We are currently standing at a threshold in human-computer interaction as significant as the invention of the mouse or the transition to the graphical icons we use today. For decades, software has been a passive, reactive tool—a collection of buttons and sliders that sit waiting for a user to click, drag, or type. However, we are moving beyond this era of the “control panel” and into the age of the Agentic User Interface (Agentic UI).

In this new paradigm, the interface is no longer a static surface for manual operations; it is a mediator of intent. This shift transforms software from a reactive utility into a proactive, semi-autonomous digital teammate. Instead of forcing a user to navigate complex hierarchies to find a function, the Agentic UI leverages the reasoning power of large language models to understand high-level goals and orchestrate the necessary actions to achieve them.

Agentic User Interface: A system defined not by its visual aesthetics or layout, but by its underlying capacity for agency—the ability to act independently, purposefully, and semi-autonomously toward a defined objective with minimal human intervention.

This transition marks the end of “navigation-driven layouts” and the beginning of “intent-driven behavior models,” fundamentally altering the way we experience and learn digital systems

Core Philosophy: Intent Over Instruction

The fundamental shift in the Agentic era is the move from Instruction to Intent. In traditional software, the user acts as a “Manager of tiny steps,” responsible for mapping a desired outcome to a rigid sequence of manual commands. In an Agentic system, the user becomes the “Director of the final outcome.”

Consider the task: “Prepare the monthly budget report.”

* GUI (Instruction - The “How”): The user must manually open the database, export raw data, launch a spreadsheet, sort columns, apply formulas, generate a chart, and save as a PDF. The burden of complexity is on the human to remember the steps.

* Agentic (Intent - The “What”): The user states the outcome. The agent abstracts this complexity, autonomously figuring out which databases to query, how to process the data, and which format is required.

Then vs. Now: The Interface Contract

* GUI (Instruction): Rigid, static layouts where users must hunt for features across siloed applications.

* Agentic (Intent): Generative layouts that are assembled in real-time to solve a specific task, breaking down the barriers between applications.

This shift from manual instruction to goal-oriented intent is supported by five core capabilities that define the “teammate” experience.

Conclusion: The New Human-Digital Contract

The Agentic UI revolution represents a fundamental shift from “layout to choreography.” We are moving away from designing static screens and toward designing behavior models. The future of software is defined by a collaboration where the human provides the vision and the digital teammate manages the execution.

By shifting the burden of navigation and instruction to reasoning-capable agents, we unlock a new level of productive potential, allowing humans to focus on the what while the software handles the how.

Key Takeaways:

* Intent-Driven: Users focus on high-level outcomes, while the agent abstracts the technical complexity of the steps.

* Adaptive Processes: Digital media is evolving from static artifacts to operable, adaptive processes that proactively respond to change.

* Reliability through Architecture: Safety and consistency are maintained through “Bounded Generation,” ensuring agents operate within a vetted and secure inventory of components.

The future of software is not just about what a tool can do, but how a teammate can help you achieve what you truly want.

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The AI Product Manager’s role has shifted from writing requirements to “Context Engineering” in the vibe-coding era. Vibe-coding is the transition from AI-assisted autocomplete to generative implementation, where natural language intent replaces manual coding as the primary production bottleneck.

In this new paradigm, traditional Product Requirements Documents (PRDs) are replaced by “Context Fuel”—structured, intention-rich data (such as system boundaries, data flows, and component libraries) fed directly into agentic IDEs like Cursor or Replit. For the modern PM, the objective is no longer “spec writing,” but building functional seeds: working MVPs and prototypes generated through high-fidelity context rather than engineering tickets.

The Rise of the “Builder”: PMs and non-technical roles are Now Developers

The most significant organizational shift of the decade is the transformation of the “knower” into the “doer”.

Traditionally, non-technical roles were mere requesters.

Today, they have all the gears to implement and build prototypes.

When the practitioner with the deepest context can execute the solution, the structural bottleneck of the technical intermediary disappears.

* Product Managers: Now build “functional seeds” and MVPs directly in tools like Cursor, allowing for rapid validation of user flows without consuming engineering sprints.

* HR Professionals: Create custom “connective tissue” apps to link siloed systems (payroll, ATS, benefits) to respond to labor law changes in real-time without waiting for an IT backlog.

