The only thing constant about technology is change. Every new technological wave has forced change upon society, industries, and people. This change has been best embraced by people who have exhibited the mindset to learn, unlearn, and relearn. This time around, we have Gen AI. It’s not just about AI; it’s about a symbiotic relationship between humans and technology, a paradigm shift from competition to collaboration, from fear to opportunity.

Gen AI’s capability to handle routine tasks has sparked optimism, raising the question of potential mass human replacement at entry-level tasks. This is true of not just one sector or domain but across the board. Wherever there exists a job whose characteristics comprise repeatability, lack of creativity, and space for human error to creep in, they are prime candidates for AI to take over.

Take, for instance, the banking sector. Organizations use AI algorithms to streamline mundane tasks like data entry and verification in the banking sector, freeing up human talent to focus on nuanced customer interactions and problem-solving. AI-powered automation transforms the banking industry, enabling financial institutions to stay competitive, adapt to evolving customer needs, and deliver exceptional services.

Gen AI isn’t just about automating tasks; it’s about creating new opportunities. The growing field of AI ethics requires the human element to help AI navigate to the morally right decision. Integrating ethics into artificial intelligence-based programs is crucial for preventing negative outcomes such as privacy breaches and biased decision-making¹. Human-AI teaming (HAIT) presents additional challenges, as the ethical principles and moral theories that provide justification for humans are not genuinely appreciated by machines. This emerging discipline requires human intellect to define ethical frameworks within technology.

Generative AI holds the potential to significantly boost labor productivity by opening the doors for novel career opportunities. Organizations that will win in the talent marketplace are those that create a compelling value proposition using both their human talent and the tech prowess that Gen AI brings to the table.

Creativity and innovation, once considered solely human attributes, are now getting a Gen AI makeover. AI algorithms are boosting human creativity. They are offering fresh insights into music composition, design, and even content creation. Professionals in fields such as education, law, technology, and the arts are likely to see parts of their jobs automated¹.

AI’s predictive abilities are remarkable. They are being used in marketing to analyze consumer behavior, identify trends, and pinpoint potential opportunities and threats. Amazon’s recommendation system is a prime example of AI predicting consumer preferences with remarkable accuracy. The system uses existing user insights, such as past interactions and interests, to make personalized recommendations. In 2021, about 35% of all sales on Amazon happened via recommendations, highlighting the power of AI prediction.

Gen AI promises to revolutionize learning by shattering the one-size-fits-all approach. Think of a classroom where each student’s learning journey is hyper-personalized. Content adapts to the learner’s pace and preferences. Adaptive learning platforms like Duolingo are already pioneering this change, customizing lessons based on individual proficiency.

Moreover, Gen AI transforms learning into an absorbing and interactive experience. Virtual reality (VR) and augmented reality (AR) are reshaping education, making complex concepts tangible and immersive. Students can dissect a frog or explore ancient ruins, all within the confines of a classroom.

Generative AI could annually add the equivalent of $2.6 trillion to $4.4 trillion across various use cases, marking a substantial increase in labor productivity². As we see the transition to a full knowledge economy, we will tend to see the demand for a redefinition of the workplace.

Here are five ways in which today’s workforce can redefine their work dynamics to leverage Gen AI.

Gen AI isn’t just a technological marvel; it’s a paradigm shift. It’s about humans and technology complementing each other—a fusion that unleashes unprecedented potential. It’s about a future where learning isn’t confined by boundaries, where work transcends routine, and where innovation thrives in diverse, collaborative ecosystems.

The journey ahead is exciting, and at Infosys, we’re pioneering this expedition into the future—one where Gen AI propels us forward, hand in hand with human ingenuity.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://www.mckinsey.com/featured-insights/mckinsey-explainers/whats-the-future-of-generative-ai-an-early-view-in-15-charts
2. https://www.mckinsey.de/~/media/mckinsey/locations/europe%20and%20middle%20east/deutschland/news/presse/2023/2023-06-14%20mgi%20genai%20report%2023/the-economic-potential-of-generative-ai-the-next-productivity-frontier-vf.pdf

Technological breakthroughs are akin to tsunamis. They take everyone by surprise, pack a punch and alter everything in their path. You can either ride the gigantic wave successfully or it will swallow you. The insurance industry is experiencing its tsunami moment, with reports predicting a $1.1 trillion addition in value for the global insurance industry by 2030¹.

How can the Insurance industry players seize the opportunity to ride the wave successfully?
Let’s explore.

