The AI FACTOR Framework: Driving Strategic Impact
Artificial Intelligence (AI) is now embedded in nearly every enterprise platform, product, and process. From predictive analytics in finance to generative assistants in daily operations, AI is often marketed as a game-changer. But in the rush to adopt these tools, one important question is often overlooked: Is the AI actually delivering business value? This is a concern we hear regularly from investors analysing portfolios, customers evaluating vendors, and technology leaders reviewing internal performance. Too often, AI is added because it’s trendy, not because it solves a real problem. To evaluate whether an AI solution is actually working not just technically functional but strategically impactful, we recommend using the A.I. F.A.C.T.O.R. Framework. It is a practical way for business leaders to assess Enterprise AI solutions for business impact across key areas that matter for enterprise success. The A.I. F.A.C.T.O.R. Framework This framework is built on eight core evaluation criteria. Together, they help determine whether Enterprise AI solutions are improving your business or simply adding noise. A – Alignment Is the AI clearly tied to a business goal or customer need? The best AI initiatives are the ones that serve a measurable purpose. Whether it’s streamlining a process, boosting productivity, or improving the customer journey, the technology should be connected directly to outcomes that matter. If the AI isn’t solving a clearly defined problem, it is just another line item in your tech stack. I – Intelligence Is the solution truly intelligent or just automated? There’s a big difference between rule-based automation and machine learning. Real AI should demonstrate pattern recognition, adaptive learning, and contextual awareness. If it is just mimicking scripts or following predefined workflows, it is not AI in the sense that drives competitive advantage. F – Fit How well does the AI fit into existing systems and workflows? Enterprise adoption doesn’t work if it disrupts core operations. Good AI integrates cleanly with your CRM, ERP, cloud environment, or custom apps. It should enhance how people already work, not force teams to learn something completely new. A – Accuracy Are the AI outputs consistently reliable and actionable? From forecasts to recommendations, results must be precise enough to support decision-making. Inaccurate outputs drain time, damage trust, and can create downstream risks, especially in sectors like finance, healthcare, or logistics. Every AI rollout should include performance benchmarks and regular validation. C – Cost-Benefit Does the business value exceed the total cost of ownership? This includes more than licensing and implementation. Think about training, cloud compute, model monitoring, data governance, and opportunity cost. If the AI doesn’t create time savings, increase revenue, or improve margins, it is not worth the investment, especially at enterprise scale. T – Transparency Can users and stakeholders understand how the AI works? With growing regulatory expectations across the U.S. and globally, black-box systems are a red flag. Teams should be able to explain the logic behind recommendations, understand risks, and audit outcomes. This matters for compliance and for user trust. O – Optimization Is the AI improving over time through learning and feedback? Static models are a missed opportunity. An effective AI application should adjust to new data, user inputs, and business trends. Optimization is the foundation of long-term value, especially in fast-changing environments. R – Responsiveness Can the AI adjust to new situations, edge cases, or changing conditions? The business world doesn’t sit still. Whether it’s a change in customer behavior, a supply chain issue, or a new compliance rule, your AI tools need to keep up. Responsiveness helps teams avoid service delays, inaccurate insights, or manual firefighting. Why This Matters for Business Leaders Enterprises are investing heavily in AI, often without a clear structure to measure ROI. That is risky. You need to know not just whether your AI is functioning, but whether it is moving your business forward. The A.I. F.A.C.T.O.R. Framework helps: Audit internal projects or pilot programs Vet AI vendors during RFP processes Align product and IT teams with leadership goals Improve how you report AI progress to the board It is a tool for strategic alignment not just technical review and is especially useful when evaluating Enterprise AI solutions for business impact. How to Apply This in Your Organization If you’re currently using AI, use this framework to assess performance. If you’re planning to roll out a new solution, use it as a pre-check before committing. Ask your team: Where is our AI aligned with business objectives? Are we tracking model accuracy and financial impact? How well does it integrate with our current tools? What is the plan for model optimization and oversight? Even if you’re only in the planning phase, having this framework in place will make future deployments smoother, faster, and more effective. Final Thought: AI That Works Means AI That Delivers AI doesn’t need to be perfect, but it must be productive. Business leaders need more than impressive demos. They need measurable outcomes. The A.I. F.A.C.T.O.R. Framework keeps teams focused on what matters real intelligence, real value, and real results. This is especially true when choosing Enterprise AI solutions for business impact that Ignitho delivers with a focus on outcomes, not just algorithms.
