Agile vs AI: Did the Industry Stop Talking About Scrum?

 

Intro

Around 2020, during the pandemic and the remote-work wave, Agile and Scrum were the hottest buzzwords in IT. Every other JD had “CSM preferred,” every discussion was about stand-ups, sprints, and PI planning.

Fast-forward to 2024–2025 and the conversation has flipped. AI, GenAI, and ML are everywhere. Job posts, client RFPs, internal trainings, even water-cooler talk: almost everything has “AI” in it. Scrum and Agile feel strangely quiet.

If you are someone who invested heavily in Agile, this shift can feel uncomfortable:

• AI certifications and bootcamps are booming.

• Many companies now specifically ask for AI/ML exposure, AI product experience, or at least AI awareness. 

• Meanwhile, you rarely hear “we are hiring a Scrum Master” as loudly as before.

So what is actually happening? Is Agile dead? Why this sudden obsession with AI? And, most importantly, what should Agile and Scrum professionals do to stay relevant?

Let us break it down.

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1. The Reality: Agile Has Not Disappeared – It Has Become “Assumed”

First, it is important to separate hype in conversations from actual practices in organizations.

Surveys still show that Agile remains the dominant way of delivering software and digital products. Adoption levels have been consistently above 90% since 2018, with no meaningful decline.  Many enterprises now use Agile (Scrum, Kanban, SAFe, etc.) not as an experiment, but as the default.

In other words:

• In 2015, “We are Agile” was a differentiator.

• In 2020, it became a core transformation theme, especially in the pandemic era.

• By 2024–2025, Agile is infrastructure. It is assumed.

Recruiters do not always write “Agile” and “Scrum” in bold because they expect you already have that experience if you come from modern IT / digital roles. Job descriptions are now crowded with the “new differentiator” – AI.

So Agile has not died. It has moved from headline to foundation.

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2. Why AI Certifications and Skills Suddenly Exploded

The AI boom is not just hype; there is strong data behind the demand:

• Global postings requiring AI skills surged around 60% year-on-year in 2024, while overall job ads grew only around 1–2%. 

• World Economic Forum’s Future of Jobs reports show AI, big data, and related roles (AI specialists, data scientists, big data professionals) among the fastest-growing job families for the next five years. World Economic Forum+2World Economic Forum+2

• In India and other markets, AI/Data roles have seen 30–45% year-on-year growth, especially post-GenAI. 

Why this sudden shift from “Agile certification” to “AI certification”?

2.1. A New Technology Platform Moment

Agile is a way of working. AI is a technology platform that changes what we can build and how fast we can build it.

Just like cloud, mobile, and internet were platform moments, AI is now the next big “platform layer.” Organizations are re-architecting:

• Customer journeys with AI-driven personalization

• Operations with AI-driven automation and decisioning

• Products with AI copilots, recommendation engines, and intelligent workflows

Naturally, boards and CXOs are asking:

“What is our AI strategy?”
“Where is AI in this program?”

You rarely hear a board member ask, “Where is Scrum in this program?” Agile is important, but it is not a board-level buzzword anymore. AI is.

2.2. Competitive Pressure and FOMO

Another driver is plain Fear of Missing Out:

• When one competitor launches an AI-enabled experience, others quickly want to follow.

• Vendors, cloud providers, and consulting firms aggressively market AI solutions, accelerators, and reference architectures.

• Media narratives amplify every AI success story – from coding copilots to AI-driven customer service.

This creates a cycle where AI becomes a “must have” talking point in every roadmap, which then reflects in job postings, internal L&D programs, and certification demand.

2.3. Tooling Has Matured

With tools like ChatGPT, Copilot, Claude, and domain-specific AI assistants, AI moved from research labs to everyday tools. That:

• Lowers the entry barrier.

• Increases curiosity among non-technical professionals.

• Naturally pushes them towards AI awareness and certification programs.

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3. How Long Will the AI Wave Last?

If we treat AI as just another buzzword, we might assume it will fade in 2–3 years. But if we treat it as a platform shift (like cloud or the internet), the answer is different.

Evidence so far suggests:

• Employers expect AI and information processing technologies to transform businesses by 2030; more than 80% of companies foresee AI significantly changing their operations. 

• Jobs linked to AI, data, and analytics are projected to grow strongly over the next 5–10 years, even as some routine roles are automated. World Economic Forum+1

So, the hype spike (certifications, marketing buzz, noisy LinkedIn posts) may calm down in 2–3 years, but the structural demand for AI-related skills and literacy is likely to remain for at least a decade.

