July 2024 - Ciklum

10 Ways Digital Twin Software Is Transforming Industry 4.0

Posted on July 30, 2024August 14, 2025 by ciklum.manage

Key Takeaways:

Digital twins are an instrumental part of smarter manufacturing
Making manufacturing faster, more cost-effective, safer and more sustainable
The same principle can extend through quality control and the supply chain
Several key technologies need to come together to make it a practical reality

10 Ways Digital Twin Software Is Transforming Industry 4.0

Digitization is helping businesses develop new products and exploit opportunities in their marketplaces in so many different ways. It’s proving especially useful for organizations who want to get their concepts to market as quickly and cost-effectively as possible, without compromising on quality or testing.

Digital twin technology is one of the main ways in which product development can be expedited – and it’s no surprise that its global market size is predicted to rise from under $18 billion in 2024 to as much as $260 billion by 2032. This blog explores how Digital Twin technology works, how it’s influencing Industry 4.0, and how it can make a real practical difference to your development processes.

What is Digital Twin technology?

Digital twin technology refers to replications of physical items or assets in a virtual environment. Its features, functionality and capabilities can be simulated in real time, based on data collected from embedded IoT sensors in the real product. This can help make development, testing and maintenance a faster and more cost-effective exercise than it would be using physical products themselves.

Digital twins have gained real traction in recent years, across a variety of use cases. For example, fashion retailers have created virtual versions of clothing items that they sell, so that online shoppers can accurately assess fit and size on their own bodies before they commit to a purchase. In the energy sector, digital twins of wind farms combined with predictive analytics are enabling proactive maintenance and balancing of energy output. And automotive businesses can simulate and test new engine designs using digital twins, leveraging real-time monitoring to reduce the downtime of maintenance.

Continue reading “10 Ways Digital Twin Software Is Transforming Industry 4.0”

Exploring Data & AI’s Role in Enhancing HR Functions

Posted on July 29, 2024August 14, 2025 by ciklum.manage

Key Takeaways

Data and AI are full of potential for HR, but take-up is low so far
Data and AI can transform HR decision-making, onboarding and employee engagement
Challenges around ethical use of AI, data privacy and workforce buy-in need to be addressed
Now is the time to get on board, with more innovations in the pipeline

The demands on HR teams as part of successful modern businesses are perhaps bigger and more varied than they’ve ever been. A well-run HR function goes beyond employee processes and services, and incorporates training, workplace culture, employee wellbeing, and efforts around diversity and inclusion.

All this means that many HR teams are actively on the lookout for ways to increase efficiency, and artificial intelligence is making a real difference in that area. The ability to use chatbots to automate employee-facing procedures, transform onboarding processes, and develop more engaging and immersive training programmes can all help HR take their operations to the next level.

While many organizations have started using AI for HR-related activities in the last year or so, SHRM has found that take-up is still only around 25%. As this blog will demonstrate, there are plenty of upsides for early adopters who get on board with the role of AI in HR now.

Continue reading “Exploring Data & AI’s Role in Enhancing HR Functions”

Into the Future: How Agentic AI is Changing the Game in 2025

Posted on July 29, 2024August 14, 2025 by ciklum.manage

Key Takeaways

Agentic AI is steadily progressing towards providing more versatile agents with cross-sector and collaborative capabilities.
GenAI will persist in Agentic AI workflows, particularly where creative output is required.
RegTech could highly influence the direction of Agentic AI innovations pertaining to data cloud solutions, reasoning engines and connectors.

The global AI market continues to grow at a rapid pace. According to Hostinger, a compound annual growth rate of 28.46% is expected from now through to the end of the decade. And while generative AI has grabbed most of the headlines, there are plenty of other types of AI that can serve businesses well in the years to come.

One is agentic AI, which takes the principles of generative AI to the next level, expanding the involvement that AI can take in day-to-day work and reducing the amount of human input required to get the most out of the technology. This blog explores agentic AI, what it means for your organization, and how best to apply it now and in the future.

What is Agentic AI?

Good agentic AI solutions can make their own judgment calls, make decisions in context, and adjust objectives and workflows in real time as circumstances and situations evolve. They can move between different tasks and applications in a logical order to maximize the efficiency of processes, and can also understand and execute instructions given by humans in natural language as and when required. They can also autonomously navigate complex decision trees, and self-improve through reinforcement learning and continuous feedback looks.

