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Her Husband Took His Life & Cafe Coffee Day Was In Rs 7,000 Cr Debt, Malavika Hedge's Extraordinary Tale To Save CCD From Dying In Two Years

 

Her Husband Took His Life & Cafe Coffee Day Was In Rs 7,000 Cr Debt, Malavika Hedge's Extraordinary Tale To Save CCD From Dying In Two Years

Today, Cafe Coffee Day stands as a testament to the indomitable spirit of a woman who refused to let tragedy define her, emerging as a true inspiration in the world of entrepreneurship.

 

New Delhi: The story of Cafe Coffee Day (CCD), one of India's beloved coffee chains, is not merely about a successful business; it is a tale of resilience, determination, and redemption. When VG Siddhartha, the visionary founder of CCD, tragically took his own life, the company was left in shambles with a debt of Rs 7,000 crore. It was in this dark hour that his wife, Malavika Hegde, emerged as the beacon of hope, leading CCD through the storm and breathing new life into the business.

CCD Left In Shambles After VG Siddhartha's Death

 

Malavika Hegde, daughter of former Karnataka Chief Minister SM Krishna, had been a witness to VG Siddhartha's entrepreneurial journey since their marriage in 1991. Devastated by her husband's untimely demise, she made a resolute commitment to preserve his legacy and fulfill his dream of making CCD a thriving business.

 Malavika Embarks On An Uphill Task

Undeterred by the immense debt burden, Malavika embarked on a mission to revive CCD and reduce its liabilities to a reasonable level. With astute business acumen, she implemented a series of measures that showcased her resilience and determination.

Streamline Operations And Cut Costs

Firstly, Malavika made the bold decision not to increase the prices of CCD's signature coffees, choosing instead to streamline operations and cut costs. She removed hundreds of coffee vending machines that were not yielding profitable returns and shuttered non-performing outlets. These actions helped optimize the company's resources and improve its financial position.

Infuse New Capital Into CCD

Secondly, Malavika focused on securing new investors to inject capital into CCD. Through strategic alliances and share purchase agreements, she successfully attracted reputed companies and convinced them of the value in preserving the CCD brand. Notably, the stake sale in Mindtree and collaboration with US private equity giant Blackstone played a significant role in reducing the company's debt.

 

 

Efforts Bring Fruits

Despite the challenges posed by the COVID-19 pandemic, Malavika Hegde's leadership and dedication enabled CCD to not only survive but also thrive during difficult times. She implemented stringent safety protocols across CCD outlets, instilling confidence in customers and bringing them back to their beloved cafes. With her unwavering commitment, CCD continued to expand its presence across the country, competing with other coffee chains like Starbucks.

Malavika Hegde's remarkable journey as the CEO of CCD showcases the power of determination, resilience, and a commitment to a vision. Through her tireless efforts, she not only saved CCD from the brink of financial collapse but also steered it towards growth and prosperity. Her success in reducing the company's debt and securing its future serves as a testament to her unwavering belief in her late husband's dream. Today, Cafe Coffee Day stands as a testament to the indomitable spirit of a woman who refused to let tragedy define her, emerging as a true inspiration in the world of entrepreneurship.

 

Building A Skills-Based Organization: The Exciting But Sober Reality

 

Building A Skills-Based Organization: The Exciting But Sober Reality

 

Fueled by new AI tools and skills technology, nearly every company wants to become a “skills-based organization.” Now that we’ve had a few years to study this trend, I’d like to share some of the realities and calm some of the hype.

The Premise

Let’s start with the premise: the idea promoted by whitepapers is that we’re going to create this unbiased, politics-free company where decisions are based on skills, meritocracy, and performance. Vendors promise that we’ll have a global skills database and through the marvels of Talent Intelligence (primer here) we’ll be able to see trending skills, gaps in skills, and become more scientific about hiring, promotion, pay, and leadership.