In other words:

• The AI “noise level” may reduce.

• The AI “skill requirement” will stay – and gradually become as “assumed” as Agile is today.

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4. Where Does This Leave Agile & Scrum Professionals?

Here is the crucial point:

AI is not replacing Agile. AI is being implemented inside Agile environments.

Gartner estimates that AI will handle a portion of Agile documentation and reporting, but a significant part of Agile roles still require human facilitation, problem-solving, and stakeholder management. Target Agility+2vinsys.com+2

Similarly, research on future skills continues to highlight:

• Analytical thinking

• Creative thinking

• Leadership and social influence

• Collaboration and communication

as top priority skills alongside AI and big data. World Economic Forum+1

These are exactly where strong Scrum Masters, Agile Coaches, and Product Owners excel.

So if you are good in Agile or Scrum, you already have:

• Experience managing change and uncertainty.

• Skills in iterative delivery and experimentation.

• Stakeholder facilitation and conflict management capability.

• A mindset of continuous improvement.

Those are gold in an AI-driven world.

The real question is not “Agile or AI?” but “How do I combine Agile + AI?”

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5. How Agile Professionals Can Stay Relevant (and Actually Gain Advantage)

Here is a structured way to think about your next 12–24 months if you come from an Agile/Scrum background.

5.1. Become “AI-Literate”, Not Necessarily an AI Engineer

You do not need to become a data scientist. But you should understand:

• Basic AI / ML concepts – classification, prediction, generative AI, LLMs, embeddings.

• Typical AI use cases in your domain – risk scoring in banking, customer segmentation in retail, chatbot journeys, document processing, etc.

• Limitations and risks – bias, hallucination, explainability, data privacy, regulatory impact.

Target outcomes:

• You can participate in conversations on AI use cases without feeling lost.

• You can translate business problems into candidate AI use cases.

• You can question unrealistic AI promises from vendors.

A practical approach:

• Take a “AI for Product / Business” or “AI for Non-Technical Professionals” course.

• Experiment daily with an LLM (ChatGPT, Claude, Gemini, Copilot) on your real project scenarios.

5.2. Reposition Yourself: From “Scrum Master” to “AI-Enabled Change Leader”

Update how you brand yourself:

Instead of only:

“Scrum Master / Agile Coach – Facilitating sprints, ceremonies, and removing impediments.”

Start positioning as:

“Agile Delivery Lead driving AI-enabled products, helping cross-functional teams experiment, validate, and scale AI use cases safely.”

Concretely, you can:

• Highlight any work where teams used analytics, automation, or AI-adjacent tools (RPA, chatbots, decision engines).

• Describe how you helped teams integrate new technology into existing workflows.

• Show that you understand both ways of working (Agile) and ways of building (AI/ML-enabled solutions).

Roles where this combination is powerful:

• AI Product Owner / AI Product Manager Product School

• AI Delivery Lead or GenAI Program Lead

• Agile Coach for Data & AI Platforms

• Transformation Lead for AI-driven initiatives

5.3. Use AI to Amplify Your Existing Agile Strengths

Apply AI tools directly in your Agile work:

• Use LLMs to draft user stories, acceptance criteria, test scenarios, and release notes – then refine them with your human judgment.

• Use AI to analyze feedback from retrospectives or customer surveys to identify patterns faster.

• Experiment with AI assistants that can act as “backlog copilots,” helping you cluster, de-duplicate, and prioritize items.

This does two things:

1. You become more productive and data-driven in your existing role.

2. You build real, hands-on stories of “how I used AI practically in Agile delivery,” which recruiters and hiring managers love.

5.4. Double Down on the Human Skills AI Cannot Replace Easily

While everyone else rushes to add “Prompt Engineering” on their CV, quietly strengthen what AI still cannot do well:

• Deep stakeholder management and conflict resolution.

• Coaching individuals and teams through fear and change.

• Navigating organizational politics in large transformations.

• Ethical decision-making, trade-offs, and prioritization across conflicting interests.

Most credible research agrees that these human skills will grow in importance as AI automates routine tasks. World Economic Forum+2World Economic Forum+2

Agile professionals already operate in this space. Make it visible, measurable, and part of your professional brand.

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6. Summary: From “Agile vs AI” to “Agile for AI”

To conclude:

• Agile has not gone away; it has become assumed infrastructure for modern delivery.

• AI is the new platform wave driving demand for certifications, roles, and executive attention.

• This AI wave is not a short-term bubble; the hype will cool but the structural demand for AI literacy will remain for 5–10 years at least.