* Business Analysts: Automate data pipelines and generate custom scrapers or reporting tools by describing the data source and desired output, bypassing the need for dedicated data engineers.

In this environment, Product Management becomes the ultimate differentiator.

Since anyone can now build an app, the competitive advantage shifts from “can we build it?” to “are we building the right thing for the right user?”

PRDs are Dead; Long Live “Context Fuel”

The traditional Product Requirements Document (PRD) is being replaced by Context Fuel.

Because AI agents require highly structured, intention-rich information to generate accurate code, the primary skill for the 2026 PM is no longer writing specs, but “context engineering”.

Teams are moving away from static mock-ups toward functional seeds.

Tools like Eraser.io are used to define system boundaries and data flows, which are then fed into Cursor to generate working prototypes.

This “Context Fuel” provides the AI agent with a “contextual ground truth”—such as existing component libraries or API documentation—to ensure the output remains consistent with enterprise standards.

The PM is no longer just a writer; they are the implementation lead for the agentic workflow.

Conclusion: The Era of the Agentic Product Operating System

We are entering the era of the “Product OS”, a state where humans and AI work in live, connected repositories. The distance between a business need and a technical solution is approaching zero.

The scale of this shift is already evident.

In mid-2025, large-scale enterprise hackathons proved that vibe coding could transform 30,000 ideas into functional apps in a single week.

In this new reality, performance is measured by “Agentic Reliability”—the ability of AI systems to autonomously interpret and execute complex business intents.

The transition is no longer optional. If every employee in your department could build a fully functional, integrated app in a single afternoon, which of your “impossible” internal bottlenecks would disappear by tomorrow morning?

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The landscape of software development has undergone a fundamental shift from the precision of manual syntax to the clarity of conversational intent.

Popularized as “vibe coding” this transition allows developers and domain experts to use natural language and Large Language Models (LLMs) to generate and iterate on applications.

While this offers massive gains in speed—reducing prototyping time by 60–80%, it introduces significant “trust debt” and structural fragility, including security vulnerabilities and maintenance challenges.

Vibe-Forge-Mature Loop

To transition from “vibes” (dialogue-driven-development) to “value” (durable, production-grade systems), the “Vibe-Forge-Mature” loop helps bridge the gap between rapid experimentation and engineering resilience.

The Vibe-Forge-Mature Loop

This three-stage cycle ensures that creative speed does not result in unmanageable technical debt:

1. Vibe: Exploratory phase using high-level prompts to test boundaries in “greenhouse” environments.

2. Forge: Collapsing ambiguity into specific intent using “Spec Mode” to create structured architectural plans.

3. Mature: Hardening the system for production through infrastructure, security, and observability.

Success in this new era requires organizations to transition from manual builders to orchestrators of autonomous agentic workflows, balancing high-speed innovation with rigorous operational guardrails.

Shift in Developer’s Role

Vibe coding represents the peak of abstraction, where work is designed by those who "live inside it"—domain experts who can express business reality directly into functional software.

The developer's role has shifted from writing code to guiding an AI assistant through a recursive, dialogue-driven feedback loop.

Strategic Value and Enterprise Impact

Vibe coding significantly lowers the cost of experimentation, allowing large organizations to innovate at the speed of small startups.

Accelerated Innovation

• Prototyping: Cycle times are reduced by 60–80%, allowing for rapid validation of risky designs or internal tools.

• Productivity: Research indicates a 56% improvement in developer speed.

• ROI: Organizations coupling vibe coding with application modernization are 3x more likely to achieve significant ROI from AI.

Democratization

Non-technical roles (e.g., product managers, HR professionals, business analysts) can now contribute directly to implementation. This ensures that software output is more closely aligned with the practitioners who understand the business context best.

Risks and Structural Fragility

The “looseness” of vibe coding can lead to systemic fragility if managed improperly.

Security and Data Risks

AI-generated code may contain twice as many security flaws as human-written code. Key vulnerabilities include:

• Naive Implementation: Skipping input sanitization or access controls.

• Hardcoded Credentials: Accidental inclusion of secrets in code repositories.

• Prompt Injection: Malicious documents (like resumes) containing hidden instructions that an agent might execute.

• The “Tea App” Leak: A notable real-world example where AI-generated applications led to sensitive data exposure.

