digital innovation Archives - rinf.tech https://www.rinf.tech/tag/digital-innovation/ Wed, 05 Jun 2024 13:19:43 +0000 en-US hourly 1 https://www.rinf.tech/wp-content/uploads/2020/05/favicon-150x150.png digital innovation Archives - rinf.tech https://www.rinf.tech/tag/digital-innovation/ 32 32 How to Roll Out an Innovation Project Within Your Company: Tips and Best Practices https://www.rinf.tech/how-to-roll-out-an-innovation-project-within-your-company-tips-and-best-practices/ Mon, 05 Feb 2024 10:40:59 +0000 https://www.rinf.tech/?p=17756 This article will share three strategies to help R&D teams effectively and successfully implement new technologies.

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How to Roll Out an Innovation Project Within Your Company: Tips and Best Practices

Innovation has always been a critical component of any organization's business success. But what is often less clear is how to translate new innovative ideas into practical implementation.

Sharing six strategies to help R&D teams effectively and successfully turn their innovative projects into full-fledged technology solutions accepted company-wide.

Contents

How to Transform an R&D Project into a Widely Accepted Technology Solution Across an Organization

To explore this question, Harvard Business Review analyzed several data sources from a major multinational oil company. It found that when the company adopted the cutting-edge technology that grew out of its R&D efforts, drilling costs were reduced by 15%, resulting in an average savings of $90 million a year per subsidiary. In an era of low oil prices, it brought about significant savings. 

However, despite this significant investment in innovation (as well as a clear indication that innovation pays off financially), HBR found that the company sometimes struggled to gain internal support for new technologies developed by its central R&D departments.

Our own analysis identified several common barriers and six strategies that have been particularly effective in encouraging the adoption of new tools and systems.

Here we go.

Strategies for Launching an Innovation R&D Project

1. Always Begin by Considering the Needs of the End User

Beginning with the end user in mind is a pivotal strategy for ensuring the successful implementation of innovative technologies within an organization. The common practice among many companies is to adopt a top-down approach, where new technologies are developed based on overarching strategic and financial goals, and then distributed across various departments and teams. While this method can sometimes yield results, it often encounters resistance from those on the front lines, such as engineers responsible for carrying out the implementation. These individuals may be hesitant to adopt directives from an R&D team located elsewhere, especially if the new technology has not yet proven its practical value in their specific work context.

A senior engineer highlighted this sentiment in an interview with HBR, noting, “Our operations departments don’t need new technology unless it’s proven to be absolutely useful.” This perspective is understandable, considering the high stakes involved with innovation projects. The risks and uncertainties associated with introducing new technologies can be significant, posing the potential to squander millions of dollars if the initiative fails.

However, adopting a bottom-up approach can effectively address these concerns. For instance, HBR recounted the experience of an engineer whose initial skepticism was overturned upon discovering a new system designed to mitigate water seepage in oil wells. This innovation, encountered during a visit to the headquarters, not only caught his interest but also led to its adoption despite initial resistance from his technology head. The remarkable success in reducing water seepage demonstrated the system’s value, convincing even the skeptical head of technology of its worth across the subsidiary.

The success of this innovation was not due to it being a simple “plug and play” solution. Instead, it required engineers to conduct extensive testing and modifications to tailor the technology to their specific operational needs. This example underscores the importance of engaging with the actual users of the technology from the outset. By directly involving those who will interact with the new system in their daily tasks, R&D teams can gain valuable insights into practical requirements, potential challenges, and the real-world impact of their innovations.

Convincing a few key individuals of the technology’s benefits can serve as a litmus test for its broader applicability. Although not every innovation will prove successful in every context, gaining traction within one segment of the organization can significantly bolster the case for a wider rollout. Therefore, it is crucial for R&D teams to not only focus on the technological aspects of their projects but also to invest time in understanding and addressing the needs, concerns, and realities of their end users. This user-centric approach can dramatically increase the likelihood of successful adoption and implementation across the entire organization, ensuring that innovative projects translate into tangible improvements and efficiencies.

2. Get Early Adopters Onboard

Securing the support of early adopters is a critical next step after initially focusing on end-user needs. To effectively drive the adoption of new technologies, R&D teams must identify which segments within the organization are most likely to embrace these innovations. Often, the best candidates are those who have faced persistent challenges or limitations with current systems and are therefore more open to exploring new solutions. For instance, engineers who have grappled with issues like water seepage for an extended period may be more inclined to welcome novel technologies that promise to address such problems.

Another approach involves targeting individuals or teams with a vested interest in the technology being developed. For example, when an R&D team is working on a digital upgrade for an essential analog control system, engaging with the operations team responsible for the original system can be highly beneficial. These teams are likely to possess deep insights into the system’s strengths and weaknesses, making them well-equipped to appreciate the advantages of the new digital solution.

The Harvard Business Review case study underscores the effectiveness of two strategic initiatives aimed at fostering closer ties between R&D teams and field engineers. Firstly, by identifying engineers with prior R&D experience, companies can create a bridge between current development efforts and practical field applications. Keeping these engineers informed about the latest technological advancements ensures that valuable insights from the field are incorporated into the R&D process, enhancing the relevance and applicability of new solutions.

Secondly, the establishment of R&D centers that facilitate movement of researchers across different divisions represents a forward-thinking approach to innovation. These centers serve as hubs of technological advancement, where R&D personnel can immerse themselves in the diverse operational contexts of the company. By working closely with field engineers in their natural environments, researchers can gain a firsthand understanding of specific challenges and requirements, enabling them to tailor their inventions more effectively to meet the needs of various teams.

Both strategies highlight the importance of creating a dynamic, interactive relationship between those developing new technologies and their eventual users. By doing so, R&D teams can ensure that their innovations are not only technically sound but also practically viable and directly responsive to the unique demands of their organization. Engaging early adopters in this way not only facilitates smoother implementation and integration of new technologies but also fosters a culture of innovation and collaboration throughout the company.

3. Navigate Financial Challenges

Navigating financial hurdles is often the final but significant obstacle that teams encounter when attempting to develop and deploy new systems. The dual requirements of software engineering expertise and considerable financial resources can appear daunting. However, these challenges are not necessarily prohibitive.

A wealth of inventive strategies exists for securing the necessary resources, whether through grants, external funding, or leveraging an internal incubator program. In the case of addressing water seepage, for instance, the R&D team successfully negotiated support by convincing an internal venture fund to finance the preliminary testing phase. This not only provided the essential capital but also demonstrated organizational faith in the project’s potential.