• For Agile and Scrum professionals, the winning strategy is not to compete with AI, but to lead AI-driven change using Agile principles.

If you already know how to:

• Work iteratively

• Manage uncertainty

• Align stakeholders

• Deliver value in increments

you are better placed than many others to become the bridge between AI possibilities and real business outcomes.

The future is not “Scrum vs AI.”
The future is “AI products delivered the Agile way – by people who understand both.”

What Digital Transformation Taught Me: A Journey Through Stakeholders, Change, and Real Value

 In today’s rapidly evolving digital landscape, transformation has become more than a buzzword—it’s a reality that most organizations are either embarking on or navigating through. I’ve had the opportunity to work at the heart of multiple digital transformation initiatives in the financial and banking sectors. Each experience reshaped not just the product, but also how I think, work, and collaborate.

Here’s what I learned along the way:

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🔹 1. Stakeholder Alignment Is the First Win

In any transformation program, your first real challenge isn't the technology—it's the people. Every stakeholder group comes with its own set of expectations, fears, and motivations.

• For the Operations Team, it might be about reducing manual work.

• For Compliance, it's about control and audit trails.

• For IT, it's about system stability and performance.

• For Frontline Staff, it's usability and time savings.

What worked for me was taking time to listen, understand their world, and map their needs into the broader transformation goals. When stakeholders feel heard, they shift from resistance to ownership.

📌 Lesson: Don’t "gather" requirements—co-create them.

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🔹 2. Constant Change Is Not a Threat—It's Fuel

One common pattern I observed: business goals evolve mid-project. Regulatory changes, market shifts, or leadership vision tweaks—there's always something that alters the course.

Initially, this felt disruptive. But over time, I embraced change as a sign that we were responsive and improving. Our backlogs became living documents, and our roadmaps had contingency baked in.

I started practicing iterative planning with regular check-ins to reassess scope, reprioritize, and keep everyone aligned. This made the product stronger and the delivery more relevant.

📌 Lesson: Adaptability isn’t a project trait—it’s a leadership mindset.

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🔹 3. Storytelling Over Specs: Bringing Vision to Life

One of our key initiatives involved enabling a 360-degree view of the customer for relationship managers. It had brilliant features, but leadership was lukewarm—until I created a simple storyboard showing how a manager could reduce call handling time and upsell with confidence.

The moment they saw the impact, everything changed—budget approvals, faster decisions, and real excitement.

📌 Lesson: When you’re building something complex, tell stories, not just share screens.

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🔹 4. Tech Is Just a Tool. People Are the Key

Even the best solutions will fail without adoption. In one instance, we rolled out an intelligent dashboard, but usage was low in the first few weeks. The issue? Lack of onboarding.

We focused next on:

• Champion users who could train others

• Celebrating small wins in town halls

• Incorporating frontline feedback into future sprints

Within two months, the dashboard was part of daily routines.

📌 Lesson: Trust, training, and user empathy matter more than just delivering features.

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🔹 5. My Role Evolved Too

Digital transformation didn’t just change our systems—it changed me. I evolved from being a Business Analyst focused on requirements to someone who:

• Translated value between business and tech

• Bridged communication gaps

• Enabled change beyond documentation

I became a change enabler—not just a delivery resource.

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🔍 Final Reflection: The Human Side of Digital Transformation

The most successful digital transformations aren’t the ones with the flashiest tools, but the ones where people are empowered, aligned, and excited about the journey.

So whether you’re a Product Owner, Analyst, or Leader—your biggest impact may not be in what you deliver, but in how you bring people together to shape the change.

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👇 What’s Your Take?

Have you been part of a digital transformation? What lessons did you learn about dealing with stakeholders or change?

Let’s connect and share.

#DigitalTransformation #Leadership #ChangeManagement #StakeholderEngagement #BusinessAnalysis #AgileMindset #ProductThinking #BAJourney

🎬 Lights, Camera, Agile!

 

What Hollywood Movies Teach Us About Agile Ways of Working

In the world of software development, Agile has become more than just a buzzword—it’s a mindset, a way of thinking, and a blueprint for navigating uncertainty with speed and collaboration. But what if I told you the essence of Agile is not confined to tech teams or sprint boards?

Look closer, and you’ll find Agile thinking embedded in an unlikely place: Hollywood.

Yes, the land of blockbusters, actors, and ever-changing scripts mirrors the Agile world in fascinating ways. Let’s take a cinematic journey through Agile principles, roles, and rituals—through the lens of some of our favorite films.