Trust Debt and Maintenance

“Trust debt” occurs when code works initially but becomes impossible to debug or scale because the creator does not understand the underlying structure. Without documented standards, codebases become inconsistent and fragmented.

Conclusion

Vibe coding is a “renaissance” of intent-driven work that realigns software authorship with business needs.

However, the move from “vibe” to “value” is not automatic. It requires disciplined engineering, rigorous governance, and the evolution of the professional role from builder to Orchestrator.

The most successful organizations will be those that treat governance as infrastructure, allowing them to turn the speed of thought into durable organizational value without losing their unique brand identity.

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The state of enterprise software in 2026 is one of extreme dispersion.

The transition to "Agentic-First" architectures (Smart Layering of real-time data with dynamic reasoning using Context Engineering) and outcome-based business models will capture epic returns. This is the promising landscape as we started this year. Only time will tell how this transition plays out.

Here is a brief overview of this comprehensive landscape -

* From "Assistive" to "Autonomous": The transition from “Sidecar AI” (assistive chatbots like Copilots) to “Digital Labor”(autonomous agents) is driving the notion of “No-Click” Enterprise. The traditional “click, search, and browse” paradigm is becoming obsolete. The user interaction model is flipping from “Pull” (humans logging in to find data) to “Push” (agents proactively triggering actions based on events).

The enterprise software giants like (Salesforce, SAP) own the “System of Record” (databases). The threat is that agents will capture the “System of Action” (the workflow), pushing legacy apps down the stack to become invisible “middleware”. If the agent handles the user intent and execution, the underlying application loses its “Front Door” status and pricing power.

* Architectural Re-Platforming towards real-time and dynamic context:

The move to autonomous agents requires a total architectural re-platforming, moving away from outdated “CRUD” (Create, Read, Update, Delete) architectures toward Event-Driven Architecture (EDA).

Traditional Retrieval-Augmented Generation (RAG) and vector databases are failing in operational tasks because they rely on “stale” data; in high-stakes scenarios like fraud detection, a “Data Freshness Gap” of even minutes leads to catastrophic hallucinations.

The new “Central Nervous System” of the enterprise is built on “Context Engineering”. Platforms with real time capabilities are leading this shift, maintaining a real-time “materialized view” of the business to provide agents with millisecond-accurate state.

* The Business Model Pivot from “Per-Seat” pricing to “Outcome-based” pricing: The rationale is - if a single AI agent can replace the output of 700 human service representatives, a vendor charging per-seat faces a revenue collapse. The market must shift toward Outcome-Based pricing, where vendors charge for “work done” (e.g., per ticket resolved, per audit completed) rather than “access granted”.

Conclusion:

Companies that successfully adopt “Agentic-First” architectures and outcome-based models are poised for the epic returns as they become the backbone of the $52 billion agentic economy.

Conversely, “Human-Centric” laggards face a “quiet collapse”, where they are reduced to commoditized databases hidden behind a third-party agent’s interface.

As you evaluate your current strategy, you must ask: Is your organization building a “System of Record” that will be hidden, or a “System of Action” that will lead?

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The enterprise software landscape is undergoing a structural transformation characterized by a shift from human-centric, assistive tools to autonomous, agentic systems. This transition is redefining valuation frameworks, business models, and technical architectures.

As of early 2026, the software market faces a "Value Crisis" where traditional "Systems of Record" are being discounted in favor of "Systems of Action".

The market for agentic AI is projected to exceed $52 billion by the end of 2026, with nearly 40% of enterprise applications expected to embed autonomous agents.

The financial metrics for the software industry indicate significant market uncertainty and a decoupling of traditional valuation norms.

Here are the key takeaways -

* Valuation Metrics and Market Sentiment

Public software companies are currently navigating a period of intense skepticism, often referred to as the “Software is Dead” narrative.

* Revenue Multiples: The median Next Twelve Months (NTM) revenue multiple has fallen to 3.6x, the lowest level in over a decade.

* Growth Buckets:

* High Growth (>22% NTM growth): Median multiple of 9.7x.

* Mid Growth (15%-22% NTM growth): Median multiple of 6.4x.

* Low Growth (<15% NTM growth): Median multiple of 2.6x.

