Technology and Innovation Assignment Sample

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Introduction

Technology is a combination of the processes, techniques and tools to gain the goals and resolve problems. Innovation is practical implementation of the ideas which result in the introducing new services and goods in offering services and goods. The chosen organisation is Google, which is an American multinational corporation and technology company (Google, 2024). This is emphasising the artificial intelligence, consumer electronics, E-commerce, quantum computing, computer software and cloud computing. The report will shed light on the current digital technologies in the organisation.

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Task 1: Critically analysing the current digital technologies used in the chosen company

The technology plays a key role in the operation of Google, which is developing cloud computing services and search algorithms. As this is one of the global giants in technology harnessing digital technologies with the cutting edge, it can not only boost the service and product offering. This manages the competitive edge within the developing digital and innovative landscape. Google employs within the strategic impact, innovation, functionality and business ecosystem.

Technology and Innovation Assignment Sample

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Ben Thompson 7 Years | MSc in Computer Science

Machine learning and Artificial Intelligence: AI is a key part of the technological infrastructure of Google.  From the Google Assistant to the Google Search, the overall AI boosts the experience of users svia predictive analytics, image recognition and Natural Language Processing (NLP) (Rammert, 2021). For instance, the RankBrain of Google is a search algorithm based on AI that interprets the queries of search to offer the relevant outcomes, even for previous terms and ambiguous queries.

Machine learning is developing tools such as smart compose features and Smart Reply. This provides the suggestions of the predictive extent to usability, developing productivity. Google Translate effectively sees the key benefits of neural machine translation. This leverages machine learning to provide accurate and contextual translations. On the critical note, the AI develops the automaton and personalisation, which raises ethical concerns, mainly around the transparency, algorithmic bias and data privacy.

Cloud computing (Platform of Google Cloud- GCP):  The GCP is suitable for the services in cloud computing, which provides the software solutions, platform and infrastructure. The GCP permits developers and businesses to use developed AI tools, store information securely and host applications. The primary offerings involve the data of large-scale analysis of BigQuery. Kubernetes for the orchestration of containers and Tensorflow for efficient machine learning. On the other hand, apart from the technological strengths, the overall GCP faces issues because of rivals such as Microsoft Azure and Amazon Web Services (AWS). The enterprise support of consumer lack and fewer global data centres are effectively cited as the key limitations. On the other hand, Google is continuing to invest in developing the infrastructure of the cloud to reduce the gaps.

Quantum computing: Google has made ground-breaking progress in major quantum computing. Within the year 2019, the processors of the Sycamore reportedly gained the “quantum supremacy” by resolving the issues within more than 200 seconds (Fukuda, 2020). This will have an efficient supercomputer in the upcoming 10,000 years. On the other hand, Quantum computing is effectively experimental, which is along with the practical, limited application currently. The early investment of Google in the upcoming changing technology in overall computing.

(VR) Virtual reality and (AR) augmented reality: The VR and AR ventures of Google involve different products, such as Google Lens. This utilises the recognition visually to offer the information related to the real ARCore and real-time objectives. The platform for the development experiences of the AR on different Android devices via Daydream (Nguyen et al, 2020). On the critical note, Google has effectively embedded AR within other services such as Google Maps. The efforts in VR cannot be compared to those of the different competitors, such as (Formerly facebook) Meta.

Analytics and big data: Google excels in utilising big data, especially for advertising insights, tracking user behaviour and business intelligence. The different tools, such as Google Ads and Google Analytics, offer businesses insights into the ROI, campaign performance and customer behaviour. On the critical note, the Big Data use boosts the concerns of privacy, mainly as Google focuses on the data within the different platforms (Chrome, Android, YouTube and Search).

