tech-advisory Archives - Ninestars Digitization Services

Building an Innovation Foundation: The Key to Sustained Success

In today’s fast-paced business environment, the ability to innovate is no longer a luxury; it’s a necessity. Companies that fail to adapt to changing market dynamics often find themselves on the brink of extinction. The importance of establishing a robust foundation for innovation cannot be overstated as it allows organizations to stay relevant, meet evolving customer needs, and create long-term value.

The Power of Innovation: A Look at History

Historically, the most successful companies are those that have embraced innovation as part of their core strategy. Conversely, organizations that resisted change or failed to invest in R&D have faced dire consequences.

Kodak, for instance, serves as a cautionary tale in this regard. Once a dominant player in the photography industry, Kodak was slow to embrace digital photography. Despite inventing the first digital camera in 1975, the company’s management was hesitant to pivot away from its profitable film business. In the 1980s, Kodak controlled over 90% of the film market. Their downfall highlights the importance of spotting emerging trends and being willing to adapt. As a result, it missed the digital revolution, leading to its bankruptcy in 2012. This example underscores the importance of not only recognizing emerging trends but also adapting to them in a timely manner. In fact, several companies have failed in recent years due to their lack of innovation and inability to adapt to market changes. Tupperware which seemed like a household product until recently is struggling with declining sales due to its failure to shift from direct sales to online platforms. Toys “R” Us filed for bankruptcy in 2017, failing to compete with e-commerce giants like Amazon. Blockbuster famously ignored the rise of streaming services, leading to its collapse. Nokia failed to innovate in the smartphone market, losing out to Apple and Samsung. Yahoo missed opportunities to innovate in search engines and digital advertising, leading to its decline. Lastly, Sears, once a retail giant, failed to modernize its operations and was overtaken by e-commerce and fast-fashion retailers. These companies show the critical importance of staying ahead of industry trends and technological changes.

In contrast, several companies have thrived by constantly innovating, even stepping outside their traditional products and markets. Apple is a prime example, having started with computers, but branching into phones, music players, wearables, and even services like Apple TV+. They have consistently pushed the boundaries of technology and user experience. Amazon, originally an online bookstore, evolved into a massive e-commerce platform, cloud service provider (AWS), and entertainment hub (Amazon Prime Video). Tesla, primarily known for electric cars, ventured into energy storage solutions and solar power, constantly driving innovation in the energy and transportation sectors. Microsoft, once focused solely on software like Windows, shifted towards cloud computing with Azure and ventured into hardware with the Surface series and gaming with Xbox. Disney transitioned from traditional media to acquiring companies like Marvel and Pixar, and now thrives with Disney+ in the streaming world. Finally, Google (Alphabet) started as a search engine but expanded into areas like AI, autonomous driving (Waymo), and even hardware with Google Pixel. These companies have shown a relentless pursuit of innovation, often taking risks and stepping beyond their core offerings to maintain growth.

Lessons from Failed Innovations

While successful examples provide valuable lessons, failures teach us important insights. BlackBerry, once a dominant player in the smartphone market, is a classic example of how complacency can lead to decline. After introducing the first smartphone with a physical keyboard, BlackBerry failed to adapt to the touch-screen revolution led by Apple’s iPhone. Despite having a loyal customer base and innovative capabilities, BlackBerry’s leadership underestimated the importance of software and user experience. Interestingly, BlackBerry’s original name was Research In Motion (RIM), emphasizing its focus on innovation in telecommunications. The company eventually pivoted to software and security solutions but serves as a reminder that failing to innovate can result in obsolescence.

