The Changing Global Delivery Model for AI Led Digital Engineering
As the technological and cultural landscape undergoes tectonic shifts with the advent of AI in a post-pandemic world, businesses are striving to stay ahead of the curve. At Ignitho, we are trying to do the same – not just keep pace but shape the future through our global delivery model augmented by our AI center of Excellence (CoE) in Richmond, VA. This CoE model, firmly anchored in the “jobs to be done” concept, reflects Ignitho’s commitment to creating value, fostering innovation, and staying ahead of emerging trends. Let’s examine three key reasons why Ignitho’s approach to creating this upgraded global delivery model is a game-changer. Embracing the AI Revolution The rise of artificial intelligence (AI) is redefining industries across the board, and Ignitho recognizes the pivotal role that data strategy plays in this transformation. Rather than simply creating conventional offices staffed with personnel, Ignitho’s global delivery model focuses on establishing centers of excellence designed to cater to specific functions, be it data analytics, AI development, or other specialized domains. Our AI Center of Excellence in Richmond, VA promises to become that source of specialized application. By actively engaging with our clients, we have gained invaluable insights into what they truly require in this ever-changing technological landscape. Our global delivery approach goes beyond simply delivering on predefined roadmaps. Instead, it involves close collaboration to navigate the intricate web of shifting data strategies and AI adoption. So, in a world where data is the new currency and AI is transforming industries, the process of crafting effective solutions is no longer a linear journey. Ignitho’s Centers of Excellence in the US plans to serve as a collaborative hub where our experts work closely with clients to make sense of the dynamic data landscape and shape AI adoption strategies. Then the traditional global delivery model takes to do what that model is good at. This approach to AI is not just about creating models, churning out reports, or ingesting data to various databases; it’s about crafting and delivering a roadmap that aligns seamlessly with the evolving needs of the clients. Reshaping Digital Application Portfolios The second pillar of Ignitho’s global delivery model revolves around reshaping digital application portfolios. Traditional software development approaches are undergoing a significant shift, thanks to the advent of low-code platforms, the need for closed-loop AI models, and the need to adopt the insights in real time. So as with the AI programs, Ignitho’s model allows us to engage effectively on the top-down architecture definition in close collaboration with clients, delivering a roadmap that then subsequently leads to the conventional delivery model of building and deploying the software as needed. The different global teams employ similar fundamentals and training in low-code and AI led digital engineering, but the global team is also equipped to rapidly develop and deploy the applications in a distributed Agile model as needed. By adopting such an approach, Ignitho ensures that the right solutions are developed, and the clients’ goals are more effectively met. Shifting Cultural Paradigms As the global workforce evolves, there’s a notable shift in cultural patterns. People are increasingly valuing outcomes and results over sheer effort expended. Networking and collaboration are also no longer limited to narrow physical boundaries. Ignitho’s global delivery model aligns seamlessly with this cultural transformation by focusing on creating value-driven centers of excellence. As a result, by delivering tangible and distinct value at each of the centers, Ignitho epitomizes the shift from measuring productivity by hours worked to gauging success by the impact created. What’s Next? Ignitho’s upgraded Center of Excellence based global delivery model is better suited to tackle the challenges and opportunities posed by AI, new ways of digital engineering, and evolving cultural norms where success is taking on new meanings. So, as the digital landscape continues to evolve, businesses that embrace Ignitho’s approach stand to gain a competitive edge. The synergy between specialized centers, data-driven strategies, and outcome-oriented cultures will enable us to provide solutions that resonate with the evolving needs of clients across industries. As a result, we are not just adapting to change but we are driving change.