Gen AI as a Game-Changer

1. Vast datasets can be processed for precise risk assessment leading to personalized policy creation.
2. Quick validation and fraud detection via Gen AI can significantly enhance claims processing.
3. Gen AI-powered chatbots will handle up to 95% of all customer interactions by 2025² leading to hyper-personalized customer interactions 24/7.

These represent just the tip of the iceberg.

The bigger portion of the opportunity iceberg is predominantly spread across four major areas that insurance players can explore for use cases. These are search, summary generation, content creation and code.

However, high-quality training data is key to achieving AI success. Poor data results in sub-optimal training that can have disastrous consequences. Think of claims data riddled with errors or inconsistencies. An AI engine trained on such data might incorrectly process claims resulting in delayed payments, wrongful claim denials, or even fraudulent claims slipping through unnoticed. We all know how frustrating such experiences can be!

How can one be absolutely sure of the training data? Sadly, we don’t have the luxury of trusting all of the training data due to its very nature of having emerged from a not-so-perfect world. An alternative is to have Gen AI complement humans instead of replacing them. The “human in the loop” approach, where humans collaborate with AI, can provide accurate results and, more importantly, reassure end customers that AI conclusions are human-vetted.

Potential pitfalls include data privacy risks, bias in AI models, and over-reliance on automation. Ensuring data privacy and compliance with regulations is crucial, as AI systems handle sensitive customer information. Consider plausible scenarios that might unravel.

In contrast, in a medical context, it was observed that AI, when used alone, outperformed human doctors, providing the most accurate results. This highlights the need for continuous testing and analysis when integrating AI into various industries, including insurance. Balancing accuracy with efficiency will be a challenge for some time to come.

While a lot of Gen AI tools and techniques abound, here are the approaches that could provide the greatest dividends for the successful implementation of Gen AI in commercial insurance.

You don’t always need to chase the latest AI models. Previous AI versions may be more suitable for certain use cases and can be more cost-effective, making them a viable option for insurers.

Gen AI constructed responses need to be presented in a manner that is discernible and acceptable to humans. Developing interfaces that allow non-technical users to understand and verify AI outputs is crucial.

Experimentation is inevitable. However, insurers should engage in small-scale, rapid experiments to determine the feasibility and potential impact of Gen AI on specific use cases. Rapid elimination of ‘what does not work’ can help your teams to bring the focus back on ‘what can be potentially pursued’. Experimentation is preferred over a ‘wait-and-watch’ approach.

The democratization of AI has enabled a rare opportunity for employees in the insurance industry to move to more value-adding activities³. This should also put the focus back on low-hanging fruits that can be the focus of early experimentation within insurance enterprises.

Budgetary Challenges

Allocating budgets can be challenging as AI experimentation may not provide a clear cost-benefit analysis. No Pain-No Gain – This adage underlines the need for experimentation without overly worrying about the cost. Allocate a portion of the budget for experimentation and risk-taking to facilitate AI adoption.

The rapid development of AI has also led to a mushrooming of vendors, making it essential for insurers to be cautious. It might be prudent to build on existing partnerships and conduct pilot projects with trusted vendors.

Also, it pays to remember that while AI technology evolves quickly, organizational culture changes slowly. It is recommended that enterprises work towards

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://www.mckinsey.com/industries/financial-services/our-insights/insurer-of-the-future-are-asian-insurers-keeping-up-with-ai-advances
2. https://www.forbes.com/advisor/in/business/ai-statistics/
3. https://www.bcg.com/publications/2023/why-insurance-leaders-need-to-leverage-gen-ai

Human bodies are complex, as are the diseases that affect them. Consequently, many diseases do not have a cure even today. While the reasons are many, one of the major bottlenecks is the expensive, drawn-out drug development process. Discovering and developing a drug can take up to 15 years of human effort and cost over $2.8 billion. It also has an overwhelming failure rate¹. But does it have to be such a daunting task? With technology advancing at an unprecedented pace, we now have enough data and tools to do everything better. Finding better drugs faster that impact millions of lives should be the top priority of the tools we have today. Visionary pharmaceuticals and biotechnology companies understand this. They have digital teams to identify use cases that can have maximum impact in shrinking the timelines and increasing the precision at every step of the process.

A significant challenge for this innovation, however, has been regulatory compliance, but recent strides from the FDA has created new hope.

The Center for Drug Evaluation and Research (CDER) at FDA has undertaken the Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) Initiative in collaboration with the Center for Biologics Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH).