* Index Distribution: Approximately 39% of the software index is trading at less than 3x NTM revenue.

* Free Cash Flow (FCF): The median FCF multiple sits at 16x NTM FCF for a median growth rate of approximately 20%.

* Hyperscaler CapEx Explosion

While software multiples are compressed, infrastructure spending by “Hyperscalers” has reached unprecedented levels, indicating a massive bet on AI infrastructure.

The combine annual CapEx Projection by the likes of Amazon, Google, Meta and Microsoft is staggering $525B.

* The “Legacy” Tag

Software is being classified as “Legacy” based on “Agentic Readiness” rather than age. Characteristics of legacy software include:

* Human-Centric UI Constraints: Requires manual clicking and browsing.

* Batch-Processed Data Lag: Relying on data that is hours or days old.

* Rigid API Infrastructure: Brittle point-to-point integrations.

* “Bolt-On” AI: AI treated as an extra tab rather than a foundational logic layer.

* The Erosion of the Per-Seat Business Model

The traditional “per-user” subscription model is becoming unsustainable as AI agents replace human labor.

* Per-Seat Subscription - The 2026 projection shows “Declining Dominance.

Justification: Decouples value from headcount; revenue erodes for manual tools.

* Usage-Based (UBP) - 59% of providers expect growth in 2026 using this model.

Justification: Shifts volatility to customers; requires token management.

* Outcome-Based: 40% of Enterprise SaaS to adopt this model.

Justification: Aligns incentives with results (e.g., pay per resolved ticket).

Conclusion: Strategic Realignment

The current market dispersion suggests that only a small percentage (estimated at 10%) of legacy SaaS companies will successfully capture the new AI-curve.

The winners will be those who modernize their technical spine with event-driven architectures, adopt outcome-based pricing, and bridge the “semantic gap” using sophisticated knowledge graphs.

Moving forward, the industry’s health will be judged not just by revenue growth, but by “AI leverage ratios”—the ability to create value independent of additional human labor.

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The enterprise software landscape is currently facing its most significant structural transformation since the move to the cloud.

This shift is driven by the emergence of “Vibe-Coding”—the concept that rapidly improving AI models can replace traditional software development—and the transition to “Agentic-First” autonomous systems.

This briefing outlines the threats to incumbent software providers, the pitfalls of this new paradigm, and the economic shifts defining the era of autonomous software.

The Core Disruption: “Vibe-Coding” and Agentic AI

Market sentiment in 2026 suggests a “Software is Dead” narrative, driven by the belief that traditional Software-as-a-Service (SaaS) is being disrupted by AI-native tools.

Threats to Incumbent Software Providers

Established software companies face “front door risk,” where their products are reduced to middleware. In this scenario, AI agents capture the majority of the value and expansion revenue, while legacy systems sit at the bottom of the technical stack as mere repositories.

The Erosion of the Per-Seat Model

The traditional SaaS model of charging per “user login” is becoming unsustainable. As AI agents replace human labor, the value of a “seat” diminishes.

• Outcome-Based Pricing: Providers are shifting toward charging for results (e.g., “pay per ticket resolved”) rather than access.

• Revenue Impact: High-performing AI agents can do the work of hundreds of human employees; if a vendor cannot charge for those human seats, they face a “Value Crisis.”

Conclusion: The Path Forward

The market is currently pricing software with a “shoot now, ask questions later” mentality, resulting in the lowest revenue multiples in over a decade (median 3.6x). Analysts suggest that only a small percentage—perhaps 10%—of legacy SaaS companies will successfully transition to becoming “AI winners.”

To avoid obsolescence, incumbents must:

1. Own the Data Moat: Turn proprietary transaction history and domain-specific data into a defensive advantage.

2. Transition to Outcome Pricing: Align their revenue with the productivity gains provided by AI.

3. Adopt Open Standards: Utilize protocols like the Model Context Protocol (MCP) to allow agents to connect across different platforms seamlessly.

While “vibe-coding” poses a legitimate threat to simple workflow tools, the robust moats of complex enterprise systems of record may provide them more resilience than the current market volatility suggests. However, the window for these incumbents to re-architect for an agentic, autonomous future is closing rapidly.

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In the volatile timeline of artificial intelligence, every year crowns a new viral chapter.

