for you. However, he does warn that some decisions are exempt from this rule; it only applies to areas where you already have significant expertise.
Artificial Intelligence and machine learning can support leaders to see complex patterns that can lead to new understanding in this fast-moving, digital era. The contention is that ‘human gut’ feeling can go hand in hand with AI – each supporting the other to achieve balanced outcomes.
A Joint Venture Between Head and Heart
Many see AI as an aid to human intelligence, not a replacement. To be one-step ahead in the AI era, professionals must learn to balance human and machine thinking. Organizations will have to showcase the ability to use the correct information at the right time and take action. It’s about using your instinct to take advantage of data and transforming that information into timely business decisions. AI is not yet ready to replace the human brain, but it has matured into an effective co-worker.
Will intelligent machines replace human workers sometime soon? I guess not. Both have different abilities and strengths. The more important question is: Can human intelligence combine with AI to produce something experts are calling augmented intelligence? Augmented intelligence is collaborative, and at the same time, it represents a collaborative effort in the service of the human race.
Figuring out how to blend the right mix with the best of data-driven deliberation and instinctive judgment could be one of the most significant challenges of our time.Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×
Article | March 23, 2020
Data-driven experiences are rich, immersive and immediate. But they’re also delay-intolerant data hogs.
Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data — quickly. So they can’t sustain latency as data travels to and from the cloud. That to-and-fro takes too long; instead, many of these data-intensive processes must remain localized and processed at the edge and on or near a hardware device.
Article | November 17, 2020
Expert cites machine learning advancements creating immediate, actionable value to drive data literacy, elevate cognitive insights and increase profitability in kind.
In today’s tumultuous business-scape amid increasingly intricate, and often vexing, marketplace conditions, curating and mining data to drive analytics-based decision making is just no longer enough. For competing with maximum, sustained impact and mitigated opportunity loss, it’s rapidly monetizing data that’s now the name of the game—particularly when spurred by artificial intelligence (AI). Indeed, emerging AI methodologies are helping forward-thinking companies achieve and sustain true agility, fuel growth and compete far more aggressively than ever before.
AI is critical as a means toward those ends and also certainly with respect to aptly predicting, preparing and responding to prospective crises as with the COVID-19 pandemic the globe is currently immersed in. In fact, Gartner recently cited the need for “smarter, faster, more responsible AI” as its No. 1 top trend that data and analytics leaders should focus on—particularly those looking to “make essential investments to prepare for a post-pandemic reset.” Novel coronavirus matters aside, Gartner underscored just how impactful AI will become, predicting that, “by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”
“To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, Distinguished VP Analyst, Gartner.
However, employing AI techniques like machine learning (ML) and natural language processing (NLP) to glean insights and render projections is simply no longer “enough” to get the job done—especially for organizations seeking to compete efficiently on a national, multi-national or global scale. Today’s organizations must endeavor toward a culture of AI-driven data literacy that directly and positively influences their top and bottom lines.
“To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain, there are a few key ways to implement and elevate machine intelligence so that it’s far smarter, faster and more accountable than protocols past,” said Microsoft alum Irfan Khan, founder and CEO of CLOUDSUFI—an AI solutions firm automating data supply chains to propel and actualize data monetization.
Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now—the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability.
Manifesting new market opportunities
Today’s machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. Present technology offers the opportunity to wholly analyze image, spoken or written inputs rather than just numerical, helping companies better find connections across these diverse data sets. This generates and maximizes value in a number of ways. Relative to the bottom and top lines, not only can it significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus.
Machine learning advancements also help companies better monetize their data and establish new revenue streams. In the above example, of course patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. This includes determining that a certain drug is only effective on woman between certain ages—critical insights for pharmaceutical developers and physicians.
Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, as are the resulting solutions, discoveries and profit-producing results thereof.
Reconcile emotions with actualities
Data generates value, which leads to the generation of money. It’s that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. There existed very rudimentary tools like regression and correlation, but today’s analytics call for gaining a true understanding of what extracted data actually means. How do you convert data into a story you can actually tell? Often, decisions are made based on emotional foundations. Leaders are using data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking, while also prompting them to ask new questions. So, garnering meaning out of a company’s own data provides tremendous advantages.
“Human nature is such that unless we can see it touch it feel it, it’s hard to understand it,” Khan says. “We as data scientists haven’t done a really great job of explaining AI-driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies.”
Scale statistical models for actionable models
We often separate our data as factuals, asserting “this is what happened.” Neural networks connect the “human decision-making process” to those factuals—a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors and such in silos. But when these multiple data sets are connected, it becomes quite evident that no two humans—or customers—are exactly alike.