Under the covers of this concept is the idea that we can “tag” or “assess” everyone’s skills with laser precision. And many of the AI tools, including the ones we use for our GWI research, promise to do this today. How do they assess our skills? They use the magic of AI to look at our job history, performance, work product, and other sources to infer, model, and predict what we’re good at, what we’re exceptionally good at, and what we need to learn next.

What a glorious vision. And the benefits are many: unbiased competency-based hiring, directed mobility of people to new roles, and strategic planning tools to help us plan pay, locations to hire, and more.

The Reality

This is not a new idea: skills have always been important in business.

I graduated from college in 1978 with a degree in Mechanical Engineering. Upon graduation I interviewed with Procter and Gamble, Boeing, the US Navy, and other organizations. Back then, 45 years ago, every company was interested in my skills. I took tests, answered technical questions, told interviewers about my courses, and demonstrated my proficiency in interviews. (Admiral Rickover, the head of the nuclear navy, asked me specific questions about heat transfer.)

But these companies were not naive. The reason they asked these questions was not to understand what I learned in college, but to understand how I think. I later learned that my behavioral interviews at P&G were designed to decode my personal goals, my mindset, my ability to think, and my ability to communicate. While these may be classified as skills, they are much more complex than figuring out if I knew how to code in Java.

Today, half a century later, it feels like we’re moving backwards. We’re heavily focused on tools and systems to identify technical skills and generic business capabilities. And while these tools and systems are amazing, we have to remember that the most important skills of all (the PowerSkills, as I call them), are yet left out. As my IBM manager used to say, “hard skills are soft,” it’s the “soft skills that are hard.”

In other words, companies succeed based on culture, ambition, learning agility, and alignment. And while we want to assess skills to define jobs, roles, and development, we also have to assume that every person can learn new skills (and must) on an ongoing basis. And this means we want a more holistic (“systemic” in our language) view of skills, moving beyond technical proficiencies alone.

Boris Groysburg, a Harvard professor, studied the performance of world’s top investment bankers. These individuals are highly skilled in financial products, deals, and large transactions. And what did he find? If you take a “highly skilled” investment banker in one company and move him to another, most likely he will not be a high performer any more. His “hyper-performing” skills were actually not his technical skills, they where his unique ability to leverage the organization and know how to get things done.

So building a skills taxonomy can be complex. As our research has found, business skills fall into many categories, each valued in different ways by different companies. And while generic skills certainly matter, it’s the way you use them in your company that drives value.

§  Technical proficiencies (coding, software, IT systems, medical procedures, etc.)

§  Operational proficiencies (running equipment, fixing a pump, safety procedures, etc.)

§  Functional proficiencies (marketing operations, CRM, product management, engineering, design)

§  Industry proficiencies (understanding oil and gas industry, chemicals, software business, etc)

§  Management and leadership proficiencies (managing teams, leading businesses, etc.)

Each of these is filled with “skills,” so much that companies like Lightcast, who aggregate skills for tens of thousands of job titles, build dynamic libraries with tens to hundreds of thousands of skills. And on top of this we have the big new world of AI-inferred skills, like “handling objections” or “analyzing financial statements” which it figures out on its own.

 

 

 

So What Is New Here? A Lot.

Given these complexities, what’s really new? Well the big change is the interest in building a corporate skills taxonomy, a single “dynamic database” for skills.

This taxonomy is not like the competency models of the past. This is an enormous set of data (tens of thousand of hierarchical skills) and every word in the taxonomy is subject to debate. Should we use “collaboration” or “teamwork?” Should we us “java” or “java programming” or “java language?”

There are hundreds of off-the-shelf taxonomies, and every industry is different. Energy companies have refining, production, and distribution skills. Consumer product companies have brand marketing, product marketing, and channel analysis skills. And Pharma and Chemical companies have scientific, genetic, and regulated manufacturing skills.

Some skills must be verified: entire platforms like Kahuna let you decide who can validate skills and when they have to be revalidated. And other skills need assessments: built on leadership, management, and other soft-skill models.

You can see how complex this is, and remember each company is different. Your company may value innovation and product design skills; your competitor may focus on manufacturing and distribution.