Furthermore, adopting a flexible approach to project management can offer a solution to financial and risk-related concerns. Our research indicates a natural hesitation among large project managers to introduce unproven technologies due to the perceived risk. An effective strategy to overcome this involves identifying smaller, less risky projects as initial testing grounds for new technologies. This allows for a proof of concept to be established with minimal financial risk. Once proven on a smaller scale, these innovations can then be more confidently rolled out to larger projects, equipped with tangible evidence of their effectiveness.

This strategic alignment of projects of varying scales creates a symbiotic relationship between different initiatives within the organization. Large projects benefit from minimized risk and the opportunity to implement proven technologies, while smaller projects gain access to cutting-edge solutions that would typically be beyond their budgetary reach. This not only mitigates potential financial losses but also fosters an environment of innovation and experimentation across the board.

It’s imperative for R&D departments to recognize that they may not always have the capacity to bear the full cost of introducing new technologies. Thus, exploring these creative financing solutions becomes essential. By doing so, they can engage and empower local champions within the organization who are key to the successful adoption and scaling of innovative technologies. These strategies not only circumvent the financial obstacle but also reinforce a culture of innovation, collaboration, and strategic risk-taking that can drive the organization forward.

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4. Foster Cross-Departmental Collaboration

Encouraging collaboration between different departments and teams can significantly enhance the adoption of new technologies. By fostering a culture of cross-departmental teamwork, R&D teams can leverage diverse perspectives, expertise, and insights, which can lead to more effective and universally accepted solutions. This strategy involves creating interdisciplinary teams that include members from R&D, operations, marketing, finance, and any other relevant department to ensure that the development and implementation of the technology consider all aspects of the business. Cross-departmental collaboration not only enriches the development process with a broader range of insights but also facilitates smoother integration of the technology into various areas of the organization, thereby enhancing overall buy-in.

5. Implement Agile Development and Iterative Feedback

Adopting an agile development approach can also play a crucial role in successfully rolling out new technologies. Agile methodologies prioritize flexibility, continuous improvement, and rapid iterations based on user feedback. By involving end-users and stakeholders in the development process through regular reviews and adjustments, R&D teams can ensure that the technology evolves in a way that closely aligns with user needs and organizational goals. This iterative process allows for the early identification and resolution of potential issues, making the final product more likely to be accepted and adopted across the organization. Additionally, this approach helps to build a sense of ownership and acceptance among users, as they see their feedback and suggestions being actively incorporated into the technology.

Both cross-departmental collaboration and agile development with iterative feedback are essential in bridging the gap between R&D projects and their successful adoption as technology solutions within an organization. These strategies complement the initial focus on end-user needs, engagement of early adopters, and overcoming financial obstacles by ensuring that the innovation process is inclusive, responsive, and aligned with the broader organizational context. By integrating these approaches into the innovation strategy, companies can significantly increase the likelihood that their R&D initiatives will result in impactful, widely accepted technological advancements.

6. Partner with a Technologically Advanced R&D Center

Seeking a partnership with an external R&D center that boasts advanced technological capabilities and expertise can offer another strategic avenue for accelerating the development and adoption of new technologies within an organization. This collaboration allows companies to tap into cutting-edge research, specialized knowledge, and innovative methodologies that may not be available in-house. By leveraging the resources and expertise of a tech partner, organizations can not only speed up the R&D process but also mitigate some of the financial and technical risks associated with developing new technologies from scratch.

A robust tech partner can provide access to a wide array of technologies, pilot projects, and prototypes, enabling an organization to explore and experiment with new solutions without the full commitment of internal resources. Furthermore, such a partnership can enhance the credibility of the technology being developed, making it easier to gain buy-in from internal stakeholders and early adopters. It also allows for the sharing of best practices, lessons learned, and market insights, which can refine the development process and ensure the technology is aligned with industry trends and customer expectations.

Incorporating a partnership with an external R&D center into the innovation strategy broadens the scope of possibilities for an organization, providing access to a richer pool of resources and expertise. This strategic move complements the internal efforts of engaging users, fostering cross-departmental collaboration, implementing agile development practices, and overcoming financial challenges. Together, these strategies form a comprehensive approach to transforming R&D projects into successful, widely accepted technology solutions across an organization, driving forward innovation and maintaining a competitive edge in the market.

An example of innovative solutions we build in our R&D Embedded Center

Wrapping up

In conclusion, transforming an R&D project into a technology solution that is embraced organization-wide involves a multifaceted approach that begins with a deep understanding of end-user needs and extends to strategic partnerships and agile development methodologies. The strategies outlined collectively form a robust framework for innovation.

The journey from R&D to widespread technological adoption within an organization is complex and fraught with challenges, from resistance to change among front-line users to financial and operational barriers. However, by adopting a user-centric approach, engaging early adopters, creatively overcoming financial hurdles, encouraging collaboration across departments, iterating based on feedback, and leveraging external expertise, organizations can significantly increase the odds of success. These strategies not only ensure that new technologies are developed with a clear understanding of real-world applications but also foster an environment where innovation is nurtured, supported, and effectively integrated into daily operations.

The case study from the Harvard Business Review and the strategies derived from it underscore the importance of a holistic and adaptable approach to innovation. By viewing each R&D project through the lens of these strategic pillars, organizations can navigate the complexities of technological adoption, ensuring that innovative solutions not only see the light of day but also deliver tangible benefits across the entire organization. Ultimately, the goal is to transform innovative ideas into practical, effective solutions that drive organizational success, enhance efficiency, and maintain a competitive edge in an ever-evolving technological landscape.

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]]> Machine Learning Project Ideas for Different Industries https://www.rinf.tech/machine-learning-project-ideas-for-different-industries/ Wed, 24 May 2023 14:17:41 +0000 https://www.rinf.tech/?p=23520 This article explores interesting machine learning projects that companies in various sectors can launch to gain a significant competitive advantage in a highly saturated business landscape.

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Machine Learning Project Ideas for Different Industries

Machine learning, or ML, is an artificial intelligence capability that teaches computers to learn information without human intervention or specific programming. ML has been one of the most exciting technologies in the last few years and has infiltrated almost every major industry in the world.

The forecast for the global ML market shows a value of $302.62 billion by 2030 at a compound annual growth rate of 38.1% since 2021. The value and potential of ML as a defining technology were illustrated when it became one of the few markets that weren’t devastated by the COVID-19 pandemic. The ML market was and continues to be a force to reckon with.