FRAME focuses on developing a framework for AI-enabled development and manufacturing of pharmaceuticals. Regulatory oversight and a connected ecosystem will enable pharmaceutical manufacturers to modernize the technological infrastructure and integrate AI / ML models and analytical applications into core functions.

How exactly should R&D teams leverage AI in the drug discovery process? We discuss three focus areas that pose the touchest challenges in developing new treatements.

Drug design

Let’s dissect a typical drug discovery process. Millions of datapoints need to be analyzed to find potential drug compounds that might effectively treat diseases. Once a potential compound is identified, it has to be matched with a specific protein receptor– the right biological target for the compound to interact with. After mapping the compound with a protein, we must validate this pairing (to make sure it’s the right target) and optimize its structure. Determining the structure of just one such protein could take years of laboratory work.

Drug Screening

Deep learning models can also predict the properties –biological and biochemical activity, physicochemical properties, and toxicity – of these molecules by analyzing their structure. Researchers need this information to understand how a drug will behave in the body, screening out compounds that are not likely to succeed. This allows the R&D teams to focus and select the most promising compounds to be taken forward for development. It also allows the teams to optimize efficiency and safety features prior to the synthesis or production of the new drug molecule.

Drug Repurposing

While AI has already made its mark in small molecule discovery, it’s the large molecules like antibodies and proteins that make the pipeline in pharmaceuticals – the market is expected to grow to $3.2 trillion⁴ by 2030.

Scientists are now harnessing AI/ML capabilities to predict structures and functions of proteins, and in streamlining the process of designing both traditional drugs and advanced therapies like RNA-based vaccines. This more than just about bringing new drugs to market faster; it’s about repurposing existing ones for new uses.

AI is making this possible by identifying and classifying new targets for existing drugs and determining similarities between molecules. For instance, AI-based analysis of the toxicity of similar compounds improves safety while reusing a drug for a new therapeutic use.

Clearly, AI technologies create immense value in every stage of the drug discovery process. They’re making the process faster, finding new ways to use existing drugs, and helping ensure that these drugs are safe and effective.

Once a new drug is discovered, pharmaceutical manufacturers conduct extensive studies on new drugs, vaccines, and biologics to establish the effectiveness and safety of the treatments. They take a phased approach– Phase 1, Phase 2, and Phase 3 – with each phase having different objectives. After successfully completing the trials, they submit detailed reports to the FDA for review. Only after the FDA’s approval can they launch a drug into the market. This process can take several years to complete.

During the pandemic period, for instance. Moderna took just about 65 days to develop their mRNA vaccine. Clinical trials and authorization by the FDA, however, took an additional 270 days⁵. While the importance of safety is not up for discussion, we can certainly speed up the process to make the right treatment available at the right time.

AI-powered systems shorten the timelines for designing and delivering new chemical entities. In turn, it ensures economic viability for drug developers and efficient treatment for patients. Possibly, every pharma and biotech company realizes the importance and impact of AI. But barely 14%⁶ have successfully embedded digital technologies into their R&D workflows and that’s the more mature process. So the question is where should companies start? The first step must be aligning key stakeholders and experts and developing a comprehensive road map – digital transformation, talent strategy, partnerships, investments – to get a jumpstart in the right direction.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://www.asbmb.org/asbmb-today/opinions/031222/90-of-drugs-fail-clinical-trials#:~:text=It%20takes%2010%20to%2015,to%20develop%20one%20successful%20drug
2. https://www.fda.gov/news-events/fda-voices/fda-releases-two-discussion-papers-spur-conversation-about-artificial-intelligence-and-machine
3. https://www.ipd.uw.edu/2021/07/rosettafold-accurate-protein-structure-prediction-accessible-to-all/
4. https://www.einnews.com/pr_news/599925140/global-pharmaceuticals-market-projected-growth-until-2030
5. https://asteriskmag.com/issues/04/from-warp-speed-to-100-days
6. https://www.mckinsey.com/industries/life-sciences/our-insights/top-ten-observations-from-2022-in-life-sciences-digital-and-analytics

The high-stakes chess between cybersecurity teams and hackers is constantly escalating, with Gen AI playing on both sides of the field. On the one hand, Gen AI is helping threats morph at breakneck speed in the digital underworld. On the other hand, Businesses can leverage Gen AI to prevent, detect, and combat these threats with zero tolerance. While the hackers need just one of their attacks to succeed, security teams need to neutralize every single shot aimed at them. Clearly, for enterprises, playing defense¹ is no longer sufficient; they need to predict—and deflect—the next move.

