Innovation and Technology
What Sets Agentic AI Apart From Today’s AI Tool

The key distinction between generative and agentic AI lies in their approach to tasks and decision-making. Generative AI, which powers popular tools like ChatGPT, Google Gemini and Claude, works like an incredibly sophisticated pattern-matching and completion system. When you prompt it, it analyzes vast amounts of training data to generate appropriate responses, whether that’s writing a poem, creating an image, or helping debug code. While this is hugely impressive, these systems are essentially reactive; they respond to specific prompts without any real understanding of context or long-term objectives.
Agentic AI operates with a degree of autonomy. These systems can set their own goals, develop strategies to achieve them and adapt their approach based on changing circumstances. Think of generative AI as a highly skilled assistant waiting for instructions, while agentic AI is more like a colleague who can take the initiative and work independently toward broader objectives.
The Building Blocks Of Intelligence And Purpose
What makes agentic AI truly revolutionary is its architecture. While generative AI excels at processing and producing content based on patterns in its training data, agentic systems incorporate sophisticated planning modules, memory systems, and decision-making frameworks that allow them to maintain context and pursue objectives over time. They can break down complex tasks into manageable steps, prioritize actions, and even recognize when their current approach isn’t working and needs adjustment.
The Convergence of Generative and Agentic AI
We’re beginning to see the first signs of convergence between generative and agentic capabilities in mainstream AI tools. OpenAI’s recent introduction of scheduled tasks in ChatGPT represents an early step in this direction. This feature allows the AI to operate semi-autonomously, performing scheduled actions and maintaining ongoing responsibilities without constant user prompting. While still in its early stages, it points to a future where AI systems combine the creative and analytical capabilities of generative AI with the autonomous decision-making of agentic AI.
Real-World Applications And Implications
The practical applications of agentic AI are potentially far-reaching and transformative. Imagine an AI system that doesn’t just help schedule your meetings but actively manages your entire workflow, anticipating bottlenecks, suggesting process improvements, and autonomously handling routine tasks without constant supervision. In manufacturing, agentic AI could manage entire production lines, not just by following pre-programmed routines but by actively optimizing processes and responding to unexpected challenges in real time.
The Future Of Human-Machine Collaboration
As agentic AI systems become more sophisticated, we’re likely to see a fundamental shift in how we interact with artificial intelligence. Rather than simply issuing commands and receiving outputs, we’ll develop more collaborative relationships with AI systems that can engage in genuine back-and-forth dialogue, propose alternative solutions, and even challenge our assumptions when appropriate. This evolution could lead to unprecedented levels of human-machine synergy, where AI becomes less of a tool and more of a partner in problem-solving and innovation.
Looking Ahead: Challenges And Opportunities
The development of agentic AI isn’t without its challenges. Questions about decision-making transparency, ethical boundaries, and appropriate levels of autonomy need careful consideration. How do we ensure these systems remain aligned with human values and interests while maintaining their ability to operate independently? How do we balance the benefits of increased automation with the need for human oversight and control? These are critical questions that will shape the future development of agentic AI systems.
Shaping Tomorrow’s Intelligence Today
The shift from purely generative to more agentic AI represents a fundamental reimagining of what artificial intelligence can be. As these systems become more sophisticated and widespread, they have the potential to transform industries, enhance human capabilities, and open new frontiers in human-machine collaboration. The key will be ensuring that we develop and deploy these technologies thoughtfully, with clear frameworks for accountability and control.
Conclusion
Agentic AI is poised to revolutionize the way machines think and act independently, offering a new era of human-machine collaboration and problem-solving. As we move forward, it’s crucial to address the challenges and opportunities presented by this emerging technology, ensuring that we harness its potential to benefit society as a whole.
FAQs
- What is agentic AI?
Agentic AI is a type of artificial intelligence that operates with a degree of autonomy, setting its own goals, developing strategies to achieve them, and adapting its approach based on changing circumstances. - How does agentic AI differ from generative AI?
Agentic AI incorporates sophisticated planning modules, memory systems, and decision-making frameworks, allowing it to maintain context and pursue objectives over time, whereas generative AI excels at processing and producing content based on patterns in its training data. - What are the potential applications of agentic AI?
Agentic AI has the potential to transform industries, enhance human capabilities, and open new frontiers in human-machine collaboration, with applications in areas such as workflow management, manufacturing, and problem-solving.
Innovation and Technology
Are AI Product Managers The Role Of The Future?