ML use cases across industries can vary greatly. Identifying a sector that wouldn’t benefit from machine learning projects would be difficult. However, some sectors would certainly benefit more than others. They include retail, transportation, delivery and supply chain, semiconductor, and hardware.

This article explores interesting machine learning projects that companies in various sectors can launch to gain a significant competitive advantage in a highly saturated business landscape. The slightest advantage via efficient and streamlined operations can nudge a company ahead of its competitors, and certain ML ideas can provide a massive headstart.

The best machine learning ideas can benefit organizations in the abovementioned industries, including higher cost-savings, increased revenue, greater operational efficiency, better safety, security, and compliance, optimized workforce and resource utilization, and improved human and machine behavior pattern recognition accuracy. Let’s explore some ML project ideas for different industries.

Contents

Machine Learning Use in the Retail Industry

The retail industry has tremendous potential to benefit from advanced ML. ML has already made significant inroads in retail. The best ML use in retail includes cutting-edge recommendation engines, fraud detection, virtual assistants and chatbots, highly personalized targeted ads, and video surveillance.

ML in video surveillance for retail environments has exciting possibilities, and that’s best exhibited in rinf.tech’s Vid.Supervisor machine learning model. Vid.Supervisor runs over videos in retail stores to identify and tag human behaviors without breaching their confidentiality—the core features of Vid.Supervisor include people detection, facial recognition, and automatic video creation.

rinf.tech’s AR (augmented reality) shopping app is another example of ML potential in retail. The innovation was in the form of a mobile application that helped customers identify products in a store and automatically add them to a digital shopping cart or bookmark them to be shared later. This project is a reminder that ultramodern technologies like ML can augment and co-exist with traditional in-store shopping experiences that many global consumers still prefer.  

Machine Learning Use in the Transportation Industry

Tremendous ML activity and advancements are occurring in transportation sectors around the world. Businesses are facing significant transportation-related challenges that could result in financial setbacks if left unaddressed. These challenges include centralized management of large fleets, avoiding empty miles, and ever-increasing costs of labor, fuel, equipment, and vehicles.

ML use in transportation can provide several benefits via route optimization, predictive fleet maintenance, computer vision-based smart parking solutions, self-driving vehicles, pedestrian detection, and traffic management.

rinf.tech developed Cloud CCTV Services for trains. Developed for AdComms, a key supplier across the rail industry in the UK, this project was a response to AdComms’ request for an enterprise-grade Cloud solution to facilitate remote access to CCTV video footage on trains in the UK.

We at rinf.tech responded with an ML-powered four-pronged solution that comprised on-train Windows services, cloud services, a web application, and an SPSO-driver application. This highly lauded and awarded CCTV solution is currently running on UK trains, a monument to the potential of ML use in transportation.

Machine Learning Use in the Delivery and Supply Chain Industry

The delivery and supply chain industry, one of the major engines of the world’s economic growth, faces a series of challenges, including labor shortages, material scarcity, equipment availability, forecasting complexities, port congestion, energy scarcity, and repercussions of various global bottlenecks. The practical application of ML can solve many of these challenges and shape a new delivery and supply chain management era.

Some examples of ML solutions that can transform delivery and supply chain activities across the globe include robust automated warehouse management, advanced IoT (internet-of-things) machines and vehicles, robotic process automation, computer vision-powered smart surveillance systems, anomaly detection, and highly accurate demand forecasting.

Digital twin modeling is an ML-powered technology that allows companies to recreate a digital version of a physical object or environment with real-time behavior synchronization via sensors. Creating digital twin supply chains can help companies experiment with different workflows and pipelines and try to optimize certain aspects of a supply chain process. Digital twins in the supply chain sector can help organizations improve visibility, assess risks associated with a variety of potential decisions, and simulate disasters to test out remediation playbooks.

Machine Learning Use in the Semiconductor Industry

McKinsey refers to the current decade as the “semiconductor decade.” The semiconductor industry is a global machine that’s set to become worth a trillion dollars by 2030. ML use in the semiconductor space can further drive that growth.

Semiconductors become smaller and smaller with every passing year. At the same time, companies in the semiconductor industry have mountains of data documenting the entire semiconductor technology lineage. This data can be leveraged with ML to discover new semiconductor materials and innovate ways to transform semiconductor manufacturing.

Other ideas for ML application in the semiconductor industry include smart neuromorphic chips, automated hardware design, fault detection via nano-scale image generation, streamlined manufacturing pipelines, and automated testing.   

Companies are in a neck-and-neck race in this highly lucrative sector. Machine learning capabilities can provide some of the most promising companies with the deserved boost they need to get ahead of the rest and further revolutionize the industry.

Conclusion

Machine Learning is taking over the world. Some industries, in particular, are set to benefit greatly from the constantly-advancing powers of ML. The retail, transportation, supply chain, and semiconductor industries stand out as high-potential beneficiaries. These industries have already undergone profound change due to ML. However, these changes will likely be small stepping stones that lead to an ML-powered smart future.

ML is also rapidly transforming leading industries like healthcare, education, and energy. ML is evolving so quickly that it’s difficult to predict what effect this soon-to-be trillion-dollar industry could have on our world. One thing is certain: most modern companies, however big or small, can benefit from ML. As with most technologies, the key to success is strategic and responsible application and collaboration with experts.

Experts like rinf.tech can help modern businesses deal with vast volumes of data efficiently and effectively. That data is of no use if it can’t be mined for business insights at sub-second speeds. Our experts can help a diverse range of enterprises unlock the potential of ML and improve the quality of business insights, and optimize performance, costs, and operations.

Contact us now to learn how to do a machine-learning project to solve your most pressing business challenges.

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]]> Top Use Cases of ChatGPT Integration into Business Strategies and Operations https://www.rinf.tech/top-use-cases-of-chatgpt-integration-into-business-strategies-and-operations/ Wed, 19 Apr 2023 12:28:15 +0000 https://www.rinf.tech/?p=22988 This article explores top use cases of integrating ChatGPT into business strategies and operations across multiple industries and domains.

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Top Use Cases of ChatGPT Integration into Business Strategies and Operations

ChatGPT is taking the world by storm. Developed by OpenAI, an American Artificial Intelligence (AI) research lab, ChatGPT was unleashed to the world in November 2022. It’s an AI chatbot that’s built with the powers of Reinforcement Learning from Human Feedback (RLHF). ChatGPT can interact with users in a way that reflects advanced human conversation. Since November 2022, ChatGPT has been the subject of conversations and arguments from coffeehouses to global conferences.