The ability to take the bigger picture into account is the key area in which agentic AI differs from conventional AI platforms. Being able to leverage huge datasets, major computing power and large language models (LLMs) mean that agentic AI can plan out what it needs to do, then go and do it.

The Evolution of Agentic AI Systems

Agentic AI can be seen as the logical next step from the conversational AI platforms that have become commonplace in recent years. What used to be relatively simple chatbots, gradually expanded in functionality to handle requests and processes based on natural language and autonomous driven decision-making.

Agentic models take this further by moving beyond ‘request and response’ processes and towards more complex systems that are based around actions and workflows, such as Large Action Models (LAMs). These differ from AI copilots in that they have much more autonomy and independence to make their own decisions, using their own understanding to make choices without needing input from human endeavor.

What Are the Building Blocks of Agentic AI?

An agentic AI architecture is typically made up of three core components, each of which makes its own valuable contribution:

Perception: the integration of data from many different sources, in order to gain the most detailed understanding of the situation possible. This can include text, still images, video, audio, and even information from sensors gained from IoT devices.

Cognition: the processing of the information gained at the perception stage, in order to make the right decisions for workflows and processes. This is achieved through the use of deep learning models, and leveraging the results of previous experiences.

Action: using the cognitive decisions made to execute complex tasks and workflows, control algorithms to ensure precision, create feedback loops that can make dynamic adjustments as required, and even robotics where the actions required are physical rather than digital.

One of the reasons that agentic AI is so full of potential is the sheer breadth and diversity of the data stores it can make use of. This can include unstructured data, structured data, embeddings and vector stores, and even knowledge graphs. The fact that agentic AI can operate in both digital and physical contexts also expands the possibilities of real-world use cases for the technology.

Pros and Cons of Agentic AI

If deployed correctly and in the right places, the scale of the benefits that agentic AI can deliver is enormous, including:

Efficiency:

Being able to not only process vast amounts of data, but also make contextual decisions faster than humans, can make everyday workflows much quicker, more efficient and more accurate.

Continue reading “Into the Future: How Agentic AI is Changing the Game in 2025”

How to Improve Quality Assurance In Banking & Financial Applications

Posted on July 24, 2024August 14, 2025 by ciklum.manage

Continue reading “How to Improve Quality Assurance In Banking & Financial Applications”

Conversational AI vs Generative AI: Choosing the Right AI Strategy for Your Business

Posted on July 4, 2024August 14, 2025 by ciklum.manage

The rapid expansion of artificial intelligence in the world of business means it’s now starting to become a mainstream activity. According to IBM, 42% of IT professionals in large organizations report to have deployed AI within their operations, while another 40% are actively exploring their own opportunities to do so.

But amid the gold rush to get on board with AI technology, it’s important to understand the different types of AI tools out there, what they do, and the key differences between them. This blog explores the distinctions between two of the most popular forms around: Conversational AI and Generative AI, and how to work out where you should apply them to your business activities.

What’s the difference between Conversational AI and Generative AI?

Conversational AI refers to technology that can understand, process and reply to human language, in forms that mimic the natural ways in which we all talk, listen, read and write. Generative AI, on the other hand, is the technology that can create content based on user prompts, such as written text, audio, still images and videos.

Aside from the functionality that they offer, there are several key differences between the two. For example, Conversational AI relies on language-based data and user interactions, whereas Generative AI can use these datasets and many others when creating content. However, there is some scope for overlap between the two, such as when text-based Generative AI is used to enhance Conversational AI services.

There’s also plenty of variation between the main suppliers of each technology, and the costs involved. Conversational AI features many of the big tech players through Virtual Assistants: think Google Assistant, Amazon’s Alexa and IBM Watson; however, a number of smaller players like Kore.ai are making waves, too. As for Generative AI, many new businesses have made real headway in gaining market share, such as OpenAI with its Artificial Intelligence application ChatGPT. But even Generative AI is becoming increasingly centred around Big Tech, particularly when it comes to infrastructure models.

Where is Conversational AI best used?

There is a wide range of industries that are already benefiting from Conversational AI implementation, including (but not limited to):

Data collection:

Conversational AI can help gather important data from several sources and collate it for driving meaningful and digestible insights to guide data-driven AI decision-making.

Customer support:

Responses to the most common queries and issues can be automated by chatbots, freeing up service agent time to deal with more complex cases.

E-commerce:

Feeding personalized recommendations to customers to encourage them to purchase, as well as supporting order management when customers look for information.