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🎭 Agile Principle 1: Individuals & Interactions Over Processes & Tools

“You don’t win with fancy tools. You win with great people.”

Take Ocean’s Eleven. The crew doesn’t rely on tech alone—they rely on each other. Their trust, communication, and clarity of roles make the impossible heist possible.

Agile teams are no different. While tools are important, collaboration and people dynamics drive success.

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🌀 Agile Principle 2: Responding to Change Over Following a Plan

“Plans are nothing; planning is everything.”

Ask any director: film production never goes exactly as planned. Weather shifts, actors improvise, studio notes come in. Adaptability is survival.

Case in point? Titanic’s iconic “draw me like one of your French girls” scene—it wasn’t in the original script. Agile teams too, respond to change—not resist it.

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💬 Agile Principle 3: Customer Collaboration Over Contract Negotiation

Deadpool wouldn't be what it is without fan input. The tone, the humor—it all clicked because the creators listened to their audience.

In Agile, involving customers early and often leads to better outcomes. It’s about co-creation, not just delivery.

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⚙ Agile Principle 4: Working Software Over Comprehensive Documentation

Imagine a filmmaker obsessing over script documentation while ignoring dailies. Sounds absurd, right?

Mad Max: Fury Road was built more on storyboards than a full script. Like Agile teams, they prioritized visible progress over detailed paperwork.

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🎥 Agile Roles = Film Crew Roles

Agile Role	Film Crew Equivalent
Product Owner	Director (holds the vision)
Scrum Master	Producer (removes obstacles, keeps flow)
Development Team	Cast & Crew (brings the vision to life)

Much like a movie set, Agile teams thrive when everyone understands their part and plays it in sync.

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🔁 Sprints = Shooting Scenes

Agile works in iterations, just like films are shot scene-by-scene, not all at once. Directors review scenes, reshoot if needed, and make course corrections. Agile teams do the same through sprint reviews, retrospectives, and feedback loops.

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🚧 Blockers = Production Delays

Every movie faces challenges—weather, budget, creative conflict. Agile teams too face blockers—technical issues, unclear requirements, resource gaps.

In both cases, the ability to identify, escalate, and resolve fast is the real superpower.

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🧠 Fail Fast, Learn Faster = Sequel Thinking

Even Hollywood learns from failure. The first movie may flop, but a reboot or sequel often redeems it.

Just like Agile’s philosophy: fail early, learn quickly, improve continuously. Every sprint is a sequel—and a chance to do better.

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🎬 The Final Scene

Agile isn’t confined to tech teams. It’s a mindset you’ll see on film sets, in creative processes, and across industries that require collaboration, iteration, and the courage to adapt.

So the next time you’re watching your favorite movie, ask yourself:

“What would an Agile team do in this situation?”

Because whether you’re shipping code or shooting scenes, Agile thinking keeps the story moving—and the audience (or customer) satisfied.

From Paper Trails to Digital Rails: How AI & BI Can Transform the Rigid Workflows of BFSI and Beyond

 

Introduction

For decades, Indian banks and insurance companies have relied on paper-heavy processes that are time-consuming, error-prone, and environmentally unsustainable. While recent efforts by private players in the BFSI (Banking, Financial Services, and Insurance) sector have introduced digital onboarding, Video KYC, and API integrations with Aadhaar and PAN databases, the transformation remains surface-level in many places.

The question is: Why are we still settling for partial digitisation when AI and BI can take us so much further?

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The Paper Problem

Visit any branch of a public bank or a regional insurance office, and you'll likely find shelves stacked with physical forms, policy documents, and loan files. The process of retrieval, verification, and audit often leads to duplication of efforts, bottlenecks, and missed customer SLAs.

Not to mention — the cost of paper, printing, storage, and compliance management adds up significantly over time.

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Where AI & BI Step In

1. Intelligent Document Processing (IDP)

AI-powered tools like OCR, NLP, and machine learning can digitize handwritten or printed forms. Beyond mere scanning, these tools can:

• Extract key information

• Classify documents

• Auto-validate against back-end systems (e.g., Aadhaar, PAN)

This eliminates the need for manual data entry, reducing errors and processing time.

2. Predictive Analytics and BI Dashboards

Once data is digitized, BI tools like Power BI or Tableau can:

• Help identify bottlenecks in document processing

• Track SLA breaches in real-time

• Predict demand for policy updates or customer support needs

Banks and insurance firms can then proactively allocate resources or automate repetitive tasks.