It isn’t merely anecdotal evidence that suggests that ChatGPT is dominating the world. A UBS study (based on Similarweb data) proved that ChatGPT was the fastest-growing app on the planet, with more than 25 million daily visitors a day. Website traffic has been rising at an average of 3.4% per day. February 2023 saw more than 1 billion visits. These visits include casual and curious internet wanderers as well as strategy-led business teams and professionals.

ChatGPT has also rapidly become a controversial app. It was recently banned in Italy. Everyone from tech leaders to artists has commented on the risks of such apps. But one thing is for certain. ChatGPT is here to stay, as are a whole host of competitors. The revenue forecast for the global chatbot market shows a figure of $27,297 million in 2030, at a compound annual growth rate of 23.3% since 2023.

Retail and e-commerce are the end-user segment that is currently benefiting the most from chatbots. Customer service is the most common application. That being said, ChatGPT has already started to get integrated into businesses across a plethora of diverse industries. Use cases have been unique and unexpected. Companies are seeing measurable results with their applications of ChatGPT.

While debates about the authenticity and ethics of ChatGPT will continue, business leaders and entrepreneurs need to accept that ChatGPT is a vital and viable business tool for them to leverage and build solutions on top of. And now is the best time to do so.

In this article, we explore top use cases of integrating ChatGPT into business strategies and operations across multiple industries and domains.

Contents

Which Industries Are Using ChatGPT?

Before delving into the top use cases of ChatGPT integration into business strategies and operations, it’s important to get a high-level understanding of the kinds of industries that have started using the capabilities of this advanced chatbot. ChatGPT is sure to make encroachments into every industry in the future. For now, a few major industries have been early adopters.

IT industry is a rather obvious start to the list. There is a cloud of uncertainty that’s hovering over the IT and tech sector concerning ChatGPT. Rather than get caught up in whether ChatGPT is a blessing or a disaster for certain industries, IT leaders should accept its position as a disruptor and strategize ways to work with it.

The education and healthcare industries are both experiencing the integration of ChatGPT. The public narratives around ChatGPT usage in these sectors are dramatic, to say the least. Universities and colleges have no option but to adjust the positives of ChatGPT and build fortifications to avoid their students using ChatGPT in impermissible ways. Concepts like ethical research and plagiarism are being re-explored and re-articulated.

Healthcare, on the other hand, is being revolutionized by AI adoption, both on the patients’ front as well as for healthcare providers. Tools like ChatGPT are being applied in the healthcare industry by numerous stakeholders involved in primary, secondary, and tertiary healthcare. These include patients, caregivers, doctors, administrators, and health sector business leaders.

The media and entertainment industry is rampant with ChatGPT use and so is the marketing industry. Any sector that requires high-quality content generation is honing its ChatGPT query skills to generate content that’s fueled by the deepest archives of literature, film, music, art, and media that ChatGPT has mined and internalized.

Besides IT, healthcare, education, media, and marketing, ChatGPT is also enriching companies in manufacturing, tourism, publishing, and law. Governments and politicians have begun using ChatGPT, a phenomenon on display when Isaac Herzog, the President of Israel, delivered a ChatGPT-written speech in 2023. NGOs and non-profits can also benefit from the chatbot. Across industries, there are diverse, innovative, and groundbreaking use cases of ChatGPT.

Top 7 Business Use Cases of ChatGPT

Coding/Software Development

It may first appear that coding is quite a specialized IT-related application of ChatGPT. However, what ChatGPT offers businesses are the opportunity and tools to empower members of their organization with coding skills. ChatGPT can assist with writing software code, completing incomplete fragments of code, identifying coding mistakes, optimizing code, and even testing its efficiency. ChatGPT is also simultaneously an encyclopedia of coding history, methods, and intricacies.

No-code and low-code development platforms have been utilized by entrepreneurs, startups, small businesses, and those who can’t afford top-of-the-line IT teams and experts.

Now, with ChatGPT, entrepreneurs, and leaders, to a fair extent, can integrate coding into their business operations and strategy. This opens up a whole new array of options for business leaders to explore. With ChatGPT, the aperture of innovation opens up significantly.

Content Creation

“Content” has been the buzzword for a few years. Most companies have to generate high-quality and engaging content to keep an active conversation going with their audience and customers. Content creation is often the centerpiece of a marketing strategy and ChatGPT is starting to allow businesses to elevate and scale their content creation at significantly lower costs. AI-generated content is quickly becoming the go-to plan for marketing leaders.

Since ChatGPT specializes in text-based content, it’s a perfect solution for screenplays, loglines, and synopses for visual media, as well as written content marketing like blogs, think pieces, whitepapers, emails, and social media posts. ChatGPT is a robust tool in the hands of marketing leaders because it makes businesses capable of generating high volumes of SEO-optimized content at high speeds, without the expenses of hiring or outsourcing writers and creatives. 

Customer Service

As mentioned above, customer service is the dominant application for chatbots like ChatGPT. While other use cases arise, customer service will remain a hotspot for ChatGPT usage. Companies can train and leverage ChatGPT to quickly respond to customer inquiries, complaints, and comments. This can be a luxury for micro-businesses, sole proprietors, or businesses in an early stage that don’t have budgets for 24/7 customer care.  

In addition to directly responding to messages from customers, ChatGPT, as it continues to develop, can keep conversations active, find out information from customers, and direct them to an array of products and services that they might not have been aware of before. Business leaders need to remember that replacing their customer service representatives with ChatGPT might be rash. It would be wiser to empower their existing agents with tools like ChatGPT.

Cloud Navigation

Numerous companies from various industries are leaving the old pastures of on-premise data centers and moving to cloud-based IT infrastructures. Most new companies go straight to the cloud. The cloud is widely acknowledged to be a more optimal modern solution. It can potentially be cheaper, easier to use, and yield better results for businesses. However, not all businesses are familiar with the technicalities of cloud computing and related infrastructure.

ChatGPT can educate entrepreneurs and business leaders about cloud-based infrastructure, solutions, providers, tools, and tactics. It can provide a list of best practices for cloud adoption and empower businesses with a solid roadmap (or multiple roadmaps for them to choose from). ChatGPT is a useful tool to simulate variations of cloud infrastructures to explore which might work best for a particular business and its specific needs, objectives, and resources.