How can we put this all together? Isn’t this a “boil the ocean” type of problem?

Companies tend to take two paths. Path 1 is to build a skills taxonomy team, and then create a long process of working with business units to agree on the language and taxonomy architecture. This may work, but ultimately it has many points of failure. Without really testing these skills in action they’re likely to need tuning, so this often takes a long time.

Path 2, which we recommend, is to start by focusing on a problem. From that problem you build a part of the taxonomy, create a process for design and governance, and learn what tools work best.

Falling In Love With A Problem

Let me give you a real-world example. Suppose you have high turnover and low morale in customer service.

As you dig into the problem (what we call “falling in love with the problem”), you realize the customer service challenges are broad. The team is broken into small groups focused on different product areas, making their jobs boring and repetitive. So you sit down with team leadership and develop a “skills model” for customer service.

As you build the model you discover that very few of the staff are cross-trained. And some are not trained at all! So now, thanks to your skills model, you can decide how to reorganize the group (finding also that some of these “skills” can be automated by ChatGPT), start cross-training, and identify the high performers.

You also now discover that some of your folks are a poor fit. So you use the skills model to find other internal candidates and better source externally. And as you look to hire, you build assessments or interview questions to “hire for these skills.”

American Express actually did this years ago. They realized that the “skills” needed in the Amex sales and service teams were not customer service skills at all, but hospitality skills. Amex treats clients like guests, so they started recruiting from Ritz-Carlton and other hospitality companies. It took a skills-based analysis to figure this out.

As you can see, when you focus on a problem the work can quickly converge and you can solve a real problem. We just interviewed a company that used this approach to more clearly define its cybersecurity roles and found they could save a$20,000 per employee by hiring more junior candidates.

And this kind of analysis helps you decide whether to “buy or build” these skills. In 2020 we did a study of three companies and found that “building technical skills” can be as much as six-times less expensive than buying (hiring).

Such Skills Projects Are Everywhere

There are many use-cases for this approach.

In recruitment, a skills-centric approach lets you expand your network of candidates, often locating internal staff that may be a perfect fit for a job. Through the technology of “skills adjacencies,” we can find people with similar skills who will fit right into a role.

And skills-based hiring reduces bias. A large semiconductor company told us they now use an AI-based skills platform for hiring (Eightfold) and their entire candidate pipeline has more than tripled. They are finding people with excellent skills by blinding name, gender, and academic degree from the resume.

In career development and growth, talent marketplace and internal mobility tools deliver fantastic results. Rolls Royce uses a skills-based model to find manufacturing and production specialists, enabling people to rotate to new jobs in engineering and operations. MetLife, Schneider Electric, J&J, and other companies use a talent marketplace (skills-based employee to job matching system) to promote gig work, career growth, and talent mobility.

In pay and rewards, companies are experimenting with skills-based pay. A large pipeline company told us they now certify repair technicians in various functional areas (pumps, instrumentation, electrical engineering) and when a technician achieves an adjacent skill credential their hourly rate goes up by $5-10 per hour. Imagine all the pay equity data we can analyze against a skills model: this is likely to help us further reduce inequalities regardless of job level or title.

In technology, IT, and science, many organizations feel they can’t keep up. How well prepared is your company for AI, for example? One company we work with is in the middle of building a new skills model for their IT function, and they found that many of their staff are working on technologies that are 15 years old. The new model is helping them recruit, reskill, and energize the entire IT/product function so they can improve hiring, retention, and productivity.

So How Do We Scale This Up?

From a data perspective, companies need to build a business-centric way to manage, govern, and update these models.

Ericsson, for example, built a well-defined skills model for their massive 5G transition. This model was designed by engineers, sales and marketing teams, and the chief learning officer working together. They sat down and decided what areas, roles, and technologies to address, and from there settled on a model from which to grow. Their new journey is to refresh all their IT skills.

BNY Mellon has taken the same approach across IT operations. They’re developed “capability teams” which collaborate on critical job roles (ie. product manager, project manager, analytics) so the teams can keep their skills models up to date.