Healthcare:

Preliminary diagnoses for common ailments can be taken care of by virtual healthcare platforms, which can also support the management of appointment scheduling.

Banking:

The process of conducting financial transactions and dispensing financial advice can be eased through Conversational AI.

Human resources:

Many of the important but relatively straightforward HR functions can be covered by Conversational AI, such as onboarding processes, recruitment procedures and employee support.

Where is Generative AI best used?

The use cases for Generative AI tend to be very different to its conversational counterpart, but they’re no less valuable, such as:

Business process automation:

Repetitive tasks and processes can be intelligently automated, as Generative AI can extract the key data required and complete the process independently.

Content creation:

Every type of organization can benefit from creating marketing copy or writing blog articles with some assistance from Generative AI.

Media:

Similarly, Generative AI can be used to create images, logos, videos and other visual promotional content.

Software development:

Snippets of code can be generated to expedite development processes, while Generative AI can also assist in software debugging.

Education:

Personalized learning experiences can be supported through the generation of educational materials.

Finance:

Generative AI can also understand patterns of human activity, helping finance firms with fraud detection, especially when combining Generative AI with existing Machine Learning classification problems to boost the performance of both technologies.

R&D:

The ability to analyze and process data at scale to create hypotheses can be helpful in assisting scientific research.

In Summary: Choosing the Right AI Strategy

The business AI solutions landscape is complex, and it’s evolving at a rapid rate. Not only that, but the global AI marketplace is saturated, meaning that it can be hard to know how to get started with what is a very important investment for your organization.

The key is to establish a comprehensive, agile strategy for AI, and that begins by understanding where you can apply Conversational AI vs Generative AI. The following five steps are a good place to start:

Align AI decision-making with business goals and objectives to ensure you get the most out of the technology.
Structure AI implementation in a modular way to encompass all the different variants of AI.
Ensure you’re well versed in ethical AI use and create appropriate intellectual property strategies and priorities to avoid getting caught out by existing and emerging regulations.
Invest in upskilling your employees on both the technology and business sides of AI to ensure AI strategy filters through the entire organization.
Monitor emerging trends and industry practices like multi-bot experiences, omni-channel experiences, and voice assistants for Conversational AI, and multi-modal education, Artificial Intelligence applications and services for Generative AI.

Drive forward AI-powered creativity by partnering with pioneers with proven success. Explore Ciklum’s Experience Engineering approach to fast and iterative development, alongside end-to-end strategy and execution, here.

Continue reading “Conversational AI vs Generative AI: Choosing the Right AI Strategy for Your Business”

10 Ways Product Discovery Fuels Tech Innovation

Posted on July 4, 2024August 14, 2025 by ciklum.manage

In a highly competitive and globalized business world, every organization needs full confidence that their new products are going to be successful. A big part of that comes from understanding customer needs and preferences and developing products that solve well-defined consumer problems.

Ideally, no organization should commit valuable time, money and resources to development without having that understanding in place – and the best way to get it is through product discovery. This blog explores product discovery in detail, including how it’s driving tech innovation for businesses like yours.

What is Product Discovery?

Product Discovery is an approach that ensures customer insights are integrated into every stage of the product development process. It’s achieved through collating detailed research, user feedback and market trends, to give product teams a comprehensive understanding of what their target customers need, want and prefer.

Having clear product discovery frameworks is vitally important in product development, as it substantially reduces the risk of mistakes being made – and resources being wasted – by chasing assumptions and ideas that haven’t been validated.

10 Ways Product Discovery Drives Tech Innovation

Identifying market opportunities and user pain points through product discovery techniques sets the stage for successful product development, supporting areas such as:

Identifying Customer Needs

By exploring the unmet needs and pain points of customers, solutions can better address real-world problems, create value and build relationships; this should ideally be done through face-to-face user research with the end customer.

Fostering Creativity

Brainstorming, workshops and an open-ended approach to exploration allow existing assumptions to be challenged, so that product teams think more creatively. This should be as inclusive as possible and include engineers, designers, sales and marketing practitioners, customers and all other relevant stakeholders.

Enabling Real-World Validation

Testing early versions and prototypes of products can help refine ideas based on real user feedback, spot potential issues early on, and gain validation for the concept in a low-cost, low-risk environment.

Reducing Risk

Connected to the previous point, the uncertainty around development can be removed by validating pricing strategies, product positioning and market demand as early as possible; this can also help achieve internal buy-in for the project.