Last year, Deep Seek redefined the landscape with efficient open-source models causing over $500B to NVIDIA’s market cap.

This year, the industry has pivoted from passive chatbots to autonomous agents, and the headline isn’t just about innovation—it’s about ‘infinite liability.’

Clawdbot (recently rebranded as Moltbot): the ‘local-first’ AI agent that surged to viral fame as a “Personal Jarvis,” fueled by the “vibe coding” movement and driving a run on Mac Mini M4 hardware.

However, this ascent collapsed into chaos following a trademark dispute with Anthropic.

A catastrophic “10-second gap” during the resulting rebrand allowed scammers to hijack official handles, launching a fraudulent cryptocurrency that cost investors millions.

Beyond the drama, the software represents a security crisis described by researchers as an “infinite liability surface”.

For these reasons, Clawdbot/Moltbot is considered an exceptionally dangerous tool for the general public, posing risks of total system compromise, runaway financial costs, and significant “Shadow AI” threats within corporate environments.

Listen to this podcast and join me in a detailed discussion for key takeaways on this topic. Click here

Builder, Innovator, or Achiever? Uncover Your True EnterpriseAI Score.

The 5-Stage Roadmap for ScalingAI.

By Suhas Dhekane.

It’s impossible to ignore the constant pressure on businesses to adopt Artificial Intelligence.

Let’s be honest, as your organization moves into 2026, do you notice a shift in strategic investments towards AI?

What about the existing investments

Research suggests that 60% of the companies are still stuck with the execution of Data Strategy and building interoperability for seamless integrations and communications across various teams.

All these initiatives are now being measured based on their effectiveness in the age of AI.

Here are the key strategic questions for investments in 2026 and beyond.

Are existing investments in various transformation projects able to enable AI-Driven Enterprises?

Does your existing investment able to build a competitive AI product? OR does your organization feel that the competitors are way ahead?

Today, I am handing the map that will not just find answers to the above strategic questions but also to make you a leader of the pack. Here is the map to the “Summit”.

So, lets start the climb to the Summit..

Starting at the Base Camp, where you do stand in the AI race? Here is a strategic framework designed to help organizations evaluate and advance their artificial intelligence capabilities.

The 5-Stages - From Base-Camp to Summit

There are various different AI Maturity assessment frameworks that are available like the one from Gartner, Microsoft or MIT. Although, each one defines stages differently, they are all mapping the same objectives.

It does not matter if stage 1 is called Awareness or Ad Hoc, it represents the same “starting point” proving that this is a standardized strategic framework for AI maturity assessment.

Please take a moment to read through the 5-Stages - Awareness and Foundational, Active and Experimental, Operational and Innovation, Systemic and Strategic and lastly Transformation and Pioneering.

The above 5-stages have well-defined diagnostic and operational norms will help you assess your organization maturity to identify technology and skill gaps, cross-team frictions, outdated processes and legacy infrastructure.

Now, you maybe wondering - Why Organizations fail in transformation?

Over years, organizations have partnered with various consulting firms in performing maturity assessments but later on struggled in successfully executing strategic transformation like automating business processes, cloud migrations, building data-driven enterprise.

The main reason being - most of the Maturity Framework guides with the assessment. However, they lack in providing a strategic roadmap in helping organizations mature from one stage to the next stage.

That brings us to the next section - Strategies for a Climb - Advancing from one stage to the next

Each of the Strategies details out a Transition Plan with key metrics like "The Friction" with current stage of maturity, the "end-goal" to advance to the next stage. Each plan not only provide you with actionable steps but also success markers to measure success at each step. Please refer to the transition plans for more details. This section ends with 3 critical enablers across all the stages - Building unified Infrastructure, Talent and Culture Literacy and Strategy and Governance.

As you reach the Summit, I will leave you with the Implementation Cycle that will take you beyond the Summit. As the technology is rapidly evolving, this process becomes a continuum for growth and success.

So, the real takeaway is - you have a map and a strategic plan to advance from base camp to summit - what are you going to do with this map? I leave you with the immediate action plan that will get you started immediately.

For more detailed article please click here- https://bit.ly/4k2TC8K

Builder, Innovator, or Achiever? Uncover Your True EnterpriseAI Score.

The 5-Stage Roadmap for ScalingAI.

By Suhas Dhekane.

