Google Maps can now write captions for your photos using AI
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Asylon and Thrive Logic bring physical AI to enterprise perimeter security
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Why UiPath is re-designing its platform around agents that build automations, not just run them
diginomica.com
A teenage Minecraft YouTuber raised $1,234,567 for a meme prediction market called Giggles. It broke me.
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4 days left to save close to $500 on TechCrunch Disrupt 2026 passes
techcrunch.com
Google Maps can now write captions for your photos using AI
Asylon and Thrive Logic bring physical AI to enterprise perimeter security
Why UiPath is re-designing its platform around agents that build automations, not just run them
A teenage Minecraft YouTuber raised $1,234,567 for a meme prediction market called Giggles. It broke me.
4 days left to save close to $500 on TechCrunch Disrupt 2026 passes
Google Maps can now write captions for your photos using AI
Asylon and Thrive Logic bring physical AI to enterprise perimeter security
Why UiPath is re-designing its platform around agents that build automations, not just run them
A teenage Minecraft YouTuber raised $1,234,567 for a meme prediction market called Giggles. It broke me.
4 days left to save close to $500 on TechCrunch Disrupt 2026 passes

Editor's Letter

Welcome to the June edition of Illuminar, This month’s cover story explores “The State of AI: Between Innovation and Ethical Dilemmas.” As enterprises accelerate their AI adoption journeys, we examine how organizations can walk the fine line between transformative innovation and responsible implementation. From algorithmic bias to explainability and compliance, this feature delves into the pressing questions that decision-makers must confront as AI moves from pilot to scale.

We’re also excited to introduce a brand-new section: Tech Tidbits—a quick, digestible roundup of trends, tools, and takeaways from the world of digital. Think of it as your byte-sized boost of insight for the month.

In our Interview from the Past segment, we revisit a conversation with Mr. Deepak Kumbhat, former CIO at SRF Ltd., where he discusses leading with technology in a complex manufacturing environment, driving process excellence, and enabling digital transformation with clarity and purpose.

As always, we deeply appreciate your continued readership and engagement. Your feedback and ideas keep Illuminar evolving and relevant.

Wishing you and your families good health, happiness, and success.

Have a great day and stay safe!

Best regards,

Srividya Kannan

Editor

The State of AI: Between Innovation and Ethical Dilemmas

Artificial Intelligence (AI) is no longer the technology of the future—it’s here, transforming industries, changing the way businesses operate, and reshaping society. However, with its rise comes a critical question: how do we harness its power responsibly?

AI promises incredible advancements in productivity, automation, and insight generation. Still, as we rush to deploy these technologies, the ethical implications become more pronounced. In this exploration, we’ll address the landscape of AI adoption, the categories of organizations leveraging it, the emerging ethical concerns, and what the future holds.

Current AI Adoption: A Widespread but Uneven Landscape

AI’s adoption spans a variety of industries, each using the technology for different purposes. From improving customer experiences to driving operational efficiencies, AI applications are varied, but the level of sophistication and scale of implementation is not uniform.

  • Technology and IT Sector: In the tech world, AI is the backbone of many innovations—whether it’s the automation of cloud services, cybersecurity, or product development. For instance, tech companies like Google, Microsoft, and Amazon use AI to power their data centers, optimize supply chains, and offer personalized experiences.

  • Stats from Gartner: “By 2025, 70% of organizations will shift from pilot to full-scale AI deployment, but 40% will fail to achieve expected business value due to lack of talent, operational and data maturity.” This reflects the tech sector’s struggle to bridge the gap between AI’s potential and its real-world implementation.

  • Healthcare: AI in healthcare is transforming diagnostics, treatment planning, and patient care. From predictive analytics that identify at-risk patients to AI-powered robotic surgery, the healthcare sector is deeply invested in AI’s promise.

  • AI for Diagnostics: Algorithms are increasingly being used to analyze medical images or genetic data, offering more accurate predictions than traditional methods.

  • Personalized Treatment: AI models can analyze vast datasets to provide personalized care recommendations, optimizing treatment based on the unique needs of each patient.

  • Finance and Insurance: In finance, AI is deployed for fraud detection, algorithmic trading, credit scoring, and customer service automation. Machine learning models can detect anomalous transactions or forecast market trends, giving financial firms a competitive edge.

  • AI in Fraud Prevention: Financial institutions like JPMorgan Chase and Mastercard leverage AI to monitor transactions for unusual patterns and predict fraudulent activities in real-time.

  • Manufacturing and RetailAI is revolutionizing manufacturing with predictive maintenance, supply chain optimization, and process automation. In retail, AI enables hyper-personalization, from recommending products to dynamic pricing strategies.