As artificial intelligence continues to reshape industries, a new role is emerging at the intersection of technology, strategy, and innovation: the AI Product Manager. This isn’t just a passing trend—it’s a reflection of how integral AI is becoming in the development and optimization of modern products.
To succeed in this evolving role, AI product managers must do more than understand traditional product lifecycles. They’ll need to navigate complex AI and machine learning (ML) systems, evaluate performance metrics, and ensure responsible, ethical deployment of technology. That requires a unique blend of technical acumen, data fluency, and cross-functional leadership.
Core Competencies of Future-Ready AI Product Managers
To lead in this space, product managers should develop proficiency in the following key areas:
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AI-Specific Technical Competence – Understanding how models are built, trained, tested, and deployed.
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Data Science Knowledge – Ability to interpret data, partner with data teams, and drive data-informed decisions.
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Model Performance Evaluation – Knowing how to measure, optimize, and communicate model performance.
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Ethics, Bias, and Regulation – Staying informed about legal and societal implications of AI systems.
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Education and Influence Management – Evangelizing AI within the organization and aligning diverse stakeholders around AI initiatives.
Why Every Product Manager Needs AI Skills
Just as “internet product managers” were once a niche, only to evolve into the standard model of digital product management, AI is on track to become a core element of every product manager’s toolkit.
According to Forrester, AI will become so embedded in product development that PMs who lack foundational AI knowledge may find themselves at a disadvantage. Generalist product managers won’t need to be AI engineers, but they will need to understand how to integrate AI into product features, make informed trade-offs, and iterate based on user feedback and AI performance.
How Product Leaders Can Prepare Their Teams
Leadership plays a crucial role in preparing product teams for the AI-powered future. That means more than just encouraging learning—it means building a culture that values experimentation, continuous education, and hands-on practice.
Here’s how leaders can start:
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Offer AI literacy programs tailored for non-technical professionals.
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Create hands-on experiences through internal projects, hackathons, or partnerships with AI teams.
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Provide access to online, interactive courses and workshops that blend theory with application.
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Recognize and reward team members who take the initiative to upskill.
Conclusion
AI isn’t just a buzzword—it’s rapidly becoming a foundational element of modern product strategy. As such, the AI product manager role is not only growing but evolving into a key pillar of the future workforce.
Product leaders who invest in upskilling today will set their teams up for long-term success, ensuring they’re not only keeping up with the market but helping to define it.
FAQs
Q: What skills do AI product managers need?
A: They should develop AI-specific technical knowledge, data science fluency, the ability to evaluate AI performance, a strong understanding of ethics and regulation, and the ability to educate and influence across teams.
Q: Why is AI knowledge becoming essential for all product managers?
A: AI is becoming a standard part of digital products. PMs will need to understand how to apply AI responsibly and effectively to remain competitive and meet evolving customer expectations.
Q: How can leaders support their teams’ AI/ML development?
A: Provide access to literacy courses, create hands-on learning opportunities, encourage cross-functional collaboration, and foster a culture of curiosity and continuous learning.
Innovation and Technology
The Importance of Data and Analytics in Digital Transformation

Data and analytics are no longer just about crunching numbers and generating reports. They are now a key driver of business success, helping organizations to optimize operations, improve decision-making, and stay competitive in a rapidly changing world.
Why Data and Analytics Matter
Data and analytics help organizations to:
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- Gain insights into customer behavior and preferences
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- Identify areas for improvement and optimize operations
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- Make data-driven decisions, rather than relying on intuition or anecdotal evidence
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- Stay ahead of the competition by being more agile and responsive to changing market conditions
The Challenges of Data and Analytics
While the benefits of data and analytics are clear, many organizations struggle to implement effective solutions. This can be due to a range of factors, including:
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- Limited resources, including budget and personnel
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- Complexity and technical difficulties in implementing and maintaining data analytics solutions
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- Lack of expertise and knowledge in data analysis and interpretation
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- Resistance to change and cultural barriers to adopting new technologies and processes
Overcoming the Challenges of Data and Analytics
While the challenges of data and analytics are real, there are many ways to overcome them. This can include:
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- Seeking expert guidance and support to help implement and maintain data analytics solutions
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- Investing in employee training and development to build in-house expertise
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- Starting small and gradually building up capabilities and expertise
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- Building a strong business case and demonstrating the value of data and analytics to stakeholders
Conclusion
Data and analytics are no longer optional, but a crucial part of any digital transformation strategy. By gaining insights into customer behavior, identifying areas for improvement, and making data-driven decisions, organizations can stay ahead of the competition and achieve their goals.
Innovation and Technology
5 Employee Experience Mistakes Companies Will Make This Year

Lagging In HR AI And Automation
There are lots of great ways companies can use AI within HR to drive improvements in EX. Did you know, for example, that 54% of respondents to one survey said they had given up on applying for a job they wanted due to poor communication from the employer?
Other opportunities include providing personalized onboarding, reducing administrative work by automating repetitive tasks, engagement tracking and improving many aspects of performance management.
Over-Automating Employee Experience
On the other hand, AI still presents a huge number of challenges, particularly when it’s mixed with humans! And while many companies will make the error of under-investing, just as many will, unfortunately, end up using it in ways that are potentially damaging.
Failing To Offer Personal Development Opportunities
This is critical for both retaining existing employees and attracting new talent. Technology is quickly reshaping industries, but workforces need trained and skilled employees to take advantage of this. Offering career progression planning, upskilling and retraining aimed at empowering them to use technology helps people feel they are investing in their own futures by sticking with a business.
Failing To Measure EX ROI
Investing in EX initiatives without a clear plan or milestones in place for measuring success risks wasting money without delivering tangible benefits.
Neglecting Employee Mental Health And Wellness
Workplace stress and burnout are at an all-time high. In fact, the World Health Organization reports that the US economy loses $1 trillion every year thanks to lost productivity caused by depression and anxiety.
Final Thoughts
Employees are a company’s most important resource, and neglecting EX in 2025 means they will quickly start looking elsewhere. This can be a disaster when business success is more dependent than ever on attracting and retaining the right people!
Conclusion
The message I want to get across is that every business should take a strategic approach to EX, taking care to understand how success or failure will impact goals and overall performance. Invest in staff through training, professional development and wellbeing initiatives, and they will pay you back with loyalty, growth and business success!
FAQs
- What is employee experience (EX)?
- EX is the sum of all experiences an employee has in a company, including their interactions with colleagues, supervisors, and the organization itself.
- Why is EX important?
- EX is important because it can directly impact employee productivity, retention, and overall job satisfaction.
- What are some common pitfalls companies make when it comes to EX?
- Some common pitfalls include lagging in HR AI and automation, over-automating employee experience, failing to offer personal development opportunities, failing to measure EX ROI, and neglecting employee mental health and wellness.
- How can companies improve EX?
- Companies can improve EX by providing personalized onboarding, reducing administrative work, offering career progression planning, and prioritizing employee mental health and wellness.
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