Transformative Role of AI in Custom Software Development
Welcome to the world of AI in custom software development. In this blog post, we will get into the impact of AI on custom software development in the enterprise. The emergence of artificial intelligence promises to revolutionize how we create applications and the larger business technology ecosystems. While AI brings the benefits of automated code generation and improved code quality, it is important to understand that there is still a critical place for human expertise in defining the application structure and overall enterprise tech architecture. Streamlining the Development Workflow First, let’s explore how AI can enhance the development process. This will: Create significant savings in mundane software development tasks. Empower developers to be more productive. It is common in every application development scenario where developers spend a significant amount of their time writing repetitive lines of code that perform similar tasks. We often call this software code as boilerplate code. These tasks could involve tasks like authentication, data validation, input sanitization, or even generating code for common functionalities such as calling APIs and so on. These tasks, although necessary, can be time-consuming and monotonous, preventing developers from dedicating their efforts to more critical aspects of the development process. Even today, accelerators like Intelligent Quality Accelerator (IQA), Intelligent Data Accelerator (IDA) and also shortcuts exist to generate all this automatically for developers. However, with the advent of AI-driven tools and frameworks, this scenario can be enhanced much further. The code generation is now context aware instead of just being code that needs to be customized. This will provide developers with a significant productivity boost. For example, let’s consider a developer who needs to implement a form validation feature in their application. Traditionally, they would have to write multiple lines of code to validate each input field, check for data types, and ensure data integrity. With AI-powered code generation, developers can specify their requirements, and the AI tool can automatically generate the necessary code snippets, tailored to their specific needs. This automation not only saves time and effort but also minimizes the chances of introducing errors. Thus, by leveraging AI algorithms, developers can streamline their workflow, increase efficiency, and devote more time to higher-level design and problem-solving. Instead of being bogged down by repetitive coding tasks, they can focus on crafting innovative solutions, creating seamless user experiences, and tackling complex challenges. The Importance of Human Expertise While AI excels at code generation, it is important to acknowledge that the structure of an application goes beyond the lines of code. Human expertise plays a key role in defining the overall structure, ensuring that it aligns with the intended functionality, architecture, and user experience. Consider a scenario where an organization wants to develop an application that processes customer returns. The application needs to have modules for managing customer information, tracking interactions, looking up merchandise and vendor specific rules, and generating reports. AI can assist in generating the code for these individual smaller modules based on predefined patterns and best practices. However, it is the human experts who possess the domain knowledge and understanding of the business requirements to determine how these modules should be structured and interact with each other to deliver the desired functionality seamlessly. Software architects or senior developers collaborate with stakeholders to analyze the business processes and define the architectural blueprint of the application. They consider factors like scalability, performance, security, and integration with existing systems. By leveraging their expertise, they ensure that the application is robust, extensible, and aligned with the organization’s long-term objectives. Since developing a software application often involves integrating it within an existing tech ecosystem and aligning it with the organization’s overall technology architecture, human input plays a critical role. Let’s consider another scenario where an organization plans to build a new e-commerce platform. The enterprise tech architecture needs to consider aspects such as the selection of the platform software, desired plugins, external database systems, deployment strategies, and security measures. While AI can help implement detailed software functionality, it is still the human architects who possess the expertise to evaluate and select the most suitable architecture that aligns with the organization’s specific requirements and constraints. Better Talent Management With AI assisting with custom software development, the management of skills and talent within an enterprise can be significantly improved. As developers are relieved from the burden of mundane coding tasks, they can focus on working at a higher level. That enables them to better leverage their expertise to drive innovation and solve complex problems. Let’s consider an example of an enterprise team tasked with integrating a new e-commerce platform into an existing system. Traditionally, integrating a new e-commerce platform would involve writing custom code to handle various aspects such as product listing, shopping cart functionality, payment