When you work this way – project by project – the effort gains momentum. You get real results and the business buy-in can scale. We recently helped a large software company build a federated model (business units coordinating effort) to develop a skills model for all their customer education. By doing this in a federated way they can merge and manage their internal skills needs with those of their customers, leveraging content and education in both places.

The Skills-Tech Challenge

But what about the systems? Where should all these skills be stored, and how will we keep them up to date?

While the market is still immature, let me share what we’ve learned.

Many smart AI-powered vendors now offer solutions. Workday, Eightfold, Gloat, Cornerstone, Seekout, Kahuna, Techwolf, Skyhive, Beamery, Phenom, Oracle, SAP, and ServiceNow all have offerings to help you store and define skills, leverage them across different applications, and assess skills through a variety of AI and assessment techniques.

Unfortunately they are each optimized for different purposes. Eightfold, for example, can automatically identify skills within a job description, find candidates, and then identify trending and adjacent skills through its sophisticated models. Cornerstone can show you all the skills covered in your massive learning catalog. Techwolf can infer skills from Jira and Asana projects. And Gloat and Fuel50 can infer skills and match them to career opportunities, jobs, and gigs.

And of course each vendor wants to be “a system of record.” And while many of these vendors have large clients, we have yet to find a company that uses one platform for everything. So while we may, at some point, find a single “skills cloud” capable of storing every skill for every job in the company, that goal has yet to be achieved.

The problem vendors face is the sheer magnitude of the problem. These skills systems are not simply databases: they are AI tools that ideally use second-generation AI to constantly find skills, infer skills, and update skills for every job, person, and career path. They must have open interfaces to the hundreds of skills libraries in the market (every industry and every job family has many taxonomies), and they must have tools to help you manage, analyze, de-duplicate, and curate this data.

And despite the claims, these “skills inference” tools are each different. The recruiting platforms typically are trained with the most data. These platforms (Eightfold, Beamery, Seekout, Phenom, iCims) search and index billions of worker histories and use time series, neural nets, and performance models to infer skills. This means they cover many industries and can identify and analyze skills across many job families across industries.

The talent marketplace platforms (Gloat, Fuel50, Hitch) tend to have less depth only because their goal is just to “make a match within the company.” (Gloat is moving into the overall “talent intelligence” category and now crossing the boundaries.) Gloat has introduced a recruiting product so their platform is clearly becoming an end-to-end talent intelligence system as well (they call it “workforce agility”).

The learning skills tools are least sophisticated (Cornerstone, Degreed, EdCast) because their goal is to match someone to a course or learning path. (Cornerstone is going well beyond this now as well, and has built an entirely new AI fabric to infer skills across its 7,000 customers.)

The ERP vendors (Oracle, SAP, Workday, others) are the least sophisticated, so they are more likely to become “skills aggregators” with APIs to coordinate skills data between these more specialized systems and their internal machine learning models.

In our new AI whitepaper (coming out soon) we talk about how these systems work, and you’ll see that a skills engine has to do many things. It has to infer/access billions of employee profiles, it needs to do time series analysis, and it needs advanced AI (neural nets) to infer, identify and build models that identify skills.

Over time, each skills-tech vendor will go down its own path. Newer vendors like Techwolf, Retrain, and others are looking at corporate data as a source of skills inference, now indexing information in Asana or Jira. This data, while limited, opens a new door: think about the skills information in the Microsoft Graph. Vendors who tap into this information (Viva Topics does this for document management) can learn far more about internal skills. And ultimately this is the type of data you need.

Regardless of the evolving tech market, successful projects focus on a problem. P&G built a skills taxonomy that helped them staff up their supply chain efforts during the pandemic. Reuters built a skills taxonomy to help them build up and scale their data science team. Ericsson’s skills journey started with their 5G re-engineering. And the list goes on and on.