Creating an Innovation Foundation

Building a strong innovation foundation involves several key elements:

Leadership Commitment: Effective innovation starts at the top. Leadership must foster a culture that encourages creativity, experimentation, and risk-taking. Companies like Google exemplify this approach, allowing employees to spend a portion of their time on personal projects that may benefit the company. This has led to the development of products like Gmail and Google News.
Investment in R&D: Allocating resources to research and development is crucial for staying ahead. Companies such as Amazon continually invest in new technologies, whether through advancements in AI or exploring drone delivery systems. This focus on R&D has enabled Amazon to diversify its services and improve customer experience.
Agile Processes: In a world where change is constant, businesses must be agile. Companies that embrace agile methodologies can respond quickly to market changes and customer feedback. For instance, Netflix has transformed its business model from DVD rentals to a streaming service by continuously iterating based on user preferences and technological advancements.
Collaboration and Open Innovation: Encouraging collaboration across departments and with external partners can lead to innovative ideas. The open innovation model, popularized by Henry Chesbrough, emphasizes that not all smart people work for you. By collaborating with startups, academic institutions, and other organizations, companies can tap into a broader range of ideas and technologies.
Customer-Centric Approach: Understanding customer needs is essential for innovation. Businesses that prioritize customer feedback in their product development processes are more likely to succeed. For example, Spotify has used data analytics to tailor its services to individual preferences, creating a unique listening experience that keeps users engaged.

Ninestars: A Commitment to Innovation

At Ninestars, we understand the significance of building a strong innovation foundation. We made investments in R&D very early in our journey so that we can provide advanced, differentiated solutions to our customers. Our commitment to technology and R&D aligns with the principles discussed. Our research team has been constantly leading us in product innovation and service excellence, incorporating advanced technologies such as generative AI and meeting the needs of our customers.

By fostering a culture that encourages creativity and collaboration, we ensure that our teams are empowered to develop innovative solutions. As technologies evolve and trends change, our focus on R&D and innovation will drive value creation and competitive edge for our customers and partners across industries.

Exploring the Evolution of AI: From Basic Algorithms to Machine Learning and Beyond

AI is no longer just a concept of science fiction; it is now a reality shaping our lives and the world around us. From early attempts to imitate human reasoning to more sophisticated machine learning processes, it has emerged as one of the most widely applied technological advancements in our time, finding practical applications in almost all industries including banking, healthcare, education, entertainment, gaming, and even art.

In this blog, we will explore the various stages of AI development to understand its evolution over the years and its potential for the future.

Stage 1: Rule-Based Systems (1950s-1980s)

Rule-based systems, the first stage of AI development, included formulating a set of guidelines that an AI system might utilise to make judgements. This strategy was founded on the notion that if a human expert could describe their decision-making process in a particular domain, a computer programme could do the same.

The Dendral project, which got its start in the 1960s, was one of the first instances of rule-based systems. Dendral was a programme created to use mass spectrometry data to infer the structure of unknown organic compounds. Dendral was successful in properly identifying the structure of unidentified compounds by codifying the scientists’ expertise in a set of principles.

Another example of rule-based systems is the MYCIN system, developed in the 1970s, which was designed to diagnose bacterial infections. Approximately 69% of infections could be correctly identified by MYCIN, which was regarded as quite impressive at the time.

Stage 2: Machine Learning (1980s-2010s)

The second stage of AI development was machine learning, which involves developing algorithms that can learn from data. In this method, rules are learned by the computer programme from the data rather than being encoded.

One of the earliest examples of machine learning is the backpropagation algorithm, which was first proposed in the 1980s. Backpropagation is a technique used to train neural networks, which are a type of machine learning algorithm. Neural networks have been utilised for a range of applications, including image identification and natural language processing, since they have the capacity to learn complicated patterns from data.

The IBM Watson system, which became well-known for its performance on the television quiz programme Jeopardy! in 2011, is another illustration of machine learning. With the aid of its extensive knowledge base and analysis of natural language cues, Watson was able to outwit two human champions.

Stage 3: Deep Learning (2010s-present)

The third and current stage of AI development is deep learning, which is a subset of machine learning that uses neural networks with many layers. Deep learning has led to significant advances in AI, particularly in areas such as image and speech recognition.

One of the most famous examples of deep learning is AlphaGo, developed by Google’s DeepMind. AlphaGo is a program that plays the board game Go and was able to defeat the world champion in 2016. AlphaGo used deep learning techniques to analyze millions of past games and develop its own strategies.