In this article, we discuss common cyber attacks and how Gen AI’s inherent capabilities are equipped to counter these attacks. We will also discuss how security teams’ Gen AI vs hackers’ Gen AI pans out.

You receive an email that appears legitimate, but the tone or a suspicious link gives you pause. This could be a phishing scam, where cybercriminals trick people into revealing personal information. In the U.S. alone, there were over 500 million attacks³, over 500 million attacks, and a loss of over $ 50 million due to these scams last year. As phishing becomes more sophisticated, traditional signs like poor grammar and spelling errors are disappearing, making it difficult for human eyes to spot them. Gen AI steps in here, utilizing its natural language processing to spot anomalies in communication, boasting a 96.2% success rate⁴ in identifying such scams.

Take, for instance, your finance team gets an invoice that has slightly unusual details. Gen AI can compare these against vast data sets, alerting the team to potential risks before any payments are processed.

Malware, or malicious software, can infiltrate a network with just a simple click on a suspicious link or email attachment. Traditional antivirus software, with its reactive approach, often leaves us a step behind emerging threats. Here, Gen AI offers a more proactive solution. Leveraging advanced machine learning, it continuously monitors and analyzes behavior within your software environment. For example, in a complex supply chain network involving multiple vendors, Gen AI is constantly vigilant. Upon detecting an anomaly – like a vendor’s software behaving unexpectedly – it responds immediately, isolating the issue to prevent widespread impact across the network. Security analysts can use this malware to analyze its behavior in a secure sandbox, expose their security system’s vulnerabilities, and understand the extent of damage that can occur.

Ransomware locks away an organization’s data or systems until a ransom is paid. Recently, the notorious LockBit gang⁵ targeted Boeing, publicly declaring the theft of a vast amount of sensitive data on their leak site. Despite negotiations, LockBit went on to release a portion of the stolen data. This breach spotlights the harsh reality: no entity, not even a tech giant, is immune to ransomware — risking operational chaos and severe blows to both confidentiality and corporate credibility. Gen AI can enhance predictive models⁶ to identify the early signs of ransomware behavior, such as rapid encryption of files, and stop it before it takes hold.

Vishing, or voice phishing, uses phone calls to trick individuals into surrendering private information. Deepfakes use AI to create convincing fake audio or video content⁷. Gen AI counters this by being trained to detect the subtle signs8 that content has been manipulated, like inconsistencies in facial movements or audio that doesn’t quite match lip movements.

Imagine a video circulating seemingly from the CEO, announcing that a key executive is exiting the company or declaring bankruptcy. That would cause a ripple effect of a fall in stock prices and market manipulation. A Gen AI system trained to recognize the CEO’s speech and mannerisms could analyze the video, identify it as a deepfake, and alert employees not to act on the instructions.

Is Gen AI defense stronger than Gen AI attack? Who will win in this technological tug-of-war?

Consider this. Phishing emails used to stand out with their bad grammar and spelling errors. Now, hackers are using OpenAI’s ChatGPT to write better phishing emails. They are improving the language, making it harder to spot. This is just the start, though. Ask the experts, and many say that Gen AI seems to be giving the bad guys an upper hand right now. But in the long run, it will benefit defense teams more.

Does that mean we lose this Gen AI vs Gen AI battle? Fortunately, enterprises don’t rely solely on Gen AI for security. They employ a layered approach, or in-depth defense, which includes other technologies and human oversight. Even if Gen AI-generated threats bypass one layer, others may still catch them.

The most effective cybersecurity systems involve collaboration between AI and human experts. Gen AI aids humans in identifying potential threats, but human intuition and experience play critical roles in interpreting and responding to those threats, providing a check against AI-generated attacks that might otherwise slip through.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://www.forbes.com/sites/forbestechcouncil/2023/09/25/harnessing-genai-building-cyber-resilience-against-offensive-ai/?sh=1bb64dc78ed0
2. https://www.forbes.com/advisor/business/phishing-statistics/
3. https://securityweek.com/ransomware-group-leaks-files-allegedly-stolen-from-boeing/
4. https://www.turing.com/resources/generative-ai-enhances-cybersecurity
5. https://www.washingtonpost.com/technology/2023/03/05/ai-voice-scam/

Billions of people around the world depend on supply chains to fulfill their needs and desires. It’s something we often take for granted, but recent times have spotlighted how essential they are. The pandemic turned out to be a magnifying glass that revealed the vulnerabilities and inefficiencies of the traditional supply chains. Naturally, legacy supply chains shot up as a major concern, with a staggering 60% of CEOs¹ finding their supply chains under unprecedented stress. There is now an urgent need to rethink our supply chain strategy.