It’s impossible to ignore the constant pressure on businesses to adopt Artificial Intelligence.

Let’s be honest, as your organization moves into 2026, do you notice a shift in strategic investments towards AI?

What about the existing investments

Research suggests that 60% of the companies are still stuck with the execution of Data Strategy and building interoperability for seamless integrations and communications across various teams.

All these initiatives are now being measured based on their effectiveness in the age of AI.

Here are the key strategic questions for investments in 2026 and beyond.

Are existing investments in various transformation projects able to enable AI-Driven Enterprises?

Does your existing investment able to build a competitive AI product? OR does your organization feel that the competitors are way ahead?

Today, I am handing the map that will not just find answers to the above strategic questions but also to make you a leader of the pack. Here is the map to the “Summit”.

So, lets start the climb to the Summit..

Starting at the Base Camp, where you do stand in the AI race? Here is a strategic framework designed to help organizations evaluate and advance their artificial intelligence capabilities.

The 5-Stages - From Base-Camp to Summit

There are various different AI Maturity assessment frameworks that are available like the one from Gartner, Microsoft or MIT. Although, each one defines stages differently, they are all mapping the same objectives.

It does not matter if stage 1 is called Awareness or Ad Hoc, it represents the same “starting point” proving that this is a standardized strategic framework for AI maturity assessment.

Please take a moment to read through the 5-Stages - Awareness and Foundational, Active and Experimental, Operational and Innovation, Systemic and Strategic and lastly Transformation and Pioneering.

The above 5-stages have well-defined diagnostic and operational norms will help you assess your organization maturity to identify technology and skill gaps, cross-team frictions, outdated processes and legacy infrastructure.

Now, you maybe wondering - Why Organizations fail in transformation?

Over years, organizations have partnered with various consulting firms in performing maturity assessments but later on struggled in successfully executing strategic transformation like automating business processes, cloud migrations, building data-driven enterprise.

The main reason being - most of the Maturity Framework guides with the assessment. However, they lack in providing a strategic roadmap in helping organizations mature from one stage to the next stage.

That brings us to the next section - Strategies for a Climb - Advancing from one stage to the next

Each of the Strategies details out a Transition Plan with key metrics like "The Friction" with current stage of maturity, the "end-goal" to advance to the next stage. Each plan not only provide you with actionable steps but also success markers to measure success at each step. Please refer to the transition plans for more details. This section ends with 3 critical enablers across all the stages - Building unified Infrastructure, Talent and Culture Literacy and Strategy and Governance.

As you reach the Summit, I will leave you with the Implementation Cycle that will take you beyond the Summit. As the technology is rapidly evolving, this process becomes a continuum for growth and success.

So, the real takeaway is - you have a map and a strategic plan to advance from base camp to summit - what are you going to do with this map? I leave you with the immediate action plan that will get you started immediately.

This article is part of my Viewpoints collection, which focuses on current events in the world of technology, data, and AI. It is a space where I can share my viewpoints on a specific topic of interest.

The Impact of AI on the global workforce is a complex and evolving topic.

Not too long ago, ChatGPT and Generative AI (GenAI) revolutionized the technology space.

Since then, GenAI has evolved from a nice-to-have to a must-have capability. 

GenAI projects are maturing and transforming organizations by turning enterprise data into actionable knowledge and helping employees work more efficiently.

GenAI applications that respond to prompts using natural language can solve problems with minimal human intervention. The interpretation of prompts and responses is built using large language models (LLM).

The pre-trained scaling law for LLM was the original scaling law for building the larger scale of training data.

The post-trained with reinforced learning and human feedback helped with problems like hallucinations in the responses generated by pre-trained LLMs.

Both pre-trained and post-trained models have reached their saturation and diminishing returns.

As the next phase in the evolution of LLMs, with test-time scaling law (a.k.a. reasoning), it is now seen as the next big opportunity. 

Agentic AI and reasoning LLMs utilize the test-time scaling.

It represents an evolution beyond the traditional single, less complex interaction of AI systems like chatbot applications.

Agentic AI is designed to act independently, making decisions and taking actions to achieve specific goals without constant human intervention.

There are definitive advantages of Agentic AI -

* Increase productivity and efficiency.

* Improved planning, reasoning and decision-making.