Task 2: Identifying current digital technologies gaps within the chosen company

Technological gaps in the digital infrastructure of Google: The significant gap within the digital systems of Google is the ethical use of AI and Data privacy. The organisation gathers the key user data amount within the different platforms, such as Android, YouTube, Gmail and search. This is subject to developing major criticism from the public and regulators related to the security and use of personal data. Moreover, the AI transparency lacks and Algorithmic bias is leading to the development of ethical concerns. The gap is related to the insufficient transparency within the decision-making AI and data collection. Developing the problems of the public pertaining to surveillance capitalism and algorithmic manipulation.

Collaboration tools are fragmented for work remotely: Apart from the workspace of Google, which provides productivity tools based on the cloud, the rivalry from Microsoft Teams within the collaborative workspace segment showcases the technological shortfall. The different businesses also prefer the primary utilisation of Microsoft Teams effectively for the integration of project management, calling and messaging. The gap includes the lack of deep integration among the project/ task management tools and communication (Tidd, 2023).  Inadequate workflow automation in real time and utilisation migration related to rivals because of the efficient integration and UX.

Limited progress within the Web3 integration and Blockchain: The Google Cloud effectively increased the steps related to the support of blockchain by collaborating with Web3 companies of Web3. This lacks the decentralised application capabilities in-house and the infrastructure of a robust blockchain. The missed opportunity within the digital ecosystem for the future development of learning for smart contracts, transparency and decentralisation. The gap involves the non-proprietary marketplace integration and blockchain technology. The investment is minimal within the tokenisation framework and decentralised applications. The rivals, such as IBM and Amazon, have effectively established blockchain solutions.

Risks of Data centralisation and user dependency: The entire model of Google's business effectively relies on the centralisation of data. With the developing popularity related to federated and edge computing learning, Google has developed but not implemented the processing of decentralised data at every level of the device. It showcases the significant strategic gap in which the privacy innovation first might be effectively scaled. The gap involves the effective dependency of the storage and central processing of cloud-based. The Edge computing underutilisation for the device level and real-time data processing. The vulnerability to legal regulations and cybersecurity threats.

Digital technologies propose and benefits: Edge AI deployment and federal learning: Federated learning is a technique of machine learning which permits the models of AI to remain trained within different decentralised servers and devices. This holds the samples of local data. The data of the user never leaves the devices, resolving the CCPA and GDPR concerns. This provides control and transparency over the processing of data. The AI model inference and training in real time on different devices, such as IoT devices and smartphones. Google has focused on learning within the Gboard keyboard of Android, but the implementation is broader within the Assistant, YouTube, Maps and Gmail, which might develop privacy-centric features while effectively keeping the customisation intact of AI.

Blockchain technology for security and transparency: The Blockchain is a ledger system that is decentralised, which ensures the exchange of trustless data and record-keeping that is tamper-proof (Elia et al, s2021). This might power the data management of secure users, transparency in the supply chain and digital contracts. This secures the data history and user identity, the payments based on the automated advertising, royalties within the Google Ads and YouTube, and content licensing. This develops the access of user systems by making use of the DID solutions (Decentralised Identity). It also effectively permits the systems based on tokens within the applications of the future Metaverse, YouTube and the Play Store. The blockchain incorporation for the user authentication, payments of content creators and transparency in ad auctions. This might future-proof the digital platforms of Google, build trust and decrease fraud.

Unified platforms of digital workspace (Developed Google Workspace): collaboration suite, AI-powered, integrates the analytics dashboards, task management (Trello and Asana) and communication (Chat/Meet). This developed the productivity through the task tracking summaries of automated meetings and document editing collaboration (Khin and Ho, 2019). This competes with the modular tools of Slack, meeting transcripts, language translation, and real-time. This develops the offering for consumers based on enterprise within the remote and hybrid work environments. By creating the Google workspace with practical features, Google might retain and attract business consumers, focusing on the ecosystem of digital collaboration all in one.

User data control hub and ethnic dashboard: The centralised dashboard is effective for the users; this view shows how the information is utilised. These AI systems focus on how decisions are developed based on the combined data, with customisation options and opt-out. The users might influence and view the decision-making algorithms. This focuses on the commitment of Google to transparency, accountability and fairness.