This idea is not entirely new; a paradigm shift has already been simmering beneath the surface of global supply chains. For over a decade, the term “digital transformation” has echoed through boardrooms and conferences, yet its essence remains elusive to many. Let’s demystify this and understand why it’s the need of the hour.

While the concept of warehouse automation has been around for over a century, today’s digital transformation requires a more integrated and advanced approach. At the heart of this transformative journey lies the superpowers – AI, machine learning, and automation. But where should one start their transformation journey?

The best bet is to eliminate the low-value-added tasks first. Pandemic or not, Intelligent automation (IA) proves invaluable when it comes to streamlining time-consuming routine processes. With Intelligent automation (IA) in the driver’s seat, what used to take hours can now be accomplished in just minutes. Unlike most humans, Intelligent automation (IA) excels at repetitive tasks—especially the ones that tend to be tedious and mundane. This not only liberates employees to focus on more strategic endeavors but also delivers substantial productivity gains for the company.

Why is this fervor towards digital transformation? What might IPA look like in action? Imagine a day in the life of supply chain operations: Robots handle repetitive tasks such as entering orders and updating inventory. AI and ML make intelligent, independent decisions about the best shipping routes or stock levels. Meanwhile, the system reads and writes complex reports, and cognitive agents suggest when to repair equipment. Everything from orders to shipments is tracked in real-time, ensuring smooth transitions between different steps and disparate systems.

But there is more to this story. The unmatched visibility and agility a true transformation offers and a host of benefits these two factors bring.

A telling study from McKinsey linked supply chain risks to a lack of visibility². In line with this, a CGS survey found that 87%³ of respondents consider supply chain visibility a significant challenge they are currently working on.

Imagine preempting a potential supply shortage by flagging a critical vendor’s financial troubles, allowing for a timely switch to a more reliable supplier.

Every single one of the Fortune 1000⁴ CEOs said Sustainability is important to them. Walmart⁵ has underscored the significance of sustainability and ESG factors throughout its entire supply chain, extending to subcontractors and every participant in the value chain. However, collecting and overseeing traceability data can be pretty demanding.

While AI is exciting, the road ahead has its challenges. Companies face issues⁶ like skill gaps, technology limitations, high implementation costs, and managing change every day. The good news is that providers are making solutions more user-friendly, making the transition smoother.

The use cases and benefits we discussed are just the beginning for supply chains today. For a long time, supply chains were seen mainly as cost centers and not competitive advantages like now. With competition increasing, especially with a focus on supply chain resilience, AI and Intelligent automation (IA) shine as strong options. No matter where you are in your digital journey, you can find your winning formula and a catalyst for change in AI and automation.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://kpmg.com/si/en/home/insights/2022/03/kpmg-global-ceo-outlook-survey.html
2. https://www.mckinsey.com/capabilities/operations/our-insights/future-proofing-the-supply-chain
3. https://www.forbes.com/sites/forbestechcouncil/2023/04/07/fashion-supply-chain-transparency-takes-center-stage/?sh=2047cacc65d6
4. https://www.ey.com/en_us/sustainability/sustainability-and-esg-trends-index
5. https://one.walmart.com/content/dam/responsiblesourcing/supplier_expectations/Resource_Traceability_ENG.pdf
6. https://www.mckinsey.com/industries/metals-and-mining/our-insights/succeeding-in-the-ai-supply-chain-revolution

Since its public roll out last year, Gen AI has rapidly evolved and has exerted a transformative influence across various sectors, akin to the profound impact the invention of the steam engine had on businesses at the dawn of the 19^th century. Just as the steam engine revolutionized transportation, textiles, mining, and machine tools, Gen AI, with its ability to create new content, such as large text, images, videos and other forms of media outputs resembling human-created content, holds the potential to leave an indelible mark on every industry it will touch and have a ubiquitous impact. However, its Midas touch is poised to be nothing short of spectacular in certain sectors, and the business process management (BPM) industry stands at the cusp of this revolution.

So why is the industry going ga-ga over Gen AI’s impact on BPM? Several BPM characteristics align seamlessly with Gen AI’s capabilities, such as:

With increasing adoption in BPM and rapid technology innovation, Gen AI is poised to become the mantra to disruptively transform business operations through automating, innovating, and improving processes continually.

Where do we see these capabilities play out?

AI Redefining BPM Operations

One of the leading global insurers is experimenting with a “60-second claim” process¹. This AI-powered loss assessment and evaluation enables the insurer to process a claim in less than a minute by uploading photos and documents.