Technology is now allowing us to understand trends on a factual level and then project outward. In the health realm, some companies are using this key learning to project whether or not a person is likely to suffer a certain affliction. It’s also allowing for far more efficacious “if this then what?” scenarios. If a diabetic person takes insulin controls, then their diet the treatment protocol will change. This is enabling highly personalized medicine. But the same processes, principles and benefits hold true in non-health categories as well—encompassing all industries, across the board.
Future-proof, anti-fragile data supply chains
From data connectors to pipelines; data lakes to statistical models; AI to Quantum; visual storyboards to data driven automation; ML to NLP to Neural Networks and more, there are highly effective methods for future-proofing your data value chain. The data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights.
It starts with data acquisition—garnering a wide variety and volume of data from a number of internal and external sources where data is being generated by the millisecond. Once the data is identified and ingested, it needs to brought to a central point where it can be explored, cleansed, transformed, augmented and enriched and finally modelled for use toward a purpose. Then comes statistical and heuristic modeling. These models can be of different types using different algorithms yielding different levels of accuracy in different scenarios. Models then need to be tuned and provided and environment for continuous feedback, learning and monitoring. Finally, is the visualization of outcomes—an explanation demonstrated by drawing cause-effect relationships that highlight where the most impact happens. This leads to a conclusion on how a set of problems can be solved or opportunities uncovered.
“Most organizations have some data and drive different levels of business process improvement and strategic decisions with it,” Khan notes. “However, few use data to the fullest. The right approach to data valuation and monetization can uncover limitless possibilities, including customer centricity, operational efficiency, competitive advantage, strategic partnerships, efficient operations, improved profitability and new revenue streams.”
Up to now, we have been able to write algorithms, generate immense amounts of numerical or written data and make sense of it. However, there is a significant amount of data that comes as images or voice, which has not been easy to process and manage until recent developments. The applications for the processing of visual and auditory inputs are endless. In fact, retail and finance industries have been early adopters of this technology—and with good reason. They’ve seen costs go down, engagement go up, sales increase and benefitted from other highly substantial points of monetization.
Now, a large department store can digitize their video data every night and determine that “X” amount of people saw “X” number of jeans, but they had to walk further to get to it. As a result, the department store can put those items closer to the door and walkways to determine if sales increase in kind.
Even the education realm is tapping AI-driven data. The technology is tracking retina movement to discern if kids are engaged amid the remote learning paradigm ushered in by the pandemic. They’re exploring how to measure the retina to determine whether or not a child is actually engaged in the lesson.
In radiology, they are starting to convert visual data and track it to gain a deeper understanding of digital images and video. MRIs are better able to track brain tumors—whether they are growing or shrinking and at what rate and if they are getting darker or lighter in terms of the regions. This kind of AI-driven learning is helping doctors better detect cancer and treat it more rapidly. Video data processing of the human eye can also be used to determine if a person is drunk, fatigued or even has a disease. Voice machine learning has also keenly evolved. Originally, voice recognition was being utilized to discern if a person was actually suicidal, which could be accurately predicted by inflection points in a person’s voice. Now, if that person can be captured on video, it is deemed to be about 20 times more accurate.
“All of this possibly had previously demanded a hefty price tag using systems and solutions of yore,” Khan notes. “Today, integrating multiple processes across hybrid multi-cloud environments has made data processing and analytics much more accessible and outsourceable. This negates the need for companies to purchase cost-prohibitive servers and other machine hardware.”
As one of the world's leading experts on building transparency into supply chains, Khan doesn’t just talk the talk, he’s walked the walk. As a revered marketplace change agent, he’s known for driving business transformation and customer-centric turnaround growth strategies in a multitude of environments. In addition to engineering partnerships with MIT, Khan has successfully led organizational changes and process improvement in markets across the Americas, Europe, Middle East and Asia.
“New AI solutions and trends will eliminate patchwork processes that cause data, and interpretations thereof, to get lost in translation or, even worse, remain entirely undiscovered,” Khan says. “Next-Gen platforms are solving such problems by executing all functions required to create and govern AI products— single-source systems that pull data, transform, model, tunes and recommend actions with cause-effect transparency.”
For niche players, today’s leading-edge AI technology also aptly provides for vertical industry specialization. “Emerging solutions enable common data models, compliance and interoperability requirements that, in turn, accelerate model validation, refinement and implementation that’s specific to a given sector or marketplace,” notes Khan. “All of this ultimately drives speed to insights on previously unsolved problems, which reveals untapped opportunities and automates workflow integrated cognitive solutions.”
“Overall, AI is ushering in a new and more sophisticated era of data literacy,” he continues. “It’s a new paradigm founded on automated, comprehensive and holistic data discovery, which is fostering elevated cognitive insights and actionable strategies that positively impact the top and bottom line.”
Perhaps the future mandate for AI should not only focus on becoming smarter, faster and more accountable than predecessors, but actually bridge the gap between human intuition and data-backed decisions. Doing so will assuredly advance an organization’s ability to transact with utmost trust.