Group items tagged

One of the top reasons for digital innovation and transformation failures can be summarized as the enterprise trying to reinvent the wheel. This is the tendency of the enterprise to attempt to create technology, platforms, and applications that have already been implemented, scaled, optimized and almost perfected. This tendency almost always ends up a failed one as it does not create any net new value for the enterprise but comes with a massive opportunity cost as the enterprise spends crucial resources on reinventing the wheel than innovating for the customer. How The Wheel Is Reinvented Nontechnology enterprises can get trapped in a reinvent state where they conclude that homegrown technology is the only path towards customer and business value. This in itself is not entirely false however the type of enterprise and their decision-making process along with the capabilities they have in house have a very large impact on the success of the strategy.eRaaadada Reinventing the wheel happens when a non-tech enterprise discovers a technology trend towards much later in the hype cycle almost towards the end when the technology is hitting the mass market and decides to recreate or reinvent its own version of the technology. This is often done with the assumption that with some investment, the enterprise can have a home grown version of technology or platform that is designed specifically for its needs and is thus a better fit. However, enterprises assume that the state of the technology will remain constant and while they are attempting to home grow a version that can match the current state of the art. In reality, the state of the art shifts and the enterprise is not able to bridge the gap. Who Reinvents The Wheel Typically, technology teams often decide to go down the path of reinvention when they are allowed to make technology upgrade or technology creation decisions without business KPIs and cost constraints i.e. clear success criteria with fixed cost and clear ti

A pitfall in the path to innovation and disruption is the lop sided focus on "creation" and not enough focus on "consumption". Creation is the process of creating platforms, products, and solutions where as consumption focusses on ensuring that the created artifacts deliver the intended value. Too often enterprises get caught up in "creation" or enabling "creation" and lose sight of the fact that without consumption, anything they create is bound to be a failure. Creation & Consumption can not be Sequenced A misconception that often exists is that "if we build it, they will come" i.e creating the product and solution is enough and its existence will automatically lead to consumption and value generation for the customer. However, creation and consumption cannot be sequenced i.e. made a focus sequentially. Creation and consumption only succeed when they go hand in hand where tight, iterative loops ensure that the creation is informed by consumption trends and feedback and that consumption is also leveraging the latest creations. What Does A Lop-Sided Focus Look Like The top 3 signs of this lop-sided focus are as follows Focus on Building Platforms When the focus of the enterprise is building platforms and when customer value is only created when a developer leverages the platform to build a customer facing application, it often means that the effort invested in the platform has no ROI. When the focus is on platforms, application investment suffers and the platform builders get sidetracked with platform KPIs as opposed to business value KPIs. The platform builders might only focus on "Developer" satisfaction when ultimately, customer satisfaction matters for the business. Focus on Tools, Not on Solutions Another sign of lop-sided investment is a focus on tooling at the expense of solutions. Enterprise teams can often go overboard with building large libraries of tools and using the adoption of the tool as a metric of success. However, simil

We are a different AI company. See how our LearnCloud brings together your entire enterprise to operate on the same "information plane" towards an exponential increase in efficiency. Jenny is responsible for customers at Acme Corp Jenny feels she is driving blind. She realizes the ground truth but often it is too late. The information is spread across multiple applications & systems. Even when available, it does not help her look into the future so that she can act & save the day Sarah is a Support professional at Acme Corp Sarah feels she has her hands tied behind her back. She has to manually process each & every issue. Her job is tedious and inconsistent. She is not able to leverage patterns across issues & across support professionals. John is a salesperson at Acme Corp John is frustrated with antiquated tools & late arriving insights He has to manually scour the internet for information about his prospects & their changing strategies. He has to talk to many people to understand the state of the customer. By the time he understands the current state, it is often too late to prevent abandonment or drive the expansion People like them encounter these problems all the time which causes frustration and attrition for them and their customers. How does this happen?

A comprehensive set of auto-learning, AI-driven applications.

Digital Transformation is tough, even for seasoned technologists. This is because it is a transformation of an organization at its core. Everything from culture, technology, ideation, development, integration, delivery, and support needs to fundamentally shift to be more customer-centric, service driven, automation first and experimental in nature. No wonder that a lot of organizations take a long time and a lot of investment to see ROI from their digital transformations. Here are 5 signs that your digital transformation might be in trouble. Culture mistrusts the core digital transformation team You are spawning new initiatives before completing previous ones Decisions are top down with low accountability at the leaf nodes You tend to focus on technology stacks with little focus on customer value Inter-organization politics stifles cross-organization scenarios Culture mistrusts the core digital transformation team It is almost impossible to make an entire organization aware and participate in digital transformation at the same time. There are exceptions but in our experience, starting out with a core digital transformation team is a much better strategy than otherwise. This team should be enabled to attack a limited set of important and business relevant problems, build cutting-edge solutions and use them as examples to train and evangelize digital transformation strategies to the rest of the organization. However, the more entrenched an organization in the old way of doing things, the harder they might this central team. Resistance can be active and passive such as refusal to share data or provide the relevant context of the problem. An organization that does not set up the early crusaders for success almost always has a much harder time showing value from their digital transformation activities. You are spawning new initiatives before completing previous ones Executing on a digital transformation strategy is much harder than defining the strategy especially fo

Success with AI is heavily influenced by the data maturity of an organization i.e. their ability to procure, clean, curate, store and analyze data to power value generating applications. A data-mature enterprise know what data it has, knows what the data means and can ensure that the data is accessible to whoever needs it. Unfortunately, the past few years, driven by the big data hype, have encouraged enterprises to focus on updating their data infrastructure to leverage new big data technologies. With a lot more data now available, enterprises already stuck with massive data storage costs, are being forced to choose between storing data that might eventually be useful for stabilizing, if not reducing their storage costs.

Much has and needs to be said about an enterprise's AI strategy. Artificial Intelligence or AI is considered a fundamentally disruptive technology similar to the steam engine, electricity etc, a technology that will be pervasive and absolute in its impact on the world and its inhabitants. The ability to find hidden patterns to predict the future or detect a behavior has massive implications across the world, in every industry, sector, and domain. When faced with this realization, enterprise's can find themselves stuck, paralyzed and unsure about how to proceed. The field of AI is decades old already and the early success stories have been practicing AI for multiple years already with the tech industry leading the way. How can an enterprise that has no experience and competency in this area let alone lead the technology or even leverage it appropriately to drive business value? When developing the AI strategy, two ideas are paramount. First, this a fundamentally disruptive technology and the enterprise will need to establish it as a core competency for the foreseeable future. Not doing so will not be an option. Second, a long-term plan to success is superseded by the need to drive quick wins and small successes not only to build confidence but use real-world experience to develop and hone that skill. The Short-Term AI Strategy The short-term AI strategy should focus on driving immediate business value through enhanced customer experiences that leverage any field of AI be it machine learning, deep learning, natural language processing etc. Driving the usage and deployment of AI in front of an end user making them smarter, productive and better informed can pay rich dividends by not only helping the enterprise can real-world experience, but it can also give a perception boost to the company as being innovative and cutting edge. However, most importantly, this can highlight and promote the success and potential of AI in the enterprise and encourage a snowball eff