The Cognitive Leap: Gen AI’s Smart Process Innovation

While AI and IA pave the way for cognitive automation, Gen AI takes transformation to a higher echelon. It harnesses the power of neural networks to create not just efficient but truly optimized and smart processes, paralleling human cognition. The result? Businesses attain levels of agility, adaptability, and impact that were previously elusive, allowing them to pivot swiftly in response to evolving market dynamics and customer expectations.

Transforming Businesses with Predictive Insights

Think of the following scenarios

• Predicting consumer behaviour in retail
• Detecting fraudulent financial transactions
• Predicting propensity of a customer to default on their payments
• Curating and creating new content
• Predicting maintenance requirements in manufacturing

Predictive analytics empowers businesses and BPM providers to gain a deeper understanding of their customers, minimize risks, eliminate inefficiencies, streamline operations, enhance productivity, and boost revenues.

In the financial services domain, predictive analytics can proactively detect and prevent transgressions and associated risks by leveraging historical data on past transgressions. Healthcare can be hyper-personalized by predicting patient outcomes fine-tuned to each individual’s health profile.

AI’s prowess in predictive analytics, coupled with Gen AI’s near-human cognitive capabilities, offers businesses not only insights into current operations, but also foresight into future trends, events, and recommended actions. This proactive approach acts as a shield against disruptions, reducing costs and consistently optimizing processes.

Personalized Experiences Amplified by Gen AI

The profound impact of AI and Gen AI on BPM manifests in their ability to cultivate genuine customer-centricity and deliver immersive, personalized experiences that resonate deeply with individual customer. Consider these examples:

Today’s CPG, manufacturing, and retail enterprises are facing their Netflix ‘point of inflection.’ A media company that started as a DVD-by-mail rental service, was faced with the winds of change when the digital streaming era emerged on the horizon. Much like the industries in the CPG and manufacturing sectors, Netflix faced the challenges of changing consumer preferences and the rise of digital technology.

Netflix provides a textbook case study on successfully embracing change. Enterprises for whom the supply chain is the lifeline of their business will do well to draw a leaf out of Netflix’s story that predominantly revolves around ‘The Data First Approach.’ Supply chain disruptions, such as those caused by the COVID-19 pandemic, natural disasters, or geopolitical events, have exposed vulnerabilities in global supply chains. Omni-channel retailing has introduced a new layer of sophistication. All these changes in the landscape have prompted a revisit to make the supply chain more agile and resilient.

The increasing need to sense demand Real-Time

One, ‘customer demand’ has always been at the crux of how a supply chain works. Supply chains commonly operate in the de facto mode of using last year’s demand for demand forecasting. However, disruptions caused by COVID-19, and turmoil across key geographies have played havoc with the supply chains. It has exemplified the over-reliance on demand forecasting and subsequent lack of resilience and agility in supply chains to bear the brunt of disruptive events.

Two, the increasing need to cater to both in-store and online customers and the whole gamut of new fulfilment methods like BIS, BO, BOPIS, ROPIS, BORIS, and BOSFS, have stretched the prediction models in new directions. Demand forecasting and inventory placements just got more complex!

The case for demand-sensing

Demand sensing, unlike demand forecasting, uses data acquired, days and even just hours ago to make spot-on short-term predictions about the demand for goods and services. This is imperative for customers and brand owners in the fast-moving CPG, Manufacturing and Retail space.

Getting demand sensing to work for you

Only a data-first approach powered by end-to-end digitalization allows swift and seamless data sharing with all stakeholders in a product’s ecosystem. Suppliers upstream can track stock, plan, and expedite inventory replenishment across stores by gaining near-instant access to ‘customer demand’ data.

For instance, large retailers like Amazon and Walmart (with its Luminate program) are moving away from portal download to sharing the data with brands in near real-time through API-driven digital integrations. Enterprises like Carrefour and Instakart are leveraging cloud platforms and big data stores for the same.

The number one challenge faced by enterprises is the heterogeneity of data and data sources across the stakeholders. Procuring data from hundreds of channel partners across geographies, each of whom uses disparate systems and software, is a mammoth task. Having obtained the data, next comes the task of scrubbing the data and transforming it to make it consumption-ready to enable upstream suppliers to extract insights from it.

The second challenge is the new need for enterprises and brands to run large, heavily staffed, complex IT programs at scale to continue to reap the benefits of demand sensing.