processing, and order management. This process would require developers to invest considerable time and effort in understanding the intricacies of the platform. They would have to learn specific APIs and would have to implement much of the necessary functionality from scratch. However, with the aid of AI in code generation, developers can automate a significant portion of this process. They can leverage AI-powered tools that provide pre-built code snippets tailored to the selected e-commerce platform. This allows developers to integrate the platform into the existing system much faster. Thus, the integration of AI in custom software development not only improves productivity and efficiency but also alleviates the pressure of talent management and hiring within enterprises. As AI automates the base-level coding tasks, the demand for volume diminishes. AI helps make skills more transferable across different projects and reduces the need for hiring a large number of developers solely focused on low-level coding tasks. With AI handling the foundational coding work, this shift allows organizations to prioritize hiring developers with expertise in areas like software architecture, system integration, data analysis, and user experience design. Additionally, the adoption of AI-powered tools and frameworks enables developers to explore new technologies more easily. They can adapt their existing skill sets to different projects and platforms, reducing
Rethinking Software Application Architecture with AI: Unlocking New Possibilities – Part 1
In today’s rapidly evolving technological landscape, the integration of artificial intelligence (AI) is reshaping the way we develop and design software applications. Traditional approaches to software architecture and design are no longer sufficient to meet the growing demands of users and businesses. To harness the true potential of AI, we need to reimagine the very foundations of software application development. With our AI led digital engineering approach, that’s exactly how we are approaching software application development and engineering. In this blog post, we will explore how AI-enabled software application development opens up new horizons and necessitates a fresh perspective on architecture and design. We will delve into key considerations and highlight the transformative power of incorporating AI into software applications. Note: In this blog we are not talking about using AI to develop applications. That will be the topic of a separate blog post. This blog has 4 parts. Harnessing the Power of AI Models Transforming Data into Predictive Power Creating a Feedback Loop Evolution of Enterprise Architecture Guidelines and Governance We’ll cover parts 1 and 2 in this blog. Parts 3 and 4 will be covered next week. 1. Harnessing the Power of AI Models with APIs In the era of AI, software applications can tap into a vast array of pre-existing AI models to retrieve valuable insights and provide enhanced user experiences. This is made possible through APIs that allow seamless communication with AI models. Thus, a key tenet of software engineering going forward is the inclusion of this new approach of leveraging AI to enhance user experience. By embracing this, we can revolutionize how our software interacts with users and leverages AI capabilities. Whether it’s natural language processing, computer vision, recommendation systems, or predictive analytics, APIs provide a gateway to a multitude of AI capabilities. This integration allows applications to tap into the collective intelligence amassed by AI models, enhancing their ability to understand and engage with users. The benefits of API-enabled applications that can leverage AI are manifold. By integrating AI capabilities, applications can personalize user experiences, delivering tailored insights and recommendations. Consider an e-commerce application that leverages AI to understand customer preferences. By calling an API that analyzes historical data and user behavior patterns, the application can offer personalized product recommendations, thereby increasing customer satisfaction and driving sales. Applications also have the potential to dynamically adapt their behavior based on real-time AI insights. For example, a customer support application can utilize sentiment analysis APIs to gauge customer satisfaction levels and adjust its responses accordingly. By understanding the user’s sentiment, the application can respond with empathy, providing a more personalized and satisfactory customer experience. It follows that the data and AI strategy of the enterprise must evolve in tandem to enable this upgrade in how we define and deliver on the scope for software applications. In the next section, we will delve deeper into the concept of AI-driven insights and how they can transform the way we present data to users. 