Competitor Analysis

An interesting and incredibly useful application of ChatGPT is to conduct detailed competitor analysis. This kind of analysis can be done by established companies who want to scan their competitors’ activities as well as new entrants who wish to explore a market to identify and carve out a niche for themselves within it. ChatGPT can provide senior leadership with deep insights about competitors that can enhance decision-making and influence strategic pivots.

Some examples of the information that business leaders can mine out of ChatGPT include comprehensive lists of competitors, their products and services, testimonials, complaints, marketing and advertising strategies, and pricing plans. This kind of constant and contextualized surveillance of competition can help businesses constantly refine their offerings to remain fresh, unique, and of value. ChatGPT can be used to sharpen differentiators. 

Compliance

Businesses have to abide by ever-evolving regulations. These regulations can be specific to industries, governments, or geographies. It’s a lot for businesses to stay on top of, especially those without massive portions of their budget to spare. ChatGPT isn’t advanced enough yet to provide flawless details on regulatory compliance. Companies would be taking a massive gamble by following the compliance guidance of ChatGPT.

What ChatGPT can offer businesses and their leaders regarding compliance is a rich platter of introductory information. Businesses will have to fact-check their learnings but ChatGPT can be a useful tool to explore a broad and complex canvas in a relatively short period. While ChatGPT usage to stay compliant doesn’t seem quite as exciting as other use cases just yet, future iterations of the chatbot could offer more for compliance than just being a search engine.

Cybersecurity

Malicious attackers from all around the world have begun to use ChatGPT to identify vulnerabilities in the infrastructure of their targets and even create malware. This, rightly, should make companies concerned. However, there are certain cybersecurity capabilities that ChatGPT could be trained on to become a counterforce to these malicious hackers. Firstly, it can provide broad research about cybersecurity best practices, tools, and protocols.

ChatGPT is an imperfect but dedicated student. It’s capable of internalizing archival security documents, incident response reports, and disaster recovery plans to become proficient at generating or designing similar documents for new security incidents. While there’s still a lot to be discovered on how ChatGPT can aid cybersecurity, it’s a great low-cost resource for entrepreneurs and businesses to stay educated about threats and optimal defense.

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How Businesses Actually Use ChatGPT: Real-World Examples

Morgan Stanley

Morgan Stanley, a leader in wealth management, faced a challenge in processing a large amount of data related to investment research reports, financial documents, and news articles. The challenge was to efficiently analyze and extract relevant information from this data to inform investment decisions for their clients.

Solution/tech stack and implementation details:

  • Use of GPT-3 and GPT-4 to power an internal facing chatbot to perform a comprehensive search through wealth management content, allowing advisors to parse insights into a more usable and actionable format for their clients

Outcome and results:

  • Improved efficiency in analyzing and extracting insights from large volumes of data.
  • Reduced time to read and analyze research reports and content repo.
  • Improved accuracy of data analysis, allowing advisors to accurately assist more clients, more quickly

 

Read full case story.

Yabble

Yabble is a market research platform that provides insights into consumer behavior and preferences by allowing analysis of thousands of consumer data points shared through surveys or customer feedback forms. However, developing actionable insights from their customers’ data proved highly time-consuming due to the manual labor required.

The solution/ tech stack and implementation details:

  • Use of GPT-3 to rapidly transform complex, unstructured data into relevant themes and subthemes.
  • Use of GPT-3 to better understand and process more complex user questions and respond with insights based on relevant data.

 

Outcome and results:

  • Using GPT-3, data sets that would usually take Yabble teams days to code and develop insights for, were now being translated into meaningful themes in minutes.
  • Increase in complexity of data analytics and insights.

 

Read full case story.

Waymark

Waymark is a video creation platform that helps businesses of all sizes to easily create video content – TV commercials and digital video ads. However, their customers faced a challenge in terms of video scriptwriting and the platform’s suggestions often proved too vague for many businesses.

The solution/ tech stack and implementation details:

  • Use of GPT-3 models to generate custom video scripts for business in seconds.

 

Outcome and results:

  • Waymark reduced the time required to create a video, significantly reducing the cost of video creation, allowing faster video script personalization for companies that work with lots of local businesses.

 

Read full case story.

Stripe

Stripe is an online payment processing platform that serves millions of businesses globally, and was looking for ways to streamline its activity with the power of GPT-4.

The solution/ tech stack and implementation details:

  • Stripe had previously been using GPT-3 to help their support team better serve users through tasks like routing issue tickets and summarizing a user’s question
  • Stripe decided to test GPT-4 to improve support customization, answering questions about support, and fraud detection.
    • Use of GPT-4 to scan websites, deliver a summary, and better understand how each business uses the platform – to customize support accordingly
    • Use of GPT-4 to answer support questions – can understand user questions, read detail documentation, identify the relevant section and summarize the solution
    • Use of GPT-4 for fraud detection – analyzing syntax of posts in Discord and flagging accounts, help scan inbound communication, identify coordinated activity from malicious actors.

 

Future applications of GPT-4 for implementation:

  • Use of GPT-4 as a business coach that can understand revenue models or advise businesses on strategies

 

Read full case story.

Government of Iceland

The Icelandic government partnered with OpenAI to use GPT-4 in the preservation effort of the Icelandic language.

The solution/ tech stack and implementation details:

  • Training GPT-4 on proper Icelandic grammar and cultural knowledge to produce cultural fit prompts and clear Icelandic translations

 

Future applications of GPT-4 for implementation:

  • To enable Icelandic companies to soon deploy GPT-4 in Icelandic in their interactive applications.
  • To enable voice assistant apps to have conversations with users in fluent Icelandic, as well as offering translations to other languages.
  • Enable Icelandic companies to have Icelandic speaking chatbots on their websites

 

Read full case story.

Conclusion

ChatGPT is a disruptor. Disruptors shake up the status quo and unravel a plethora of suboptimal elements that could be improved upon. That’s what ChatGPT has done for numerous industries. While there are legitimate concerns about ChatGPT, it’s also evident that business leaders and entrepreneurs who don’t view it as a useful tool risk the possibility of falling behind in an incredibly saturated and competitive global market.

ChatGPT’s use cases are wide and varied. The 7 examples provided in this article are just an introduction to the world of generative AI applications like ChatGPT. The most innovative entrepreneurs should find ways to construct on top of the capabilities of ChatGPT. It can be a wonderful tool to identify a space in the market, optimize internal processes, drive business development and growth, and improve customer experience and loyalty.