C-Suite Analytics in Healthcare: Embracing AI
C-suite healthcare analytics has become more crucial than ever in today’s rapidly evolving healthcare landscape characterized by mergers, private equity investments, and dynamic regulatory and technological changes. To create real-time, actionable reports that are infused with the right AI insights, we must harness and analyze data from various sources, including finance, marketing, procurement, inventory, patient experience, and contact centers. However, this process often consumes significant time and effort. In addition, maintaining a robust AI strategy in such a dynamic landscape is no easy task. In order to overcome these challenges, healthcare leaders must embrace comprehensive business intelligence and AI-powered solutions that provide meaningful dashboards for different stakeholders, streamline data integration, and enable AI driven predictive analytics. In this blog we will: Highlight key challenges Present a solution framework to addresses these critical issues Key Industry Challenges Not only do healthcare organizations face internal challenges such as harnessing data from various sources but they also encounter industry dynamics of M&A. To create actionable reports when needed for board level reporting and operational control, the various sources of data that must be integrated in such as environment is daunting – finance, marketing, procurement, inventory, patient experience, and contact centers, and so on. Dynamic M&A Landscape The healthcare industry is experiencing a constant wave of mergers and acquisitions, leading to an increasingly complex data and technology landscape. When organizations merge or acquire new entities, they inherit disparate data systems, processes, and technologies. Integrating these diverse data sources becomes a significant challenge, impeding timely and accurate reporting. Consider a scenario where a healthcare provider acquires multiple clinics of various sizes. Each entity may have its own electronic health record (EHR) system, financial software, and operational processes. Consolidating data from these disparate systems into a unified view becomes a complex task. Extracting meaningful insights from the combined data requires specialized integration efforts Data Fragmentation and Manual Effort Healthcare organizations operate in a complex ecosystem, resulting in data fragmentation across different departments and systems. Extracting, aggregating, and harmonizing data from diverse sources can be a laborious and time-consuming task. As a result, generating up-to-date reports that provide valuable insights becomes challenging. Example: Pulling data from finance, marketing, and patient experience departments may involve exporting data from multiple software systems, consolidating spreadsheets, and manually integrating the information. This manual effort can take days or even weeks, leading to delays in obtaining actionable insights. Need for Predictive Analytics To navigate the changing healthcare landscape effectively, organizations require the ability to make informed decisions based on accurate predictions and what-if analysis. Traditional reporting methods fall short in providing proactive insights for strategic decision-making. Example: Predicting future patient demand, identifying supply chain bottlenecks, or optimizing resource allocation requires advanced analytics capabilities that go beyond historical data analysis. By leveraging AI, healthcare leaders can gain foresight into trends, mitigate risks, and drive proactive decision-making. How to address these Challenges? To address these challenges, we need a top-down solution (AI driven CDP accelerator for healthcare) that has strategically been designed to address them. Trying to tackle the integrations, reports, and insights needed on a bespoke basis every time a new need arises will not be a scalable solution. Some of the key features are below of such an integrated C-suite analytics solution that combines data from multiple sources and leverages AI capabilities. This solution should possess the following features: Meaningful Predefined Dashboards The analytics platform should provide intuitive and customizable dashboards that present relevant insights in a visually appealing manner. This empowers C-suite executives to quickly grasp the key performance indicators (KPIs) that drive their decision-making processes. These dashboards should address the relevant KPIs for the various audiences such as the board, c-suite, operations, and providers. Example: A consolidated dashboard could showcase critical metrics such as financial performance, patient satisfaction scores, inventory levels, and marketing campaign effectiveness. Executives can gain a comprehensive overview of the organization’s performance and identify areas requiring attention or improvement. AI-Powered Consumption of Insights As recent developments have shown us, AI technologies can play a vital role in managing the complexity of data analysis. The analytics solution should incorporate AI-driven capabilities, such as natural language processing and machine learning, to automate insights consumption, anomaly detection, and trends tracking. Example: By leveraging a simple AI based chatbot, the analytics platform can reduce costs by automating