2. AI-Driven Insights: Transforming Data into Predictive Power With enterprises investing significantly in AI, it is no longer enough to present users with raw data. The true power of AI lies in its ability to derive valuable insights from data and provide predictive capabilities that go beyond basic numbers. By incorporating AI-driven insights into software applications, we can empower users with predictive power and enable them to make informed decisions. Traditionally, software applications have displayed historical data or real-time information to users. For instance, an analytics dashboard might show the number of defects in the past 7 days. However, with AI-driven insights, we can take it a step further. Instead of merely presenting past data, we can leverage AI models to provide predictions and forecasts based on historical patterns. This predictive capability allows users to anticipate potential issues, plan ahead, and take proactive measures to mitigate risks. AI-driven insights also enable software applications to provide context and actionable recommendations based on the data presented. For example, an inventory management application can utilize AI models to analyze current stock levels, market trends, and customer demand. By incorporating this analysis into the application, users can receive intelligent suggestions on optimal stock replenishment, pricing strategies, or product recommendations to maximize profitability. Furthermore, AI-driven insights can be instrumental in optimizing resource allocation and operational efficiency. For instance, in a logistics application, AI algorithms can analyze traffic patterns, weather conditions, and historical data to provide accurate delivery time estimations. By equipping users with this information, they can plan their operations more effectively, minimize delays, and enhance overall customer satisfaction. Next steps In this blog, we introduced the concept of AI-enabled software application development and emphasized the need to rethink traditional architecture and design. It is important to leverage AI models to modify behavior and engage users effectively. Additionally, applications must go beyond raw data to provide predictive capabilities. These insights empower users and enable informed decision-making. Moving forward, in the next blog post, we will delve into parts 3 and 4, which will focus on the feedback loop between applications and AI models for enhancing user experience and enriching the data store, as well as the evolution of enterprise architecture guidelines and governance in the context of AI-enabled software application development. Stay tuned for the next blog post to learn more about these crucial topics.
How to choose the right Agile methodology for project development in a frugal way
Earlier, the role of technology was limited to be a mere business enabler. But the evolving business scenario now visions technology as the major business transformation element. This puts enterprises under tremendous pressure to build disruptive products with capabilities to transform the entire business world. The role of an efficient project management model for software development is crucial here to bring the right technology solutions at the right time. Traditional project development models such as Waterfall are too rigid and depend explicitly on documentation. In such models, the customers get a glimpse of their long-wished product/application only towards the end of the delivery cycle. Through continuous communication and improvements in project development cycle irrespective of the diverse working conditions, enterprises need to ensure that they get things done with the application development within the stipulated time without compromising on quality. Without an efficient delivery method, the software development team often comes under pressure to release the product on time. According to the survey done by Tech Beacon, almost 51% of the companies worldwide are leaning towards agile while a 16% has already adopted pure agile practices. While this statistic is in favour of agile for application development, there are some major challenges faced by both the service providers and customers while practising agile. Communication gap When the team is geographically distributed, communication gap is a common problem. In such a distributed agile development model, the transition of information from one team to another can create confusion and lose the essence of the context. An outcome-based solutions model with team members present at client locations can enable direct client interactions and prompt response between both the sides. Time zone challenges Another challenge that the client faces in a distributed agile development environment is the diverse time zones. With teams working in different time zones, it is often difficult to find out common work hours where every team is present at the same time. Through an outcome-based solutions model, the customers can stay relaxed and get prompt assistance during emergencies. Moreover, in such cases, the client stays updated about the progress of projects and iterations become easy. Cultural differences In a distributed agile team, the difference in work ethics, commitment, holidays and culture creates a gap between the development team and the customer. In situations like these, a panel of experts including ex-CIO’s and industry experts can be the helping aid to provide customers with valuable insights on current market trends and solutions to close any cultural gaps. Scope Creep Scope Creep is another issue faced by countless customers associated with agile teams working on software development projects out of multiple locations. Here, the missing pillar is a scrum master at offshore defining and estimating the tasks along with onsite representatives communicating every requirement from clients. A closer look at these challenges suggests the scope of a properly architected and innovative agile model to resolve these issues. Through a carefully devised agile strategy, it becomes quite easy for both the client and development sides to interact on a frequent basis and overcome the obstacles.