Aligning with ChatGPT can be a transformative decision. However, it requires intricate work and a strategy of its own. Entrepreneurs and leaders should consider working with tech experts like Rinf Tech to maximize the potential of their offerings with the support of tools such as ChatGPT. The most innovative and successful of those leaders could write the next chapter of the leveraging of generative AI tech in business.

On March 14, 2023, OpenAI released ChatGPT-4, their most advanced system yet. Business leaders need to view this new launch as the perfect opportunity to begin their collaborative journey with ChatGPT. The outcomes could be profound.

Why build your NLP business solution with rinf.tech?

Networks, tools & frameworks

  • VGG 
  • ResNet50,ResNet101, ResNext 
  • MobileNet 
  • CNN, R-CNN, Mask-R-CNN 
  • YoloV3 
  • RetinaNet 
  • U-Net 
  • HRNet 
  • DeepLabv3 
  • LSTM 
  • AzureCognitive Services 
  • OpenVino 
  • ONNX, PyTorch, Tensorflow, Keras, NumPy
  • Vertex AI

Our capabilities per industries

  • Retail
    • Price Detector – Segmentation and Digit OCR models
  • Transportation
    • Passenger counter
    • Opened doors detection 
    • Vandalism detection
  • Delivery/Supply chain
    • Long Short-Term Memory model implementation for Address Parsing
  • Semiconductor/HW
    • Neural networks optimization through quantization and pruning
    • Research to select the most suitable ML solutions for various use cases
    • Object detection and Image classification  for various use cases such as Face Detection for Smart Homes
    • Behavior/productivity metrics 
    • ML Quantization 
       

Your benefits from partnership with us

  • Higher automation and efficiency of processes and operations 
  • Cost savings and elimination of overheads 
  • New use cases across different industries 
  • New business value 
  • Higher safety and security for the people  
  • Higher accuracy in human and machine behavior patterns recognition 
  • Less workforce and fewer resources required to execute a project

Our Next Research and Automation project: Automation of Incident Management Systems 

Are you looking to reduce your workload for Level 1 and Level 2 support?  

We can help you build a PoC project using Google Vertex AI and other state-of-the-art models. 

Contact us

Looking for a technology partner?

Let’s talk.

Related Articles

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]]> rinf.tech obtains Innovation Tax Credit (d’impot innovation CII) for French companies https://www.rinf.tech/rinf-tech-obtains-innovation-tax-credit-cii-for-french-companies/ Wed, 22 Dec 2021 12:13:00 +0000 https://www.rinftech.com/?p=6300 CII Agreement allows French SMBs to get a 20% reimbursement on custom software and robotic solutions development.

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rinf.tech obtains Innovation Tax Credit (d’impot innovation CII) for French companies

CII Agreement allows French SMBs to get a 20% reimbursement on custom software and robotic solutions development.

French Ministry of Economy and Finance recognizes rinf.tech as a top innovation partner for companies in France.

By obtaining CII Agreement, we've proven to possess technical and human capabilities required to develop innovative solutions and help French companies adopt new disruptive technologies for their own and end-user needs.

For more than 10 years we at rinf.tech have been working alongside French organizations across different industries and domains helping them gain a competitive advantage on the market, automate and optimize their processes, improve customer services, and better reach their business goals through innovation. 

The fact we’ve been granted the Innovation Tax Credit means more convenience, better cost optimization, and fewer risks for our current and future French customers.

All of our partners in France who choose to collaborate with us for designing and building custom software prototypes, and new tech products and services are eligible for a 20% reimbursement from the French government.

The Innovation Tax Credit (d’impot innovation CII) is a tax measure reserved for SMEs. The latter can benefit from a tax credit of 20% of the expenses necessary for the design and development of prototypes or pilot installations of a new software product. The base is capped at €400,000 per year and per company. The declaration is made using the same Cerfa N ° 2069-A-SD form and under the same conditions as the Research Tax Credit (CIR). Under certain conditions, SMEs can benefit from the early repayment of their ITC.

"This agreement is the result of many years of work and commitment to being on top of the IT industry. The financial structure of this deal creates the foundation for a win-win collaboration, giving our partners the opportunity to grow and innovate quickly in a fast-paced market, reducing the cost of technology development. For us, this is a chance to showcase our full potential and grow our knowledge and our presence on the French market."

Victor Dornescu, Chief Innovation Officer and Co-Founder, rinf.tech

The CII agreement will be in place for the next 4 years.

To learn more about CII Agreement and check whether your solution is eligible, please go to the website of the French Ministry of Economy & Finance.

Looking for a technology partner?

Let’s talk.

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]]> How Tech Partnerships Help SMBs Stay Ahead of the Innovation Curve https://www.rinf.tech/how-tech-partnerships-help-smbs-stay-ahead-of-the-innovation-curve/ Fri, 03 Dec 2021 16:42:48 +0000 https://www.rinf.tech/?p=15769 This article addresses how tech partnerships help small and mid-sized companies embark on custom software development journeys and adopt innovation efficiently.

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How Tech Partnerships Help SMBs Stay Ahead of the Innovation Curve

As the world slowly starts waking up after endless months of the pandemic, you have to wonder, how many made it? How did businesses survive this unprecedented event in human history? How were small and medium-sized businesses (SMBs) able to persevere?

Long answer short, many didn’t make it. The businesses that did, for the most part, ensured business continuity and resilience due to technology innovation.

Contents

How Tech Partnerships Provide a Competitive Advantage

In spring 2020, digitally transformed businesses quickly adapted and were able to “keep calm and carry on.” For example, the SMBs who were already on the Cloud and leveraging cutting-edge technologies were able to run their operation remotely.

Others were probably some of the hundreds of thousands of businesses that shut their doors for good (and we’re still feeling the repercussions today).

Naturally, enterprises that previously failed to jump on the digital transformation wagon rushed to move up to the Cloud. According to a recent study, as much as 45% of SMBs surveyed were thinking about digital transformation. Another 28% had already started digitizing their processes and operations.

But at the same time, almost half stated that the steep learning curve continued to be a massive barrier to adoption. Although this can be pretty overwhelming, it doesn’t have to be. The good news is that solid tech partnerships can help SMBs stay a step ahead in the innovation curve.