My belief, as we see these projects progress, is that the upside of this work is massive. Companies that embark on this process learn tremendous amounts about their workforce at a rapid rate. They start to understand the governance process. And they build experience with vendors that helps them sort out who can scale to meet their particular needs.

Where This Is Going: From “Jobs” to “Work”

One final point. This work is even more important than you think. As I discuss in Irresistible, this work is part of a bigger shift, away from “rigidly defined jobs” to “roles” focused on work. We call it dawn of the “post-industrial model” of business.

This transition, which I describe in in my book, means that it’s ok to take the time to do this carefully. It’s ok to set up governance, experiment with different tools, and “fall in love with the problem” one step at a time. Over the coming years, we’re going to build more adaptable, scalable, productive companies as a result.

The Skills-Based Organization is coming, one step at a time. If you take the transformation seriously and consider how important it will become, you can build a plan that works.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Dassault Aviation’s Rafale joint venture with Anil Ambani likely to be dissolved - The New Indian Express

Area:

 Business News

 

Faculty note:

 Example of entry of private players in the manufacturing of aircrafts for defense purposes. 

 

 

 

 

 

 

NEW DELHI: The Indo-French joint venture (JV) between Dassault Aviation and Anil Ambani-owned Reliance Aerostructure Ltd is likely to be dissolved as the French company has reportedly decided to pull out of the JV — Dassault Reliance Aerostructure Ltd (DRAL).

Sources said Dassault has taken the decision due to Ambani’s “inability to make the investment required to keep the joint venture going”. Ambani is the major partner in the 51-49 JV. The two companies had entered into the joint venture to handle the offset obligations arising out of India’s USD 7.6 billion deal to buy 36 Rafale fighter jets. The JV was formed in 2017.

With Prime Minister Narendra Modi scheduled to visit France to be the guest of honour on Bastille Day on July 14, where he is likely to announce another multi-billion dollar deal to buy 26 Rafale Marine fighters, Dassault has started looking for a new Indian partner to execute its present and future offset obligations. 

DRAL, having its production facility at a 62-acre plot in the MIHAAN Special Economic Zone in Nagpur, is currently engaged in the assembly of Rafale components, including engine doors, rudder, elevons, windshields, canopy, etc. 

Sources said the fate of Reliance Aerostructure’s tie-up with another French company, Thales, is not yet clear. Ambani’s company has another 51-49 JV with Thales — Thales Reliance Defence Systems (TRDS) — in which Reliance is a major partner. TRDS is said to be the most advanced Thales facility outside France. This, too, operates out of MIHAAN and produces Rafale components.

The Centre had introduced a defence offset policy in 2005 which requires all foreign vendors to invest a part of the contract value in India so as to help domestic defence companies grow and facilitate the transfer of technology, besides generating employment.

Firms defaulting on offset obligations

A large number of top foreign defence companies have defaulted on their offset obligations. Repeated requests by the defence ministry to these vendors to fulfil their obligations have fallen on deaf ears

 

 

 

Role of AI in transforming HR practices | The Financial Express

 

 

Area: HRM

 

 

By Ranjini Chakraborty

When the influencer comedian Aiyyo Shraddha’s hilarious video went viral on social media earlier this year, it was not only for the satirical take on layoffs and technology but also for what she spoke about HR that found great resonance with the community.

The caustic humor that she doled out gave a true and interesting perspective on what the year ahead will look like and sure, it did. With the world that is experimenting with Open AI, chatbot like ChatGPT there is much that AI will do to transform and reform the role of HR too.

Here is my dig on the role and influence of ‘AI in HR ‘-

AI in HR

With its direct intervention to monitor employees’ performance through stack ranking, behavior and engagement, AI will provide HR teams with valuable insights.

From analyzing employee data, such as emails, chats and work patterns, to detect signs of burnout, disengagement or even misconduct, AI will work as a measure and tool both to improvise communication between HR and employees across organization structures.

Hiring and other support

AI has always been part of recruitment process – Initial interviews conducted by chatbot are now like a SOP. These processes saves time, enhance efficiency and effectiveness . While these are a few common practice that AI will continue to deliver upon, there will also be wide range of tasks, like employee records management, payroll, onboarding- Verifying employee documents , induction training  and performance management that AI will now take over completely.