Customer Data Platform (CDP) for Media: Three AI Use Cases
As media and entertainment becomes ubiquitous in our lives, there are multiple priorities unfolding for media companies and brands. They need to be innovating constantly to understand our preferences and behavior to make sure that their content is geared to reach the right people on the right channels and media. AI and big data are important drivers of this capability. In this blog we’ll outline three AI use cases in CDP that media companies should be implementing to stay ahead in digital customer engagement. To deliver the AI and analytics charter, a Customer Data Platform (CDP) becomes extremely important. So, in this blog we will explore the interlinkages between the two. What is a Customer Data Platform (CDP)? A Customer Data Platform (CDP) allows you to unify and manage customer data from multiple sources in a central location. Media companies can benefit greatly from the use of a CDP due to the large amount of first, second-, and third-party data that they receive. For example, clickstream data coming in from their web properties is an important source, and so is the advertising data that is sourced from the networks. Although it is a common deployment scenario, a CDP does not have to integrate directly with a lot of data sources. Instead, mature enterprises are consolidating their enterprise data into a data lake repository such as Snowflake, and then pulling data from there to do AI modeling, create dashboards, and perform what-if analytics in a platform such as Domo. CDPs can be implemented in different ways because depending on the data maturity of the enterprise, not all data is always in pristine condition in a globally centralized data lake. Why is AI important in a CDP Platform? One of the many reasons a CDP is implemented is because it focuses business and technology efforts on a narrow set of AI use cases. This ensures faster time to market. Today, traditional dashboards that provide a view into the past are not enough to understand the cause-and-effect analysis of the many variables in play. Decision making can be made better. The ability to implement AI has the potential to dramatically improve the overall effectiveness of your business intelligence initiatives because it can predict and prescribe. The ability to deploy a CDP quickly and deliver results is much enhanced if the CDP platform is capable of data ingestion, AI modeling, dashboarding, what-If analysis, and API integration of insights. Ignitho’s CDP accelerator has these components with the added advantage that it lets you maintain your investments in various technologies such as Snowflake, Domo, Microsoft, GCP, and AWS. Key Customer Data Platform (CDP) Use Cases for Media Personalization & Promotion Uplift In a crowded digital marketplace, it is important to personalize content recommendations and advertising for each user. By unifying data from different sources, such as website interactions, social media, multiple channels, and email statistics, a CDP helps you gain a holistic view of the audience segments and also at an individual level. You can now provide real time and personalized recommendations based on interests, preferences, and behavior. The insights from AI models in the CDP can be used directly in the customer experience systems to improve performance. Or they can be used for advanced what-if scenario analysis such as gauging the effect of an upcoming promotion being planned. Subscriber Retention & Content Affinity This is an important content monetization use case as publishers begin to experiment with introducing new content formats and packages with different prices. This can be done, for example, by analyzing changes in engagement levels or patterns. By identifying these customers early, we can take targeted actions such as offering personalized discounts or promotions. Using the API layer of the CDP, the insights from the AI models can be integrated right into the content management and promotion systems, thus improving overall responsiveness. Price Sensitivity If we understand customer behaviors and preferences, then we can identify potential churn risks early on. We can take targeted actions proactively and increase engagement. In addition to the ongoing A/B tests in engagement and adoption, a CDP can help analyze who is likely to churn given a price increase or change. These insights can be valuable, as we can then take proactive action to prevent this scenario from occurring and improve the retention considerably. It is often more difficult to reengage customers than to maintain or enhance their engagement. Which Supporting Capabilities to Examine? Implementing a robust Customer Data Platform (CDP) requires a combination of technological, operational, and strategic capabilities. Capturing Zero Party Data Zero party data is information that is intentionally shared by customers of their own accord. It is valuable because it can be used to deliver highly personalized experiences and campaigns. So digital capabilities that prompt customers for their input, feedback, and wish-lists are important to implement. Enterprise Data Fabric An enterprise data fabric aims to create and provide a unified view of data across an organization’s various applications and data sources. It helps break down data silos. As you