the reports generation. It can also help users easily identify outliers and trends, and provide insights into data lineage, allowing organizations to trace the origin and transformation of data across merged entities. Seamless Data Integration The analytics solution should offer seamless integration with various systems, eliminating the need for extensive manual effort. It should connect to finance, marketing, procurement, inventory, patient experience, contact center, and other relevant platforms, ensuring real-time data availability. Example: By integrating with existing systems, the analytics platform can automatically pull data from different departments, eliminating the need for manual data extraction and aggregation. This ensures that reports are current and accurate, allowing executives to make data-driven decisions promptly. AI-Driven Predictive Analytics Utilizing AI algorithms, the analytics solution should enable predictive analytics, allowing healthcare leaders to identify trends, perform what-if analysis, and make informed strategic choices. Example: By analyzing historical data and incorporating external factors, such as demographic changes or shifts in healthcare policies, the AI-powered platform can forecast patient demand, predict inventory requirements, and simulate various scenarios for optimal decision-making. Provide a Path for Data Harmonization and Standardization In addition to the challenge of integrating different data systems, organizations face the hurdle of harmonizing and standardizing data across merged entities. Varying data formats, coding conventions, and terminology can hinder accurate analysis and reporting. Example: When merging two providers, differences in how patient demographics are recorded, coding practices for diagnoses and procedures, and variations in medical terminologies can create data inconsistencies. Harmonizing these diverse datasets requires significant effort, including data cleansing, mapping, and standardization procedures. Next steps In an era of rapid change and increasing complexity,
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
A Guide to Innovation Pods: The Future of Enterprise Delivery Model
As a C suite executive your business growth has never been by mere chance; it is the result of the right strategies and catalysts working together for your success. In this journey of building a mighty business for your company one of the tricky challenges that you might have faced is to be versatile in a dynamic and massive business environment. The questions are many as to how to rapidly adapt and develop ideas to align with the changes, capitalizing and navigating around the opportunities and risks efficiently or how to integrate the agility that you see around within your company. The answer lies in how you distribute the control to make the decisions regarding a project promptly with close alignment with the clients. This could only be achieved through autonomous entities that can act on their own without interfering in other business activities that exist within your work ecosystem. Innovation pods An innovation pod is a small, autonomous unit that is cross-functional enabled to deliver projects that the clients appreciate through distributed agile methodology. With the digital switch-over that’s occurring at a fast pace across the domain, emerging innovation pods that convene multi-functional teams, bringing together design with technology in a project to get the best out of the idea, has taken hold as the brand-new centers of excellence (CoE) that will transfer the optimal value in these critical times. Innovation pods are employed to have a seamless delivery process, focusing clearly on the outputs and outcomes that you deliver to the clients. Pods are independent and scalable One big-time advantage of the pods is they are autonomous units that are functioning ahead of the “ripple effect” that might affect the usual operations within the company. They are free to innovate and endorse new work patterns and workflows; devoid of permissions. These pod-level innovations could be well adapted into multiple pods as this process is rather frictionless and productive. As pods are modular, adaptability becomes much easier which makes it more scalable and competent. There exists a good deal of latent skills within each pod that enables you to scale up when the need comes- focusing on the client’s requirements. When the time or the demand comes this digital pod can be reproduced into multiple pods bringing in new members who are cross-functional, thus minimizing the growing pains. Innovation Pods and Agile Methodologies Agile methodologies are gaining momentum since it has created a mindset that has moved far beyond a mere software development team. Today, leaders in the digital domain are employing agile methodology to transform their business workflows and culture. It is with this agile methodology that the concept of digital pods become more prominent concerning the way they ideate and function. An innovation pod is assigned throughout to a particular project; recurrent and consistent in its structure at the same time. Each pod will be having its unique sprint schedules to focus on. Consistency is one of the key aspects that is behind a productive creation and management of agile