You can address both these challenges by using off-the-shelf AI-powered demand-sensing solutions. These customizable solutions come with inbuilt capabilities, domain expertise and enable automation, thereby reducing the time and effort needed to acquire and process data. They provide brands and enterprises with rich insights that can be translated into action.

Improving Outcomes with Real-Time Data

Real-time data is the real deal! Real-time data is like the lubricant that does away with the friction points in your supply chain. It drives home at least five distinct advantages.

In the ever-evolving symphony of technological progress, the banking industry stands as a testament to transformation. Over time, banking technology has undergone an incredible journey. From the early days of cloud computing to today’s rise of Gen AI, the path of banking technology has been a testament to constant change, adaptation, and meaningful influence.

Amazon Web Services pioneered cloud computing¹. Its advantages, including seamless hardware scalability and cost efficiency through the pay-as-you-go model, were specifically tailored for industries like banking and financial services, historically reliant on substantial computing power. However, the banks initially responded tepidly.

The reason for this was the stringent compliance standards and security concerns that governed the banks.

This spurred Amazon and other cloud providers to weave robust security protocols, encryption measures, and compliance certifications tailored specifically to meet the rigorous standards of the financial sector, into their cloud offerings.

This transformed the perception of cloud computing within highly regulated banks. Banks began adopting cloud computing and today most global banks operate on hybrid cloud models, leveraging multiple hyperscalers like Amazon, Google, and Microsoft.

Technology adoptions seen in hindsight, as previously, clearly point to certain set patterns of adoption. Though the outcomes of new technologies remain uncertain, we can glean insights from past industry adoptions. The lifecycle of these transformative technologies follows a familiar trajectory:

Gen AI is currently transitioning to the second stage. Here are some use cases that should double up as quick wins.

The ongoing evolution in banking technology underscores the enduring truth in Roy Amara’s words—”We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” As banks traverse these technological frontiers, the convergence of AI and banking envisages a future that transcends human limitations, redefining the very essence of financial services. $200-$340 billion is the estimated productivity value that is waiting to be unlocked through Gen AI technology in the banking sector³. Now, isn’t that something to look forward to?

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://techcrunch.com/2016/07/02/andy-jassys-brief-history-of-the-genesis-of-aws/
2. https://www.cbinsights.com/research/generative-ai-chatbots-banking-market/
3. https://www.mckinsey.com/industries/financial-services/our-insights/been-there-doing-that-how-corporate-and-investment-banks-are-tackling-gen-ai

Which department in your company plays the most important role? As business leaders, we know that every department, from sales to IT, plays a significant role in achieving our business objectives, and the essence of success lies in effective collaboration. Without it, customer issues become a game of hot potato, valuable information is often repeated, and unnecessary administrative tasks consume employee efforts. This issue becomes even more pronounced when dealing with multiple external partners.

An insightful Harvard study¹ asked leaders about their primary focus in workplace relationships. Many pointed to vertical, hierarchical connections – the immediate up and down lines in their own departments. However, the narrative shifted when questioned about the relationships that deliver the most value to customers.

In this article, we will cut through the jargon and get straight into what this means for businesses, what’s holding them back, and outline practical steps to overcome these obstacles.

Ever wondered what our jobs would look like if they came with a ‘self-driving’ mode? A few years back, McKinsey threw a figure into the ring: 30% of tasks in 60% of jobs¹ could be automated. Fast forward to the era of Gen AI, and it’s not just the routine tasks in the list anymore. We can automate a lot more – in fact, there is more ‘auto’ in automation than ever before.

We have seen automation inch into our work lives, mostly in the form of bots that handle repetitive tasks. The old McKinsey data, insightful as it was, didn’t account for the Gen AI revolution. Back then, the thought of machines performing jobs requiring a high level of creativity or cognitive skills was more science fiction than impending reality.

Ours is now a world where automation is no longer just an assembly line but an AI-powered assistant that can think, design, and innovate. So, where does this take us?

Take contact centers, for example. In the not-so-distant past, each customer call was individually tended to by a person who would consult ledgers, navigate systems, and weave in solutions—a meticulous but slow art form.

The advent of RPA automated the basic patterns of customer service—account checks, billing inquiries, and routine data entries. The bots were fast, tireless, and error-free, handling tasks with a mechanical grace that lacked only a human’s intuitive touch.

Today, Gen AI is revolutionizing the field, blurring the lines between the once distinct capabilities of AI and RPA. It’s an evolution of the process—a leap from mechanized assistance to creative and cognitive partnership.