look to implement a CDP and/or a data lake, creating a well-designed data fabric is important to maximize the ROI from an enterprise insights program. Identity Resolution CDPs rely on resolving and unifying customer identities across different channels and devices. This requires the ability to match and merge customer records based on common identifiers and data attributes. Data quality issues and inadequate analytics can often result from not being able to resolve identities efficiently. Privacy and Security A CDP should have robust privacy and security capabilities to protect customer data and ensure compliance with relevant regulations, such as GDPR and CCPA. This includes capabilities such as data encryption, access controls, and audit logging. Integration and interoperability A CDP should be able to integrate with other marketing and technology platforms, such as marketing automation tools, CRM systems, and data management platforms, to enable seamless data exchange and campaign orchestration. Summary An AI-powered CDP (Customer Data Platform) can help media companies monetize the vast amounts of data
Unlock the Power of AI for Business
I recently wrote a Forbes article on leveraging the power of AI for your business. In this article, I highlighted two specific items from a broader framework to accelerate the generation of value. The first is the application of design thinking to analytics and AI initiatives. I explained using a realistic use case why it is vital to step back and understand not only the problem we are trying to solve, but also consider whether the solution truly meets the needs of users. In addition, it’s also important to think about the future business strategy and how our technological solution will support that desired direction. The second principle was that of creating closed loop analytics to amplify the ROI of your investments. I mentioned constant improvements of the data sources used for insights generation, and also the actual incorporation of insights into operations. These problems are not trivial so a robust data architecture and data pipeline model is important to set up. To provide a fuller perspective, in this blog I wanted to briefly outline the overall framework for data science that we follow at Ignitho. This framework truly allows us to bring alive our mission of using human-centric engineering and AI to our clients. The framework has 2 parts: The data science loop The underlying principles Together these 2 parts help us unlock the potential of AI for our clients. The article further adds to our journey of igniting thought through Ignitho’s strong focus on AI driven human centric digital engineering and thought leadership in digital innovation and transformation. This comes on the back of Garner’s study on Business Composability and CIO’s increasing dependence on AI for an accelerated business growth – Ignitho’s strong expertise in the area. Enterprises and service providers often struggle with their data cycle management which needs a holistic view and not just restricting within the constraints of the service agreements. Data cycle management when backed by an efficient design thinking principle continues to dominate the AI and Data Science space, keeping the users at the centre of business growth and increased efficiency in business operations. Design thinking and end-to-end data framework generally do not get discussed by enterprises – a major phenomenon that is engulfing enterprises, not for good. In my experience and in sync with what we continue to do at Ignitho Technologies, one needs to close the loop between the following 5 step Data Lifecycle: Data Strategy – Leverage decision insights from data derived out of a solid foundation Data Ops – Create resilient and effective data pipelines Compliance and Security – Reduce risk of data loss and privacy from the ground up Insights Generation – Generate insights that are not just predictive but also prescriptive Insights Operationalization – Making sure the insights are internalized into the right business processes The closed loop Data Lifecycle Framework works as a self-sustaining model with set of processes and constant optimization. But how does one ensure that this Data Lifecycle is brought to life? The value add of bringing the above framework to life is powered by Design Thinking – asking the right questions at the right level and focussing on internal and external user needs. Design thinking when done right, evaluates the customer’s point of view, while also considering the goals and objectives that the business itself needs to accomplish. Not sure how to take the first step towards accelerating your AI adoption? Here’s our short Online Analytics Maturity Assessment which has been appreciated by CXOs across enterprises for the eye opener it has been for them. We are sure it will help you gauge your organisations AI adoption, do give it a try. The results are realtime across 5 dimensions and you can compare how your organisation scored over others. We would love to know your feedback. We are at the cusp of new opportunities coming our way and our POD based tribes led by our CTO, Ashin Antony continue to scale up as experts in AI enabled human centric engineering. We are excited about this journey of igniting thought. Do let us know your feedbacks and queries.