pods. Significant roles such as scrum masters are expected to be kept constant throughout the project along with other key responsibilities. Right from developing the hypothesis to maintaining and scaling the Minimum Viable Product (MVP) the pods are required to maintain steadiness. The innovation pod’s engagement model comes hand in hand with the agile methodology that has the following steps in common. Scoping– The project is scoped out thoroughly crisscrossing the client’s requirements. Kick-off– Initiate the project after sectioning off the project into phases. Discovery– From the several, critical problems that the client is facing are prioritized for the ease of building. Framing– Through funnel methodology, the solutions are generated for the problems based on their significance and urgency. Define– The solutions are defined for their integration into MVPs. MVP– A Minimum Viable Product (MVP) is developed incorporating the specified solutions. Iterative delivery– After the first phase of the cycle, the product is delivered which then kick start the second phase of the project. To maintain consistency, the pods are expected to be the same throughout the delivery process right from the scoping estimation to the iterative delivery. This is to make sure that the functional capability, scope of the work and the creep along with customer satisfaction remains intact in every step that the pod takes in the project. In this process, stakeholders on both sides become “Co-Innovators” where the enterprises are encouraged to make critical decisions during the early stages, thereby reducing risk in large scale investments. Align the pods to your agile methodology In order to maintain the spirit of the agile methodology, your innovation pods must be aligned in such a way as there exists a cascading effect happening right from the methodology to the pod members. To achieve this there are certain basic functions that your enterprise has to follow on: • Kick start the transition to agile pods by training the pod members in the right direction throughout the onboarding process. • While you generate the teams for the pod, take into consideration their skillsets and how they complement each other. • Provide the right autonomous control within the pods to make innovative and progressive decisions. • The scrum master should be sharing the best practices to be followed after the right ideation within the team and should encourage an open learning environment. • Make the right use of the pods’ skills as much as possible when it comes to the cross-functionality within the structure. • Leverage the use of right stacks and the right technologies as per the client’s requirements. But make sure that the new technologies are not employed in the midway of a delivery process. • Autonomy within the pods is a concept that needs to be ingrained within the pods over time. So be prepared to give the required time to the team to scale up themselves to play their respective roles in full efficiency during the process. Innovation pods can be stated as the future
Role of Innovation Consultant in Industry 4.0
Who is an Innovation Consultant? The multifaceted consulting industry ranging from strategy consulting to social media consulting consists of stalwarts who excavate every part of a business. These consultants identify areas of concerns for businesses, minimize these areas of weakness and strategize how to bring in the maximum output from the areas of strength. Innovation as a term had started gathering widespread usage only by the start of twentieth century and is now part of each industry. Every company is implementing innovation led practices in their growth strategies as well. An Innovation Consultant is one of the more recent roles which the consulting industry has developed because of the disruptions innovation is creating for multiple sectors and industries. Historically, Innovation, for the big players, has always been treated like a top secret lab that was protected from everyone except the researchers in their lab coats. But the fast pace of business and the ever-changing consumer demands have forced these companies to strategize on innovation to stay ahead of the curve or be quickly replaced by the competition. For example, Blockbuster being replaced by Netflix regarding the entertainment industry. Or how companies like Apple, Tesla, and Amazon are pushing their boundaries to meet their customer expectations. Companies like Nokia, Philips and General Electric have faltered with innovation despite having a focused innovation strategy. Innovation Consulting and Industry 4.0 The industrial revolutions that mapped the progress of human kind are a combination of some of the momentous events in history. Starting from the commercial steam engine during Industry 1.0, moving to harnessing electricity in 2.0 and finally ending up with the computers in 3.0. With the advent of interconnected technologies taking over our daily lives, we are already witnessing Industry 4.0. This rapid pace of digitization and innovation in enterprises is signaling towards the rapid adoption of these interconnected technologies eyeing rapid transformation. To keep up the pace with the rapid adoption of these interconnected technologies, the old methods of R&D labs and siloed approaches in innovation is