This isn’t just automation as we’ve known it; it’s a reimagined collaboration. The spectrum of automation has broadened to include not just tasks but the very seeds of creativity and strategic thought. For every organization, this means a redefinition of possibilities. It’s taken us from a bot for every employee to an AI for every employee.

The true essence of Gen AI’s impact lies in its ability to transform productivity. Companies like JPMorgan, for instance, have harnessed the power of their customized version of ChatGPT, turning it into a tool for simplifying financial operations and customer interactions.

Many companies are using capabilities such as content summarization, content discovery, and content creation in many processes and tasks that earlier could not be automated through RPA and similar technologies. This means that a lot more tasks and activities could be augmented and help professionals from various functions save time, reduce errors, improve quality, and increase efficiency.

Industry-specific processes such as credit processing, claims processing, doctor’s review, procurement document preparation, and review are heavy with paperwork and manual verification. The AI evaluates the information available with precision, reducing processing time from days to mere seconds³. This isn’t a marginal upgrade; it’s a reinvention of the workflow, slashing operational costs and enhancing customer satisfaction.

In sectors like IT, for example, where once troubleshooting was a time-consuming puzzle, AI now offers instant solutions. The result? Teams are liberated from the drudgery of problem-solving to concentrate on innovation. It’s about the quantifiable leap⁴ in output and the qualitative enhancement of work.

Consider the procurement process. A task that used to consume countless hours of monotonous data entry into systems like ERP, Excel, or specific company websites now unfolds in minutes. Better yet, it often requires no human touch at all, running seamlessly from start to finish.

The explosion of productivity aside, what really grabs our attention here is the new definition of work. What used to be long, laborious hours of manual rekeying is now a task completed with a few clicks, leaving employees free to refocus on more strategic and creative endeavors.

For example, with Gen AI-powered Copilot, the role of the FPA analyst will undergo a significant transformation. They can now converse and conduct Q&A with data using natural language. Procurement specialists can also read through pages of PDF documents and summarize RFP responses while reviewing contract clauses. In essence, isn’t just a tool for cost-cutting or better productivity—it’s a catalyst for a fundamental shift in how work is done. It’s not about replacing humans. No. It’s about redefining their roles, enabling them to engage in more meaningful work while AI handles the rest. This is the crux of the productivity explosion: liberating human potential by offloading the mundane to machines at a scale that we have not seen before.

AI and, in fact, Gen AI is not very new⁵. What is new is that it is now readily available to the masses. Employees at every level are recognizing its potential, creating a groundswell of demand for its integration into daily operations. But it is not as straightforward as it might seem to legitimize it in an enterprise.

For companies looking to capitalize on Gen AI, the priority is clear: ensure the right access. This means securing B2B subscriptions that fit the business model and legal frameworks, thus legitimizing Gen AI’s use. With only a year since these tools became mainstream, every company is at a different stage in their Gen AI journey.

A cautionary tale comes from the tech sector, where Samsung developers⁶ found their proprietary code suggestions being shared by Gen AI platforms. This incident spotlights the importance of using public domain Gen AI tools responsibly and ensuring that they are trained appropriately to respect legal and IP boundaries. Caution is understandable, especially around data security and intellectual property rights.

One thing becomes abundantly clear: Not providing these advanced capabilities to employees is a missed opportunity and a strategic oversight with far-reaching implications.

In the near future, visionary companies might equip every employee with a Gen AI assistant tailored to their specific role. A finance professional could have an AI that manages and analyzes complex data sets and predicts market trends with precision. In marketing, an AI assistant might autonomously optimize digital campaigns in real-time, learning and adjusting from each interaction. These are the companies that will attract top talent who seek to work in a culture that’s both progressive and productive.

These are now available at the fingertips of every employee through Microsoft Copilot and other similar applications.

In this future, a company that lags risks stagnation or, worse, regression. It’s like clinging to outdated fax machines in a world dominated by instant messaging. So the critical question we need to ask ourselves is this – Are we inching, if not leaping, towards this future enterprise, or are we risking our future by standing still in a rapidly advancing technological landscape?

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://www.mckinsey.com/mgi/overview/in-the-news/how-many-of-your-daily-tasks-could-be-automated
2. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-organization-of-the-future-enabled-by-gen-ai-driven-by-people
3. https://aimagazine.com/articles/lemonade-sets-world-record-with-2-second-ai-insurance-claim
4. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#work-and-productivity
5. https://bernardmarr.com/a-simple-guide-to-the-history-of-generative-ai/
6. https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/?sh=3ab62c2b6078