Breathing life into employee engagement initiatives

As AI enables HR professionals in leveraging machine learning and algorithms to streamline work processes. It will further help in, if not eliminating, then reducing biases, which will further enhance analysis and decision-making of HR . From customizing employee training programs to , creating data-based career paths for each individual instead of traditional generic approach, AI will help employees gain in-demand business skills per their competence and merit. It will further help scout and match up the talent from within the organization for, growth, promotion, and career development opportunities which would have otherwise gone untapped or unnoticed.

For all of its benefits, AI is not flawless and comes with its own pack of challenges

It still requires human programming, which means there’s room for potential errors and biases. If used too frequently to replace regular human interactions, the technology can start to make work environment distant and alienating.

This, in turn, can be counterproductive and may even hurt employee satisfaction which can lead to negative retention rates in the organization.

The flipside to AI in HR

Too much of AI intervention can be detrimental for the organization and its employees.

So, we need to watch-out for pitfalls before integrating machine learning and other ‘smart’ technology into every process.

Even machines can err!

Errors in the programming can result in misinterpretation of data, so before leaving important reports analysis and decision-making entirely up to AI, we need to keep a close watch on likely errors that can be machine generated.

Perpetuating biases in hiring

Use of AI in sorting candidatures can still unintentionally set biases and eliminate qualified, diverse profiles. This can well happen in case the initial parameters set up in the program include implicit bias. For instance, job boards use algorithms to reflect job applications, however very often the algorithms targets and shows up the profile to the candidates, that it analyzes as the suitable candidature. This process of automation can reinforce stereotypes around specific roles.

Some decisions will always need human intervention

AI is great at analyzing data and presenting useful conclusions however, it can’t always pick on important, non-technical nuances. Ethics, culture, values, emotional intelligence, empathy are much beyond the purview of AI. In HR, we often need to apply the concept of collaborative intelligence specially while delegating tasks. Assigning jobs that require human discernment to read nonverbal cues or tap into emotional intelligence. AI can be a great support mechanism which focuses on optimizing HR-related procedures, however the ones which need emotional response, will always require human intervention.

Increased risks to cybersecurity

Sharing sensitive personal or company data through apps leave us open to cyberattacks or identity theft. As chatbots and applications which are great vehicle for streamlining routine HR transactions can also be easy and soft targets for hackers.

Integration into existing systems

One of the most common challenges faced while implementing AI solutions is integration into the existing systems, such as CRM or ERP systems. The process can be complex and require careful planning to ensure that the AI system works seamlessly with the existing infrastructure. Businesses need to work closely with IT department or a third-party specialist. Compatibility, data transfer, security are few prime challenges that needs to overcome before such integration.

Talent & cost challenges

AI is a complex field and requires specialized skills and expertise. Many businesses struggle to find and hire employees with these skills to develop and implement AI solutions. To overcome this hurdle, businesses need to invest in training and development programs which then comes at a considerable cost.

Developing and implementing AI solutions is often expensive, especially for small and medium-sized businesses. The hardware, software, and personnel cost can add up quickly, making it difficult for some businesses to justify the investment.

Careful evaluation of the ROI vis a via its potential benefits like increased efficiency, improved decision-making, and cost savings can help organization arrive rational conclusions.

In the end,

No technology can fully replace humans. Even AI-based tools that automate tasks are made by humans and, therefore, prone to errors.

While Artificial intelligence will continue to have a huge place in HR. We need to remember the keys that will help us take advantage of its upside and work our ways through the challenges posed by it.

With the right approach, we can integrate AI successfully into HR processes and keep the business ahead in today’s evolving workplace.

 

The Financial Express: Tactical Acumen to Strategic Brilliance: How Indian Air Force Has Upped Its Professional Military Education

 

   

 

Category:  General

 

Faculty note:  Choose to take an unconventional path while honing your management skills. Have you thought about joining the defense services after graduation? 