going to hurt these enterprises. In the current scenario, for a majority of companies, innovation is considered a painstakingly long process and if an innovative product or service emerge from these innovation pods, most of them fail. Faced with adversities like lack of productivity, along with increased competition and shrinking innovation lifecycles, enterprises should no longer exclusively rely on internal R&D labs. According to a recent survey conducted by IBM and BCG, enterprises have placed internal R&D eighth out of nine, far behind the general employee population, business partners and customers in terms of important sources of innovation. These adversities and dissatisfactions point towards a tremendous opportunity for enterprises to use innovation consultants as the pivotal point for a new, more focused and rapid innovation process. An innovation consultant who could effectively communicate, collaborate and share information to effectively meet the innovation needs for the enterprises, could make the difference. What should one look in an Innovation Consultant? When one decides to onboard an Innovation Consultant (it can be a person or an organization), there are some key factors that should be considered. Revisit entrepreneurial spirit – Enterprises should identify Innovation Consultants who could help them revisit their entrepreneur self. These Innovation Consultants should help the enterprises to regain the unique value and differentiation these companies theoretically possessed when they started. This would enable these enterprises to sustain their growth while constantly innovating for their customers Create a vision for customers – In order to stay ahead of their competition, enterprises should work alongside the Innovation Consultants and create a roadmap for the services and products being offered to customers. The plan is to achieve these goals before the competition does. Identify and nurture ideas – The Innovation Consultant, along with the enterprises (specifically the Chief Innovation Officer (CIO) or Innovation Lead), should work to identify and qualify relevant ideas based on the effort and capital required for the Return on Innovation (ROI). Apart from the key factors which are mentioned above, enterprises need to analyze some of the key points when approaching an Innovation Consultant. This next section touches upon some of those factors. What should be the approach in innovation consulting? Innovation for enterprises is not just adopting best practices and cost-effective methods, there is also a mindset change which is required from the entire organization if they are to successfully implement an effective innovation strategy through a consultant. Below mentions some of the points: Keep the innovative edge : A common trend among enterprises who start out as innovative organizations is that they lose their innovative edge when they move from a startup to a scaleup. These companies lose focus of their vision as more and more effort go into keeping business as usual. Innovation Consultants can help enterprises to align their organization goals around innovation to retain their innovative edge. A supportive culture : An enterprise without ample encouragement in innovation initiatives is a recipe for disaster as far as innovation is concerned. An organization that gives importance to more conventional methods of Business as Usual would most probably have an adverse environment for innovation. An innovation consultant would provide an outsider view and could suggest improvements to nurture an innovative culture. Ideas : Enterprises are often bombarded with a plethora of ideas. Many times, the problem comes in while filtering these ideas and finding enough time to nurture these ideas. Innovation Consultants can help these enterprises to better communicate and collaborate on the best ideas. Market Intelligence : To stay relevant among the competition, an enterprise who finds it hard to implement innovation might try to become the sheep and follow the herd. Instead these enterprises should work with Innovation Consultants to gather market intelligence to narrow down on decisions based on relevant data. Conclusion Finding the right Innovation Consultant, at the right time, can be very crucial for companies looking to maintain that innovation edge. When working with a person/company to help with your innovation, it is best to partner with a company who lives, eats
Return On Innovation: What You Need To Know As An Innovation Leader
It has been a while that we have been hearing the term “Innovation” almost everywhere and we have been lured to believe in its existential importance. But how many of us have taken a moment to pause and think what exactly Innovation does to one’s business? CIO Magazine rightly points out the insights of Colliers International CIO Mihai Strusievici who had been dealing with the pressure of developing fast and efficient business solutions quickly. He says, “We can create applications very fast, but our business partners may have expectations that if you do it fast, that you also got it right on the first attempt, iteration is harder to accept than one would believe.” This has made the global real estate services team face many challenges as innovation was taken from theory into practice. The biggest worry