  • Supply Chain Optimization: AI models in manufacturing forecast demand, optimize stock levels, and streamline logistics. In retail, brands like Walmart use AI to manage inventory and predict consumer behavior more accurately.

  • Public Sector and Governance: Government agencies are increasingly adopting AI for predictive policing, resource allocation, and public health initiatives. However, the public sector’s AI adoption is often slower, hampered by budget constraints and bureaucratic challenges.

The Ethical Dilemmas of AI: From Bias to Privacy Concerns

AI’s widespread implementation brings with it a host of ethical challenges. From algorithmic biases that discriminate against minorities to concerns over privacy violations, the ethical landscape of AI is complex and, at times, problematic.

Algorithmic Bias

One of the most pressing ethical concerns in AI is the issue of bias. AI systems are often trained on historical data that may reflect societal inequalities. If the data used to train algorithms is biased, the outcomes will be too.

For example, in criminal justice, predictive policing algorithms have been found to disproportionately target minority communities. Similarly, facial recognition systems have been criticized for inaccuracies in identifying people of color.

The risk of reinforcing discrimination through AI is a major concern. Gartner’s findings support this:

“By 2025, 60% of AI projects will fail to meet ethical or regulatory standards due to insufficient governance frameworks.”
— Gartner, 2023

Privacy Violations

As AI systems gather and process massive amounts of data, the issue of privacy becomes more urgent. Many AI applications, particularly in sectors like healthcare, finance, and retail, require access to sensitive personal information. Without robust data protection policies, this opens the door to misuse, hacking, or unintended surveillance.

Accountability and Transparency

AI’s “black box” nature—where decision-making processes are often not fully understood even by the developers themselves—raises questions about accountability. Who is responsible if an AI system causes harm? Can organizations defend AI-driven decisions when they can’t explain how the model arrived at its conclusions?

For example, in credit scoring, AI may decide to deny loans based on historical patterns. If an applicant challenges the decision, how can a company explain the reasoning behind the rejection, especially if the algorithm is opaque?

The Risk of Job Displacement

As AI continues to automate tasks across industries, concerns about job displacement are rising. In manufacturing, autonomous robots can replace human workers on assembly lines. In customer service, chatbots and virtual assistants handle many of the tasks that once required human interaction. While AI can improve efficiency, the social consequences of widespread job loss are significant.

The Future of AI: What Lies Ahead?

The future of AI holds both immense promise and daunting challenges. While the technology is poised to grow exponentially, how it’s deployed will determine whether its impact is positive or harmful.

  • AI as an Augmented Workforce: The future is likely to see a shift from AI replacing humans to AI augmenting human abilities. Rather than robots replacing workers, AI will likely be integrated as a tool that enhances human decision-making.

  • Example: In healthcare, doctors may rely on AI-powered diagnostic tools, but the human element of patient care, empathy, and decision-making will remain crucial. AI will become a collaborative partner, enabling workers to perform tasks faster and more accurately.

  • AI Governance and Regulation: As AI permeates more aspects of our lives, regulating its use will become more important. By 2025, governments are expected to introduce stricter regulations around AI ethics, ensuring that systems are transparent, fair, and accountable. As a result, organizations will need to adopt AI governance frameworks to comply with these evolving standards.

  • Gartner’s Insight: “AI ethics and governance will be a top priority for organizations as they scale AI solutions. By 2025, 60% of AI projects will have failed to meet ethical or regulatory standards due to insufficient governance frameworks.” — Gartner, 2023

  • Autonomous AI and Generalized Intelligence: Looking farther into the future, we might see the rise of autonomous AI systems capable of making independent decisions without human oversight. While we’re currently far from achieving Artificial General Intelligence (AGI), advancements in machine learning and neural networks may push us closer to that reality.

However, this raises critical questions about control, safety, and ethics. Who will control AGI systems? How do we ensure that they align with human values? These are challenges that will need to be addressed as AI evolves.

Conclusion: A Balancing Act Between Promise and Peril

AI holds tremendous potential to improve lives, streamline processes, and enhance business outcomes. Yet, the excitement surrounding AI often overshadows the inherent ethical challenges that come with it. From bias in decision-making to privacy concerns and the risk of job displacement, these issues are not minor inconveniences—they are fundamental questions that must be addressed.

The path forward will require balancing innovation with responsibility. As organizations continue to deploy AI, building robust ethical frameworks, improving transparency, and ensuring accountability will be paramount. AI’s future is not determined solely by its technological capabilities, but by the choices we make today about how to use and govern it.