A CIO’s guide to the need for Frugal Technology Innovation in Enterprises
In our previous two blogs in this series, we discussed what Frugal Innovation is, and the five principles that guide Frugal Innovators. While still relatively unknown in an enterprise context, the Frugal approach to Technology Innovation in the Enterprise may just be the golden ticket for CIO’s and business leaders to help escalate the pace on effective innovation at a time of rapid business disruptions caused by COVID-19. CIO’s and business leaders already recognize that innovation is no longer a luxury but a necessity for an enterprise today. Recent corporate history shows that innovation could well be the difference between exponential success or rapid decline in the enterprise. Consider the well-known examples of Kodak and Blockbuster, giants in their time but who no longer exist today out of poor reactions to the changing business environment and consumer behavior. Kodak underestimated the potential of digital photography which later disrupted the entire industry and replaced its film-based photography. Similarly, when Blockbuster CEO John Antioco and his team laughed at the proposal of partnership with Netflix in the year 2000, little did they know what waited for them in the coming years. In an enterprise context, innovation is normally a result of a burning need, an emerging trend or a popular new technology platform, or a convergence of these. For example, look at how the enterprise landscape has changed because of the coupling of a need with a new technology trend, such as gaming, social media and the emergence of super-powerful smartphones and tablets. When mobile took over the user experience factor, businesses had to adapt and deliver mobile-friendly applications to attract and retain their customers. While clearly recognizing the need to innovate quickly, enterprise CIOs face practical challenges in using a one-size-fits-all, big bang approach to all technology innovation. Our discussions with over 100 CIO’s have thrown up the following top issues. Lots of ideas but no sufficient bandwidth to nurture Innovation in an enterprise is often not a problem of finding ideas. In many scenarios, the CIO or innovation group is bombarded with a plethora of ideas coming from various internal sources. The problem therefore really lies in finding the necessary bandwidth to nurture these ideas, to run alongside larger transformation initiatives and business-as-usual. Need help in qualifying ideas and creating business cases The CIO’s we spoke to tell us that they would welcome advice and extended bandwidth to qualify ideas based on factors such as effort, capital, output, and success. Often though, this comes at a high financial cost and may also be time-consuming, resulting in ideas either being dropped or a loss of the window of opportunity. Identifying ideas that require minimum effort and provide maximum output is easier said than done using conventional approaches to innovation. Not enough good ideas that qualify While there may be a long list of potential innovation ideas, sometimes innovators face the issue of good quality ideas. When the success rates of these available ideas are compared with certain metrics such as the effort, capital and output required, many of them fall off the scale, resulting in a lot fewer ideas. Limited budgets can only nurture a few ideas Big bang transformational approaches to innovation are normally very expensive and time-intensive, thereby consuming whatever little budgets were available in the first place. Once again, this may cause other potentially brilliant ideas to fall by the wayside for lack of available budget and resources. No designated budget for innovation in non-core solutions Surprising as it may sound in today’s digital era, CIO’s are still often stifled by the lack of appetite within the enterprise to invest in new ideas. They must work very hard to push an agenda of innovation to run alongside business as usual initiatives. As a result, most ideas fail to get off the ground using the traditional big bang approach to innovation. Frugal Technology Innovation may be the answer Ignitho’s Frugal Technology Innovation methodology (Doing More with Less), built-in conjunction with Jaideep Prabhu, one of the world’s leading authorities and best-selling author on the subject, helps tangibly demonstrate ideas to the business stakeholders using limited resources through Rapid Prototyping, which can be ramped up to Scalable Solutions based on early success. Ignitho’s Innovation Labs, its unique peer ecosystem, and proven high-quality business and technical resources, are already translating business ideas into successful reality for enterprises. Talk to us today to find out more and get started on your own Frugal Technology Innovation journey in your Enterprise.