 

Tactical Acumen to Strategic Brilliance: How Indian Air Force Has Upped Its Professional Military Education

These academies laid a high proportion of weightage to academics and one couldn’t just ignore it.

By Group Captain Praveer Purohit (retd)

One of the many motivations for teenagers to join the National Defence Academy (NDA) was a dream of ‘escape from studies’.One reason was the fallacious assumption that ‘brawns’ were more valued than ‘brains’ in the military. Even those who joined the forces directly after graduation felt that their phase of studies was over. Of course, all these dreams (and day-dreams) came crashing when one joined the NDA or the follow-on academies such as Indian Military Academy (IMA), Air Force Academy (AFA) and Indian Naval Academy (INA). These academies laid a high proportion of weightage to academics and one couldn’t just ignore it.Sharpening of the brain was as important as strengthening the body. So as every officer would attest, their relationship with books, manuals and training notes continued for a long time into their careers.However, the focus predominantly remained on training and very little thought or importance was given to Professional Military Education (PME). One constantly heard the senior leadership talking and writing about ‘Training’ but hardly ever about PME. The late Air Commodore Jasjit Singh passionately espoused the cause of PME, unfortunately without much success, at least till the Kargil conflict. It was the Kargil Review Committee that reiterated the importance of PME and recommended establishing an Indian National Defence University (INDU).  

 

One may well ask if there is any difference in training and PME. Although both are necessary, compatible and complementary, there are differences. Training is more focussed on the immediate skills necessary for a warrior. It seeks to develop those psycho-motor and technical skills required for the job at hand and develop muscle memory. In essence one trains for certainty. PME, on the other hand, includes intellectual, conceptual and ethical foundations of good leadership. PME is more indirect, long term and delves into the ‘why’ of issues. It seeks to develop critical thinking skills and an ability to think strategically. PME thus aims to prepare and educate leaders to deal with uncertainty. Traditionally, however most militaries including in India have tended to favour training over education. Consequently, the cultivation of a strategic mindset, honing of intellectual ability and tolerance for ambiguity have suffered.

Fortunately, the Indian Air Force (IAF) was quick to realize the importance of PME. A beginning was made when it became the first service to facilitate the establishment in 2001 of a think tank – Centre for Air Power Studies (CAPS). Housed within the precincts of Western Air Command, and often mistaken as an ‘in-house’ think tank of IAF, CAPS is autonomous and independent but has a symbiotic and mutually enriching relationship with IAF. Towards educating the IAF personnel on matters of national security, geo-politics, aerospace power, nuclear issues and diplomacy, CAPS has been conducting seminars and conferences on mutually agreed themes with all Commands of the IAF. These seminars have been very useful in acquiring knowledge that shapes a strategic bent of mind. IAF officers, both serving and retired, have been undergoing fellowships in CAPS and have published books/papers that are intuitive, analytical and a treasure trove of knowledge

Various ‘in-house’ measures have been taken by IAF towards PME in the last decade and half. Till 2006, the first PME programme/course that officers were exposed to was the Air Staff Course at DSSC, Wellington. Based on a tough selection criterion, only a small percentage of officers could undergo the course. A large number of officers never got exposed to strategic studies, geo-politics, war studies, and higher direction of war. Alive to these lacuna, a major revamp took place in 2007, when for the first time, the IAF introduced two mandatory PME courses for all its junior officers. The first was Basic Air Staff Course (BASCO) for Flight Lieutenants and the other was Intermediate Air Staff Course (ISCO) for Squadron Leaders. These courses had a year long distance learning component followed by a four week contact programme. The methodology adopted was that of self-learning, peer learning, sharing of domain knowledge and mentoring/ guidance by highly experienced, qualified and competent officers posted as Directing Staff or DS (akin to Professors in civil parlance) in the Faculty of Leadership & Air Power at Air Force Administrative College. The USP of these courses is that all officers are exposed to subjects such as Regional Studies, Area Studies, Geo-Politics, International Relations, Air Campaigns, Military History, Jointness, and Leadership. The coursework involves intense reading and research and sows the seeds of strategic thinking at an early age in service. Word about the quality of education and the good outcome has spread beyond the shores of India. These courses are much sought after and subscribed by officers from countries such as Mauritius, Bangladesh, Benin, Nepal, Afghanistan and many others. The Indian Army (IA) sent officers for these courses in 2019. According to those in the know, the feedback from IAF officers, foreign officers and IA has been exceptionally good. Unfortunately, for some inexplicable reason, BASCO was done away with, in 2021. However, the ISCO continues to be an enriching course and ‘lead-in’ step towards the Air Staff Course.