3. Automating Compliance and Audit Trails

AI can monitor transactions and document flows to auto-flag anomalies, missing documentation, or policy deviations. A complete digital audit trail ensures transparency and reduces regulatory risk.

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Case in Point: Progress in Digital Onboarding

Several private banks and insurance companies have started:

• API integrations with Aadhaar and PAN for identity verification

• E-signature systems for secure document approvals

• Video KYC platforms for real-time customer onboarding

But this innovation usually ends at the onboarding phase. Claims, servicing, loan restructuring, and customer queries often still depend on back-end teams shuffling through scanned PDFs or, worse, physical files.

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The Road Ahead: Opportunities for Transformation

• Policy Renewals: Automate reminders and enable e-signature-based approvals.

• Claims Processing: Use AI to detect document fraud and fast-track genuine claims.

• Loan Underwriting: Train ML models to assess risk profiles without printing income proofs or balance sheets.

• Branch Operations: Use BI to track paper usage, reduce waste, and optimize process flows.

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Conclusion: From Compliance to Confidence

Digitisation is no longer optional — it’s the bridge between regulatory compliance and customer confidence. By embracing AI and BI, the BFSI sector can finally break free from its rigid, paper-reliant past and stride confidently into an efficient, digital-first future.

Has India Missed the Bus on AI Adoption?

Has India Missed the Bus on AI Adoption?

The Reluctance of Indian Businesses in the Age of Digital Transformation

The global business landscape has been abuzz with the potential of Artificial Intelligence (AI) and Machine Learning (ML) for several years now. With technologies like ChatGPT, predictive analytics, and automation reshaping industries, countries worldwide have been quick to integrate these advancements into their business models. However, India—despite being a technology powerhouse—seems to have adopted a cautious approach, particularly in the post-COVID era, when digital transformation was at its peak.

The Paradox of India's Digital Push

During the COVID-19 pandemic, digital transformation became a necessity rather than a choice. Businesses had to pivot to digital channels, adopt remote work tools, and rethink their operational strategies. The Indian market saw a surge in cloud adoption, e-commerce, and digital payments. Yet, when it came to leveraging advanced AI/ML technologies, many organizations hesitated.

This hesitation wasn't just about AI/ML; it extended to Business Intelligence (BI) tools as well. While tools like Power BI were present in many organizations, they were often underutilized. Instead of harnessing advanced analytics, predictive insights, and automation features, many businesses limited their usage to basic reporting and dashboarding.

Understanding the Reluctance

1. Lack of Awareness and Understanding: Many businesses, particularly traditional ones, did not fully understand the potential of AI/ML and advanced BI tools. Without clear insights into how these technologies could solve specific business problems, there was a natural resistance to change.

2. Cost Concerns: Implementing AI/ML solutions often requires upfront investments in technology, infrastructure, and talent. During the pandemic, when conserving cash was critical, many companies preferred safer, incremental improvements over transformational change.

3. Skills Gap: While India boasts a large tech talent pool, the gap in advanced AI/ML skills is significant. Many organizations struggled to find or train the right talent to implement and manage sophisticated AI projects.

4. Limited Vision: The adoption of BI tools like Power BI was often limited to generating static reports rather than leveraging data for strategic decision-making. Many organizations did not push beyond the surface-level functionalities.

5. Risk Aversion: AI/ML adoption involves experimenting with new approaches and sometimes accepting short-term failures for long-term gains. Many Indian businesses, particularly SMEs, preferred tried-and-tested methods over venturing into uncharted technological territories.

The Road Ahead: Catching the AI Bus

India has not entirely missed the bus on AI and digital transformation, but the journey needs acceleration. Businesses need to:

Invest in Education: Upskill employees on advanced technologies and their practical applications.

Adopt a Strategic Approach: Understand specific business challenges and explore how AI/ML and BI tools can provide tailored solutions.

Embrace Change Management: Foster a culture that is open to experimentation and learning from failure.

Leverage Government Initiatives: The Indian government has launched several initiatives to promote AI and digital transformation—businesses should actively participate and benefit.

Conclusion

India's tech ecosystem is robust, and the potential for AI/ML adoption is vast. While the initial hesitancy may have slowed progress, the time is ripe for Indian businesses to shift gears. By fully embracing AI, ML, and advanced BI tools, organizations can transform their operations, drive innovation, and remain competitive in the global market. The bus hasn't left—it's still waiting at the stop. The question is: Are Indian businesses ready to hop on?

About Author: Pratik Pohankar

Digital Transformation Leader, Consultant, Agile Coach and Business Analyst 

www.linkedin.com/in/pratik-pohankar-businessanalyst