By investing in AI responsibly, both businesses and society at large can unlock the full potential of this transformative technology while minimizing its risks.

Interview of the Month

Deepak Kumbhat

Director Global Finance Shared Service Organisation
What are the top 5 factors that enterprises should keep in mind to build as they build and mature their shared services?
  • Hire not for today but keeping one eye on tomorrow, resources that are keen to own the process and adapt to changing dynamics.
  • Choose technology intervention wisely – One show does not fit all.
  • Transformation programs – Next generation shared services are essential to stay competitive.
  • Global processes helps in swift actions – Move away from geography specific process to global standard process. Invest in process excellence.
  • Capability development – Drift from being a processing center to capability center. Shared services when act as engine for growth for organisation, that is the real value of SSO. See shared service as strategic partner to CXO.

How do you see the GBS Organization of the future look like? What factors will contribute to its changes?

Emotional intelligence quotient / soft skill, attitude, and courage to take calculated risk are the key differentiators. The GBS become real when you have the right skilled people with upright attitude who are ready to venture into unknown.

People are doing jobs they were hired to do yesterday, but those jobs are gone. We need them to do the job that exists today. If they are not seeing what the opportunities are, they are not empowering themselves and they are not stepping into the gaps.

GBS will see the Emotional intelligence quotient will percolate deep down in the organisation allowing people to take decision and go beyond the standard operating procedure. Workforce will need to see big picture beyond their tasks and look to own the process chain and impact of their work on it.

Until you cross the bridge of your insecurities, you can’t begin to see your infinite possibilities. Growth demands a temporary surrender of security.

Shared service / GBS will be more partners to organisation. We are already seeing the transition from being a processing center to capability centers.

What is the role of a GBS leader in building a digitally mature Organization?

Intelligent Automation is not a “thing to do” anymore but rather a “way we do things.”
Robotic process automation is becoming increasingly recognized as the “gateway drug” to digital transformation.

The role of GBS leader is to find a fine balance between technical know how and functional knowledge so that benefit of technology can be optimized. RPA (Robotic Process Automation) is a technology, and we need to shift to PPA (Pragmatic Process Automation). It is the fine balance of technology and functional knowledge that will make it possible.

As leaders in digital world, we need to grow technofunctional experts to increase success rate of technology implementation in process world (shared services / BPS)

In this fourth industrial revolution it’s the confluence of technology and people that will make the impact.

What are some of the transformation trends in the finance function within large enterprises? In what ways are these contributing to better company performance? What is the role of the Shared service leader in the context of such transformations?

One finance strategy is one theme that has been the core of many other initiatives. One finance means that we have one process across the globe when it comes to accounting policies and procedure / only deviation are regulatory requirements.

This leads to initiatives like one ERP, process design standardization, optimizing process using technology, humbots etc.

Besides RPA the technology that has gained momentum is low code no code also known as citizen program. This allows non-technical people to build reports that otherwise are very manual intensive. e.g., Power BI, Pega’s low-code platform etc.

GBS has now scoped to cover M&A and integration for which the experts are now sitting in shared services. The expertise and standardization are reaping benefits in terms of shorter payback periods.

How is technology contributing to accelerating the pace of such transformations? Do you also see this resulting in better execution?

The world around us is changing so dramatically that real-time customized experiences with predictive insights at unparalleled speed are becoming the norm. This would not have been possible without the technology interventions.

Predictive tools and models combined with AI are fast changing the landscape of shared service, employees are now moving towards knowledge driven initiatives away from rule based and mundane tasks.

RPA as a technology discipline is evolving to support sophisticated processes (not just swivel-chair processes).

Do you believe there will be significant skill gap in the future as a result of such transformations? What if any, will be the kind of skill gaps?

One of the biggest challenges we are facing in the shared service domain is people have become task masters of the broken processes they manage. They does not seem to bother too much about upstream and downstream process or are completely unaware about it. Unawareness is not a bliss and they are caught on wrong foot about the impact of their actions/work on the value chain.

Need is to change the mindset from being task masters to process owners. Wing to wing and not wall to wall should be the approach.

Technology and functions are blending so much that each of us need to have technofunctional skills. The CFO’s of the future must have deep insights in the technology. This trend is cascading down to the lower echelons.

What are shared service Organizations doing to deliver game changing performance from their shared services? Is there any change in terms of preparedness that you’re seeing now versus say five years back?

Next generation shared service is like a kitchen and can produce many dishes. They don’t come with set menu anymore (scenario 5 years back with well scoped processes). Customisation is the expectations and capabilities need to be built.