Task 3: Describing the barriers to the implementation of the digital technology/technologies identified in task 2

Infrastructure limitations and Technical complexity: The edge AI and federated learning Application showcases the significant technical challenges. The machine learning training models within the different devices need the developed synchronisation protocols, reliable connectivity and computational power. The other user devices cannot carry the battery life and processing capacity to offer support for developed tasks, which might lead to inconsistent performance.

Similarly, applying the technology of blockchain on a large scale includes the redesign of complex architectures. The traditional systems within Google are effectively optimised in changing the ledger distributed system, centralised processing of data will need changes within the system design, interoperability protocols and infrastructure. The transaction speed and scalability are effectively technical common hurdles within the environments of blockchain.

Legal and regulatory challenges: The digital technologies, such as federated learning and blockchain, pose compliance and legal issues. For example, federated learning processes private data locally. The laws of data residency within the different countries may require clarification regarding how information is utilised and stored (Appi et al, 2019). Moreover, the immutability of blockchain might conflict with the laws of data protection, such as the right to be effectively forgotten within the (GDPR) General Data Protection Regulations (GDPR) (General Data Protection Regulations, 2024). The AI ethics dashboard implementation needs transparency within the decision-making algorithm. On the other hand, the different models of AI develop the systems of learning. This functions as the boxes of a block, making it challenging to assess the outputs within the same terms.

Resource and cost constraints: Google has significant financial resources and scaling novel technologies and implementation requires substantial investment in skilled personnel, research and infrastructure. For instance, the edge computing needs the adaptations of the device-specific, which might boost the costs of development. The implementation of the blockchain might be intensive in resources, particularly at the time of focusing on the audit of smart contracts, consensus mechanisms and secure nodes.

Moreover, developing the workspace of Google to rival Slack and Microsoft teams will involve the regular investment in the developed features of AI, integration of cross-platforms and UX design.

Organisational and cultural resistance: The organisational change, within an organisation that is innovation-driven, such as Google, might face resistance internally. The accustomed teams to the centralised systems, workflows and existing tools might be disinclined to adopt the novel processes and also retain the technologies unfamiliar (Kelly et al, 2021). For instance, effectively deploying the dashboards of AI ethics may require shifts in how the teams of production monitor and develop the systems of AI.

There might be cultural resistance from the major users, who are effectively wary of federated AI and blockchain because of the misconceptions related to the security, energy consumption and complexity.

User experience and adoption issues: The novel technologies' success is heavily dependent on the acceptance of users. The federated and user learning might boost the transparency and privacy, but this compliance with the workflow and user interfaces cannot be adequately designed.

REFERENCES

Books and journals

• Appio, F.P., Lima, M. and Paroutis, S., 2019. Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Technological Forecasting and Social Change, 142, pp.1-14.
• Elia, A., Kamidelivand, M., Rogan, F. and Gallachóir, B.Ó., 2021. Impacts of innovation on renewable energy technology cost reductions. Renewable and Sustainable Energy Reviews, 138, p.110488.
• Fukuda, K., 2020. Science, technology and innovation ecosystem transformation toward society 5.0. International journal of production economics, 220, p.107460.
• Kelly, B., Papanikolaou, D., Seru, A. and Taddy, M., 2021. Measuring technological innovation over the long run. American Economic Review: Insights, 3(3), pp.303-320.
• Khin, S. and Ho, T.C., 2019. Digital technology, digital capability and organizational performance: A mediating role of digital innovation. International Journal of Innovation Science, 11(2), pp.177-195.
• Nguyen, T.T., Pham, T.A.T. and Tram, H.T.X., 2020. Role of information and communication technologies and innovation in driving carbon emissions and economic growth in selected G-20 countries. Journal of environmental management, 261, p.110162.
• Rammert, W., 2021. Technology and innovation. Soziologie-Sociology in the German-Speaking World, pp.515-534.
• Tidd, J., 2023. Managing innovation. IEEE Technology and Engineering Management Society Body of Knowledge (TEMSBOK), pp.95-108.