The ability of SMBs to identify and collaborate with technology partners is a competitive advantage (on its own). This is especially true when creating new growth business models and, of course, creating robust digital workspaces where the job gets done.

How Tech Partnerships Make a Difference When It Comes To Corporate Innovation

Technology Partners Compliment Your Goals and Values

Successful partnerships often come down to shared common characteristics, values, and desires that allow both to be successful, topical, and ever-evolving. Together, you can strategize, plan, and focus on your immediate and long-term objectives.

SMBs should also have an in-depth understanding of unit economics, natural churn, and so on. For example, they must decide between higher customer retention costs versus the lower cost of acquisition. This is especially true if you’re building applications for your target market.

Tech Partners Help Identify Your Pertinent Problems and Solve Them Effectively

Thriving ecosystems depend on an in-depth understanding of the business model, customer base, and potential problems and barriers. One of the most common problems small and mid-sized businesses are facing today is security, and a tech partner can help solve that problem.

In a rapidly evolving threat landscape, off-the-shelf security solutions alone can’t help fortify your infrastructure. Sometimes, you might need the help of smart algorithms and security specialists you couldn’t usually afford, and your tech partner will be able to fill those gaps.

So, if you have customer-facing or internal enterprise apps, it’s critical to secure them to ensure privacy, regulatory compliance, and of course, keep your brand name out of the headlines.

Although corporate giants can weather the storm that comes after a data breach, for many SMBs, a single data breach can annihilate the whole business.

Tech Partners Provide Access to Top Tech Talent and Technologies

Collaboration with your development partner at any level enables access to external expert knowledge and experience. It also provides access to cutting-edge tools and technologies that can propel your company to the next level.

SMBs also get to benefit from extensive experience and the latest technologies without all the HR headaches or overheads that go with it. As you can count on your tech partner’s in-house experts and extensive networks, SMBs can accelerate innovation significantly.

The added advantage of collaborating is learning from your partner’s experience working on similar projects. They will be able to guide you and alert you to potential hurdles that lie ahead, and find innovative ways to avoid them. This means that small businesses won’t have to waste their time trying to get around issues that already have robust solutions.

Established development partners also enable access to the latest and emerging technologies, including next-generation digital products and services—this provides SMBs with the necessary tools to compete in the marketplace with corporate giants.

In this case, SMBs can become far more agile than their corporate competitors. Tech partnerships also empower smaller players to take calculated risks and disrupt the marketplace confidently. However, it’s crucial to always focus on the process instead of potential results.

As things often change during the software development lifecycle (SDLC), SMBs must be open and flexible enough to adapt to their partner’s expert advice. While this might mean that you end up something completely different at the end of the project, it also means that the company was agile enough to be successful.

 

Tech Partners Help Optimize Resources & Save Costs

Successful partnerships open the doors to endless opportunities. Although you may have started with one project, you can always extend your collaboration when you begin identifying new prospects to scale and grow your business.

Most small businesses don’t have the necessary resources to attract top tech talent or the latest technologies. But when SMBs and technology partners pool their resources together, the potential of achieving meaningful is significant.

SMBs can also benefit from their partner’s extensive network to achieve meaningful growth and gain a competitive advantage. If you do it correctly, small businesses can sustain and scale their offering without significant capital expenditure (for example, hardware and software investments, infrastructure, new hires, etc.).

Whenever you augment in-house teams with external developers, you also help your staff improve. For example, your development partner can train in-house staff to get them up to speed or even teach them something completely new.

As you’ve probably guessed, all the above leads to significant cost savings. This is especially true if you’re outsourcing your software development project to Eastern Europe. For example, you can work with an Extended Team of top tech talent based in Romania and Ukraine at a cost that’s significantly lower than your local market rates. As you’re paying the local market rate in those countries, it’s a win-win situation for everyone involved.

If you’re wondering if you need a technology partner, ask yourself this question: are you ready for the fourth industrial revolution? Web 3.0 is here, and the wheels are already turning with smart sensors, cryptocurrencies, and of course, the metaverse.

In Summary

If you’re wondering if you need a technology partner, ask yourself this question: are we ready for the fourth industrial revolution? Web 3.0 is here, and the wheels are already turning with smart sensors, cryptocurrencies, and of course, the metaverse.

Will you be able to compete? Will you be able to stand out and stand apart in an increasingly crowded marketplace?

If your answer is no, a tech partner might be the right path to ensure business relevance. After all, working with a technology partner is a cost-effective approach to accelerate time to market, which is imperative to stay ahead of the curve.

Looking for a technology partner?

Let’s talk.

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]]> How to Capture Industrial IoT (IIoT) Value Through R&D Innovation https://www.rinf.tech/how-to-capture-industrial-iot-iiot-value-through-rd-innovation/ Wed, 17 Nov 2021 17:17:23 +0000 https://www.rinf.tech/?p=15503 This article focuses on the key technological changes enterprises need to make in order to support their innovation goals and how R&D can be an effective solution and a driver of IIoT success and sustainability.

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How to Capture Industrial IoT (IIoT) Value Through R&D Innovation

A guide for manufacturing enterprises looking to accelerate the pace of innovation

Innovations in the industrial software stack and applications for advanced analytics, artificial intelligence, machine learning, 5G connectivity, edge computing, and the Industrial Internet of Things (IIoT) are potentially valuable business assets.

However, for many manufacturing companies and businesses, "technical screening" can be the easy part. Capturing value and creating and scaling effective use cases is where the challenge lies.

Contents

Use cases need to be digitally supported, but heterogeneous systems and application landscapes can become obstacles due to outdated software and/or decentralized solutions and technologies. These technical problems, combined with unclear business plan hurdles and lack of organizational capacity, end up driving many companies into a constant pilot purgatory.

In recent years, there have been significant advances in technology, especially in scalable connectivity and integration, which finally allowed enterprises to upgrade and extend their existing solutions rather than fully replace them. From 5G to AI to edge computing and beyond – the emerging technologies enable companies to implement and scale efficient use cases with minimal additional cost when used wisely.

Over the years of creating and delivering high-performance, customized IoT solutions, we at rinf.tech have noticed that manufacturing companies with a holistic approach are the most successful ones. In particular, they benefit from the digitization of industry along the entire value chain, providing a range of advantages to both their suppliers and customers.

This article focuses on the key technological changes enterprises need to make in order to support their innovation goals and how R&D can be an effective driver of IIoT success and sustainability.

What does it take to deliver IIoT success?