* New job-creation like AI Engineering and advancements into DSML.

* Improved Safety.

However, it still raises the ethical concerns, in the near future, like-

* Bias and Discrimination.

* Job Displacements.

* Wages Stagnation.

The impact of AI is visible in various streams of the corporate world -

* Customer Services: Chatbots and virtual assistants are used to handle customer service inquiries.

* Manufacturing: Robotic and automation systems are used to perform various manufacturing tasks.

* Transportation: Advancement into Robotic-taxis, self-driving cars, and trucks will impact taxi and truck drivers.

* Finance: AI-powered systems are used to perform advanced anomaly and fraud detection, investment analysis, and low-risk portfolio management with Robotic advisors.

Agentic AI is a broader paradigm focused on creating AI agents.

The AI Agents, built to accomplish predefined multi-step and complex tasks, can analyze their performance, learn from their experiences, and improve their future actions.

In some cases, multiple AI agents can collaborate to achieve complex objectives. AI agents working towards the same goals are orchestrated in the broader paradigm of Agentic AI. 

One such example of a platform for creating AI Agents for various industries is Agentforce.

Salesforce, a leading provider of Cloud-Based Enterprise Software Solutions, heavily emphasizes "Agentforce - A Digital Labor Platform" as a key part of its AI strategy.

Salesforce views Agentforce as representing the "third wave of AI," moving beyond predictive and generative AI to autonomous, action-oriented agents that take advantage of test-time scale and reasoning LLMs.

Agentforce is designed to create AI agents that can take independent actions, provide information or responses to a prompt, and accomplish a larger objective by automating complex and multi-step tasks, making decisions, and adapting to changing conditions.

Salesforce integrates Agentforce deeply within its platform, allowing seamless integration with other products and services.

This allows the AI agents to have access to the full spectrum of enterprise data and actionable knowledge to make autonomous decisions.

Salesforce is prioritizing the development of Agentforce with a strong focus on trust and reliability, addressing concerns about AI accuracy and potential risks.

In January, at CES 2025, Jensen Huang, Nvidia's President, co-founder, and CEO, delivered the keynote announcing the next wave of AI trends.

He stated that Agentic AI would be "A next Trillion Dollar Opportunity."

Here is the link for more details.

The agentic AI market is projected for rapid growth. It is driven by the increasing demand for automation, improved decision-making, and enhanced efficiency across industries.

Viewpoint

So, with the advancement into the third wave of AI and the ability to create AI agents, it is inevitable that AI will impact how the corporate world functions in the near future.

There is no crystal ball to see clearly into the future and know what exactly will happen -

* how the next-gen AI-powered economy will shape up,

* where will the next Trillion Dollar Opportunities emerge,

* what will be the next ChatGPT moment in the technology space,

In the same way, there is no clear foresight on the impact of AI on the job market.

Some experts believe that AI will create more jobs than it destroys, while others believe that AI will lead to widespread job losses.

This is an evolving topic and the most debated one.

One thing is highly predictable: the nature of newly created jobs and skills required to fuel the next-gen AI-powered economy are also evolving. 

Key Takeaways

It is no surprise that the nature of some job functions may look different in the future. 

Some of the skills that will be in continuous demand:

* AI literacy: Understanding how to work alongside and operate Agentic AI technologies will become increasingly essential.

* Technical skills: The ability to design, implement AI-development frameworks, and deploy AI products, as well as the ability to maintain and troubleshoot them.

* Strategic-Thinking: As AI takes over more routine tasks, human workers will need to focus on creativity for higher-level problem-solving and strategic thinking to effectively leverage AI's capabilities. Ethical AI practices will be in high demand.

* Domain expertise: A deep understanding of the specific industry vertical or domain like healthcare, finance, manufacturing, or even cybersecurity and governance-regulation compliance (GRC).

* Communication and collaboration skills: The ability to communicate and collaborate effectively with other team members and AI-powered systems.

* Lifelong learning: To mitigate the potential negative impacts of job displacement, investing in robust re-skilling and upskilling includes learning new skills and technologies as they become available. Since GenAI's evolution, there has been a wave of new technologies to learn. GenAI prompts are a good way to get started on knowing this technology.

By developing these skills, we can ensure that we are prepared for the challenges and opportunities of the AI-powered economy.

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