At the middle level(senior Squadron Leaders and Wing Commanders), the IAF PME programme comprises the Air Staff Course and Defence Services Technical Staff Course (DSTSC). The next major structured PME programme is the Higher Air Command Course (HACC). The HACC is almost a year long course for meritorious Group Captains and equivalents in the other two services conducted at College of Air Warfare (CAW). The IAF also sends some of its meritorious Group Captains to Army War College for the Higher Command Course, Naval War College for the Naval Higher Command Course and College of Defence Management for the Higher Defence Management Course.Besides these programmes, select IAF officers also attend the Nuclear Strategy Capsule (conducted by CAPS), National Defence & Strategic Studies Course at National Defence College and Advance Professional Programme in Public Administrationat Indian Institute of Public Administration. To further enhance PME andpreventstasis, one more programme was started in 2022. Called the Warfare and Aerospace Strategy Programme (WASP), it is a strategic education programme of 15 weeks duration and is structured to provide the participants with a deep understanding of strategy. The broader aim is to nurture critical thinkers who can blend cross-domain knowledge to generate policy-driving ideas at the strategic level. WASP aims to take ‘self-learning’ that began with BASCO and ISCO to an even higher strategic level. The second WASP cohort, jointly conducted by CAW and CAPS concluded with a capstone seminar on 28 June. Once again, IAF is the first and only service to take-off on such a venture.

However, there are some systemic challenges that the service will have to overcome. The importance of PME is still not widely understood and confused with training. Often, PME is sacrificed at the altar of immediate tactical requirements. It is necessary for the rank and file to take a long term view of PME rather than be happy with short term training outcomes. The PME will need continuous evolution to focus on forward thinking and resist conservatism. Military culture lays emphasis on action and not reflection. PME is about reflection and thus prone to a cultural bias. The service needs to nurture and ensure that officers who are well read, inquisitive, creative and have a strategic bent of mind are not disadvantaged in their promotional prospects. The IAF will have to guard itself against rewarding those who are mere echo chambers of their seniors. Most importantly, it will require the leadership at all levels to take ownership and continuously strive to inculcate intellectual dynamism.

The impetus given to PME by IAF since the last 15 years is laudable. The knowledge band-width of its officers in matters strategic has substantially increased. Young officers today are more aware of geo-politics. The Chief of Air Staff (CAS) has promulgated a CAS reading list. Every officer is expected to read at least one book. This is a step in the right direction, especially since reading books has become rare due to social media. Educational courses such as ISCO, HACC and WASP have unleashed the insatiable hunger for more knowledge. This augurs well because in fulfilling this hunger, more numbers will transform from mere ‘air warriors’ to ‘scholar-air warriors.’The IAF Doctrine 2022, envisions itself to be an agile and adaptable air force that provides decisive aerospace power in furtherance of our national interests.Fulfilling this vision requires leaders empowered with strategic brilliance and not mere tactical acumen. IAF’s endeavour to upgrade its PME is therefore a step in the right direction to develop the critical mass of strategic leadership required in the service of the nation.  

The author served in the IAF for over three decades. He has a rich experience in operations, PME and training. A regular contributor to CASS Journal, his papers have also been published in CAW Journal and USI Journal. He is the winner of the Lt Gen SL Menezes Memorial Essay Competition 2020 conducted by USI.

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