for Strusievici is the realization that most of the business heads have become frustrated with what they perceive as half-cooked ideas. “I don’t know if the [internal conflict] will emerge as creative energy, or if it will bring [innovation] to its knees out of fatigue.” This is not just the case of Strusievici. There are many Strusievicis out there who face similar challenges when they measure the new ROI – Return on Innovation. Innovation for your Enterprise For many, Innovation isn’t just a representation of novel devices, ideas or methods but the process of revealing a new way of doing things. It can also relate to transforming enterprise models and conforming the changes to attain optimized products and services. To sum up, innovation is nothing but a consolidation of creativity and work which makes a process that utilizes the creative ability. In the enterprise ecosystem, CIOs are in search of business solutions that are novel, inexpensive and caters to their business needs and values. Therefore, for an innovation to thrive, it must be reproduced without being too expensive when solving the specific need. Businesses who vigorously take up the innovation as an opportunity in a highly dynamic business environment are more likely to not only survive but also flourish adequately in the middle of harsh economic conditions. They employ innovation as a technical and strategic tool to build an agile culture to initiate improved business processes. IDC foresees that by 2022, around 80% of the business revenue growth will depend on the digital offerings and operations. This reinforces the notion that there is very little room when it comes to project failure or an obvious tolerance for the quick and simple. Scaling Innovation Challenges Getting Innovation to scale up correctly from an idea to its phased implementation is extremely difficult for even the experts within the business ecosystem. KPMG report on Benchmarking Innovation 2019 states that 60% of the executives who are responsible for Innovation and strategies have cited competing priorities as one of the biggest challenges in scaling Innovation where 59% stated that company culture was another key challenge. Catching up with advances in technology, Innovation leaders are developing their business with IoT, AI & ML, Data Science, cloud computing and via social media. The scaleups in Innovation have been also changing the very basis of the competition that companies had. The increasing accessibility, and availability of innovative business solutions had made a hunger for the enterprises to become Innovative leaders in their fields. Innovation doesn’t have to be something humungous like the next IBM or Microsoft. An excerpt from a CIO Magazine article called What Really Makes Something Innovative? reads, “Sometimes it’s those quiet achievers who can make just as big an impact without having to be ostentatious about it.” It’s just that you must be original in your concepts – pro-active, self-assured and confident to take the risks and get it done quietly. “The problems that I’ve seen with innovation is, we look for ROI in every single project that we try to innovate. You need to have a venture capitalist mindset, especially when it comes to innovation. The company needs to say, ‘I need to invest in 10 ideas, and even if two of those succeed, it can benefit the company.’” says Satya Jayadev, Vice President and CIO at Skyworks. He has set three non-financial metrics to measure return on innovation: the number of Innovation projects it brought to the table, the number of projects amongst them that really got into a Proof of Concept (PoC) and the number of PoCs that went to actual production stage. In FY2019, thus out of all innovative ideas that were brought to the table, 60 got into PoC and 40 got converted into production. He says these metrics clearly indicate the value of innovation across the business. Ideas to ponder for Scaling up Innovation For a business to have an edge in quick-penetration and better connectivity of the markets, Innovation has proved to be vital with its ability to lead them to bigger opportunities. 1. Constantly verify and publicize success The innovators who successfully bring out Innovation take an idea or a hypothesis and scale it up through small executable steps. Throughout the process they constantly verify and publicize their success to their stakeholders. 2. Test cheaper and faster An effective way for Innovation teams to get on the right track is by developing the efficiency to experiment quickly in a cost-effective manner to generate reliable learnings than others in the same domain. The KPMG study has stated the ability to test, learn and iterate as some of the key enablers of success. 3. It’s OK to drop an idea It’s always better to drop an idea that might drain you. According to KPMG, giving too many attempts can result in having insignificant impacts on projects. Because for innovators, failures are steppingstones to success. KPMG survey states that even though organizations push themselves to be more tolerant to failures, it’s not something that they are going to embrace forever. The enablers of return on innovation will always be the right support from industry thought leaders, the right strategy for the innovation initiatives and a team with the optimized