The role of the shared service leader is to establish the right enablers at the right level of maturity, they are ultimately creating capacity to do “more with less” and shift capacity from transactions to innovation and growth.

There is much more emphasis on big picture thinking rather than myopic and scope stagnant view. This is impacting how we are skilling employees in shared service. There is great demand of people who are low maintenance, work independently and do not hesitate on the move call. I see lot of focus on Emotional intelligence quotient / soft skill in preparedness of shared services.

How do you believe digital has enabled shared service Organizations to deliver improved business impact? Could you please detail with some examples?

Digital interventions has enabled shared services to move from transactional finance to emerging finance. The roles of shared service employees have changed from back office to partnering with business. Using data, they are doing comparative analysis of within and across business and suggesting actions that business should take.

I work for an engineering consultant organisation and the shared services has work with engineers to develop a model on how change in design and material real time can be reflected in financial term giving customers the data points to make decisions immediately.

How do people within shared services need to be reskilled to get ready for the future? What should enterprises be doing about it?

My father struggled with computer, but my son learned to browse google first and leaned the English alphabets later.

In last five years both the pilots and cameramen who combined together to take aerial pictures lost the jobs because we have drones who can do their jobs.

Reskilling employees is not a choice but a mandate. “A CFO questioned the CEO why we are investing so much on people reskilling, if they leave, we will lose big time. The CEO replied what if they don’t leave?”

The easiest way to reskill people is to move performers from low skilled jobs to high skilled jobs. Don’t wait for them to be 100% ready, if they are 60% ready put them in the ocean, they will find the destination.

Several Organizations take a POC route before making RPA decisions. What is your view about taking this route? Is this a good practice?

The problem is not with proof of concept but with the decision on what goes in as proof of concept. The organisation often chooses a very small process / streamlined process for POC which even if it succeed is still a failure.

When they then go ahead with the bigger, more complex, cross functional process which are nonstandard process their success rate of implementation remains poor.

The distinction between RPA as a task specific tool or a value driven competency is important to grasp, as it separate those who will ride the digital wave at its breaking edge from those safely playing inside the breakwater.

What are some of the good practices for designing and deploying an RPA? What are some of the key business benefits of an RPA?
  • Optimize the process first before you apply RPA
  • RPA needs skill to be a benefit case. You must have a volume where you want RPA.
  • RPA is just a technology, don’t ignore the change management
  • There is a myth that ones you have implemented RPA its done, the fact is BOT need maintenance.

Like any other technology RPA free up resources do value add work, knowledge intensive work and of course, make the business more competitive by becoming efficient.

Tech Tidbits

AI-Driven Financial Forecasting: A New Era for CFOs

  • AI and machine learning are revolutionizing how CFOs approach financial forecasting and budgeting.
  • With tools like Anaplan and Oracle, companies are using AI to enhance predictive accuracy, automate financial reporting, and identify trends that may have been overlooked.
“By 2026, 90% of finance functions will deploy at least one AI-enabled technology solution, but less than 10% of functions will see headcount reductions”, according to Gartner, Inc.

Blockchain for Financial Transparency: Tackling Fraud and Compliance

  • Blockchain technology is being embraced by CFOs and CIOs for its ability to enhance transparency, security, and compliance in financial transactions.
  • By implementing blockchain in supply chain payments, audit trails, and contract management, companies can ensure real-time, tamper-proof records.
  • This has significant implications for compliance, particularly in industries like banking and insurance, where transparency and regulatory compliance are critical.

Cloud Migration: The Hidden Financial Benefits for CFOs

  • While CIOs are leading cloud migration initiatives, CFOs should also pay attention to the financial benefits.
  • Moving to a cloud-based ERP (like SAP S/4HANA Cloud or Oracle ERP Cloud) not only reduces the cost of maintaining on-premises infrastructure but can also lead to significant savings in energy consumption and IT staff costs.
  • 90% of Organizations Will Adopt Hybrid Cloud Through 2027
  • For CFOs, this represents a direct path to cost reduction and better financial visibility.

Cybersecurity Automation: Protecting Financial Assets

  • As cybersecurity threats grow, CFOs and CIOs must prioritize protecting financial and corporate data. Fortunately, automation is increasingly being applied to cybersecurity.
  • AI-driven security tools, such as CrowdStrike and Darktrace, can predict, detect, and respond to security threats in real-time.
  • The global average cost of a data breach in 2024 reached USD 4.9 million—a 10% increase over last year and the highest total ever recorded.
  • Moreover, it ensures that compliance with global data protection laws like GDPR remains intact.
IBM Report: Cybersecurity investment is projected to reach $215 billion by 2025.