Another example of deep learning is GPT-3 (Generative Pre-trained Transformer 3), developed by OpenAI. GPT-3 is a language model that can generate human-like text and is able to perform a variety of natural language processing tasks, including language translation, question answering, and text summarization.

The future of AI is bright, and we’re excited to see where this technology will take us next.

NFT Culture: Hype or Favourable?

With a hefty $91.8 million price tag, The Merge (collection of art series) is the most expensive NFT sold so far. This series was created by renowned digital artist Pak making him arguably the most valued living digital artist.

NFT culture survives at the intersection of art, culture, and the blockchain. Investors and collectors seem to be pouring money into non-fungible tokens, tokenizing anything unique as an Ethereum-based asset. In fact, sales are believed to surpass last year’s total by going up to $90 billion by the end of 2022 despite NFT prices seeing a downturn of late.

But what’s really manoeuvring the market and what makes people want to invest millions of dollars in NFTs?

Getting to know NFT

According to Wikipedia, “A non-fungible token (NFT) is a record on a blockchain which is associated with a particular digital or physical asset. The ownership of an NFT is recorded in the blockchain, and can be transferred by the owner, allowing NFTs to be sold and traded”.

NFT is simply any form of digital asset that symbolizes real-world objects like art (any form), music, in-game items, and videos. They are traded online using mostly cryptocurrencies and they are encoded with blockchain.

Cash (physical money) and cryptocurrencies are “fungible,” meaning they can be exchanged or traded for one another (which defines specific value for specific items). They’re also equal in value: one rupee is always worth another rupee; one Bitcoin is always equal to another Bitcoin. The nature of Crypto’s fungibility makes it a trusted means for carrying out business transactions and administering them on the blockchain.

Even though cryptocurrency and NFT have almost the same encoding software the similarity ends there. Every NFT has a unique digital signature that makes it rather impossible to be exchanged or considered equal (hence, the “non-fungible” moniker). For example, an NBA Top Shot gif is not equal to a cat gif just because they’re both NFTs. For that matter, even two seemingly similar NBA Top Shot clips aren’t necessarily equal to each other.

But, anyone can view the individual images or even the entire collage of images online for free. So why are people crazily spending millions on something they could easily download, screenshot or just access online?

Because an NFT allows the buyer to own the original item (OG is the new trend!). But that’s not the only reason, NFTs contain built-in authentication, which offers proof of ownership. Collectors and investors value the “digital bragging rights” more than the piece itself (most of the time).

Essentially, NFTs are like physical collector’s items, but only in digital form; i.e., when a collector auctions a painting, instead of getting an actual painting to hang on the wall, the buyer gets a digital file instead. They also get exclusive ownership rights. NFTs work with the concept of ‘one owner at a time’, and their adoption of blockchain technology makes it easy to verify ownership and transfer tokens between the owners. The creator of the art piece can store specific information in an NFT’s metadata in the file, like their signature.

NFTs are offering more power and control to content creators than ever before…

Looking beyond the hype and the price tags; NFTs could represent a huge opportunity in content marketing. Each token has an exclusive and transparent ID which can be used to track and control them. This paves the way for fundamental changes in the relationship between content creator, consumer and brand.

Traditionally after the first sale goes through, the artist or the creator’s journey ends there with their item. Once the art has been sold, the transactions usually end there. The buyers, however, could sell the same work of art again at higher prices if there is demand and if the buyer is willing to sell. However, there is one more way that the non-fungible tokens help the creator and is called ‘NFT Royalties’. NFT royalties give a certain (usually specified) percentage on the sale price every time the NFT creation is sold on a marketplace. These are automatic pay-outs to the author made on repeated sales. The percentage of the royalties are coded into the smart contract on the blockchain of the NFT. Each time a secondary transaction takes place, the encoded smart contract ensures that the terms of the NFT are fulfilled to the dot and the specified cut of the royalty percentage on the sale price goes to the artist who created them.

This works equally well for digital content, gaming accessories, physical items, real estate etc. NFT royalties are a never-before-opportunity to maximize the earnings of artists and content creators and to give them what is their fair share of profit.

NFT has the power to triangulate the relationship between artists, collectors, and the brand to create a strong community that benefits all.