Refining Solutions

Product discovery supports continuous improvement through building, testing and enhancement that’s based on user feedback and insights. This allows products to be refined and optimized towards user needs quickly and accurately.

Supporting Data-Driven Decision-Making

Collecting market research, user feedback and product usage data is vital for gaining the insights that support the right decisions, in turn ensuring that products are built on facts rather than guesswork.

Encouraging Collaboration

The product discovery process naturally brings together diverse perspectives and fosters a culture of knowledge sharing across every team and function. This can be instrumental in driving forward customer needs, technical feasibility, and business viability.

Maximizing Agility

By encouraging high user interaction through continuous product discovery, it becomes easier to adapt to evolving market dynamics and understand when and where to take advantage of improvement opportunities over time. Not only can this support product development, but the insights involved can improve marketing and the wider business strategy, too.

Enhancing User Experiences

By bringing users more directly into the development process and gathering their feedback, the overall user experience of the product can be better refined and give them the outcomes they’re looking for.

Driving Competitive Advantage

Being in a better position to embrace new opportunities and innovative ideas provides a solid platform for delivering new solutions, functions and features before competitors are able to.

Real-World Examples of Tech Innovation through Product Discovery

As demonstrated, effective technological innovation can only stem from a deep understanding of user needs and pain points. At Ciklum, we’ve harnessed the power of product discovery with our unique Experience Engineering approach to drive technological advancements that address real-world challenges across various industries.

The Ciklum team were recently tasked with enhancing a learning management system for a large network of private schools, focusing on improving parents’ experiences. During the product discovery phase, we identified two critical issues. First, parents were managing up to 17 different logins to access various services and information. Second, user testing revealed that a significant number of parents were navigating straight from the homepage to the calendar to find timetable notifications.

To resolve these issues, we implemented two key solutions. We redesigned the product to incorporate the calendar directly into the homepage, streamlining the user experience and allowing parents to access crucial information at first glance. Then, leveraging our advanced artificial intelligence (AI) expertise, we integrated a large language model chatbot. This feature enables parents with varying technical skills to interact with the platform using natural language, significantly enhancing accessibility and user engagement.

In Summary: Best Practices for Effective Product Discovery

Some of the world’s most well-known technology products have been successful thanks to product discovery. For example, Netflix transitioned from a DVD rental service to a streaming platform after recognizing that customers wanted access to a large amount of content through much easier means. In the business world, the team collaboration tool Slack has gained traction by understanding workplace communication challenges and developing a platform specifically to address those challenges.

So it’s clear that product discovery is vital to drive tech innovation and support business success in the long-term. A good product discovery phase involves focusing on problems, and as an experience engineering company, CikIum’s ability to create, build and scale technology products can help solve those problems. In our extensive experience, we believe there are five key product discovery steps to making it as successful as possible:

Involving diverse stakeholders: gathering insights from cross-functional teams, as well as customers and end-users, to develop a comprehensive understanding.

Using multiple research channels: interviews, observations, analytics and surveys can all contribute towards verifying findings and assumptions.

Embracing agile product discovery principles: taking an approach that’s iterative and customer-centric can enable rapid prototyping and testing for continuous validation.

Supporting experimentation: allowing teams to learn from failures and pursue data-driven decision-making can further drive innovative thinking.

Leveraging technology: streamline processes and insight gathering can be eased by specialist product discovery tools, analytics platforms, and expert product discovery partnerships.

Ready to take an expertise and data-driven approach to product discovery? Explore Ciklum’s ability to create, build and scale technology products here.

Continue reading “10 Ways Product Discovery Fuels Tech Innovation”

9 Ways Low Code Applications Are Transforming Businesses

Posted on July 4, 2024August 14, 2025 by ciklum.manage

In a competitive global marketplace, speed has arguably never been more important when it comes to developing new products. This is partly to gain first-mover advantage with new solutions and meet transformation goals, but also to overcome the slow and expensive nature of software development that can hold agility and reactive evolution back.

Continue reading “9 Ways Low Code Applications Are Transforming Businesses”

What is Custom Software Development?

Posted on July 2, 2024August 14, 2025 by ciklum.manage

Have you ever used a piece of software within your business and felt frustrated that it couldn’t do everything you wanted it to do? Or felt that it was a good solution generally but not very well suited to the specifics of your organization? If so, you aren’t alone – and the good news is that there’s a solution at hand.

Continue reading “What is Custom Software Development?”