Reaping the benefits of IIoT requires a holistic approach to drive the end-to-end transformation of an organization and technology.

To successfully develop IIoT projects, enterprises must focus on three core pillars:

  • IIoT Infrastructure: Plan and design an IIoT architecture to scale digital use cases across an organization.
  • Data Infrastructure: Get the right data at the right time and in the right quality for digital and analytics.
  • Technology ecosystem: Expanding a network of companies to collaborate with to bring innovation to the enterprise faster and more effectively.

 

So why is it essential for enterprises, especially manufacturing, to use IIoT and advanced technologies for digital transformation projects?

There are several good reasons why companies should start or continue to use IIoT and advanced technologies:

  • Deployment of scalable use cases is accelerating as platforms become more user-friendly. That is, from low-code to code-free software, toolkits make development and installation more cost-effective than ever.
  • Decentralized computing is becoming more popular as infrastructure solutions make it easy to manage distributed networks of platform resources and solve the problem in real-time.
  • Integration and connectivity are being dramatically improved by frameworks such as the unified architecture of Open Platform Communications (OPC) and the emergence of 5G, offering high-speed, secure and highly flexible low-latency solutions where existing alternatives don’t work.
  • Computing power and processing power have grown exponentially, while storage and CPU costs have plummeted.

 

The benefits of IIoT are significant

To name just a few:

  • Tremendous improvements in performance, productivity, resilience, flexibility, speed to market and customization can be achieved through proper IIoT implementation.
  • Moving to advanced manufacturing helps significantly increase the resilience of manufacturers, allowing them to respond faster to crises with modern digital work planning tools and solutions.

Successful implementation of IIoT at scale follows key actions in three areas:

  • On the business side

First, companies need to identify, prioritize, and test new use cases. They then need to create an achievable roadmap for deploying these use cases across IT, OT, and all enterprise/plant locations. After that, it would be great to capture value and measure opportunities with overarching impact and without distracting attention to local requirements for solutions. However, in terms of continually improving local initiatives, they need to be monitored and continued.

  • On the company side

Organizations need to set clear targets for the entire digital transformation and create a team/unit responsible for monitoring progress and adjusting courses when needed. Then a new way of working should be introduced that fosters cross-functional communication and develops related skills and capabilities.

  • On the technology side

First, enterprises need to define the current situation and target architecture of their IIoT platform, focusing on collecting, connecting, ingesting, and integrating data in ways that enable innovative use cases, including platform cybersecurity management.

Second, you need to understand the impact of cloud computing and integrate it into the overall design of the platform.

Third, an ecosystem of suppliers and partners needs to be built to support implementation, taking into account the different complexity levels of individual enterprises. They include a type of production and products, enterprise size and IT-OT landscape, etc.

Where does R&D step in to make a difference?

R&D is essential for innovative IIoT use case development and future-proofing.

Research and development (R&D) is the part of a company that seeks knowledge to develop, design and improve its products, services, technologies or processes. Along with creating new products and adding features to old ones, R&D investments integrate different parts of a company’s strategy and business plans, such as marketing and cost reduction.

R&D consists of investigative actions that a person or business takes to obtain the desired discovery result. A completely new product, product category, or service will be created.

The development part refers to applying a new science or thinking so that a new and better product or service can begin to take shape.

Research and development is essentially the first step in developing a new product, but product development is not only research and development. As an offshoot of research and development, product development can refer to the entire product life cycle, from concept to sale, refurbishment, and retirement.

R&D for IIoT use case development

Deploying IIoT use cases is facilitated mainly by the following factors:

  • Enhanced usability of IoT platforms

IoT and IIoT platforms become more user-friendly as the software supports user extension. In addition, as access to IIoT software development expands, more experimentation will take place to benefit the entire IIoT manufacturing ecosystem.

  • Simplified development

A rich set of ready-to-use APIs, code snippets and microservices, well-established communication standards, and available solutions with little or no coding ensure more cost-effective creation and implementation of use cases.

These low-code/no-code platforms offer small and midsize businesses access to IIoT software functionality without the need for talented programmers that early adopters should have had.

  • Imprinted scalability

Routines for rapidly provisioning and updating applications in reusable data pipelines, containerization for hardware-independent software deployment across multiple peripherals, and sophisticated solutions to manage the entire software/hardware stack enable DevSecOps methodologies to be implemented and help scale platforms rapidly across the enterprise.

  • 5G and AI become the catalysts of change when it comes to IIoT innovation

For enterprises migrating to Industry 4.0, 5G offers corresponding short-term IIoT opportunities. With these dramatic productivity gains, factories and plants can overcome the severe disruptions inherent in pre-Industry 4.0 production halls. As a result, manufacturing is expected to account for more than half of all 5G sales when 5G brings significant benefits.

Are you looking to leverage external R&D to future-proof your IoT solution's feasibility and/or build PoC fast and cost-effectively?

Get in Touch

Using R&D to harness the power of 5G, AI, Edge Computing, and Mixed Reality

5G

Some of the most compelling examples of 5G in manufacturing are automated guided vehicles and real-time process control. Today, automated vehicles typically connect to a Wi-Fi network and rely on software loaded on the vehicle to manage routing and task execution. 5G will dramatically improve connectivity to the edge of the cloud for real-time sharing data between vehicles and coordinating fleets, thereby allowing them to free themselves from fixed paths in an ever-changing manufacturing environment.

As an added benefit, 5G also improves reliability by offering seamless connectivity as automated vehicles move through the factory and switch between access points or radios. When it comes to real-time process control, wired controllers and sensors can be used for years. However, 5G opens up a new space for solutions, helping to connect modules for relatively easy targeted analysis of sensors and actuators – especially important for older machines, whose control and power systems may be outdated and less adaptable to modern technology.

Artificial Intelligent (AI)

Artificial intelligence represents the third era of computing, usually defined as the ability of machines to perform cognitive functions as or better than humans. These functions include perception, learning, reasoning, problem solving, contextual understanding, inference and prediction, and creativity.

The convergence of groundbreaking research, business use cases, skyrocketing data growth, and improvements in computing power and storage are all encouraging advances in AI.

As there’s no single standard marking a clear distinction between weak AI and strong AI, both AI researchers and AI project managers find it difficult to make the right decision about architectures and technologies that should be used to deliver innovative AI-driven experiences. R&D provides a solid platform for experimentation, which results in better justification of corporate AI spending.

In particular, R&D allows for extensive experimentation and discovery around the following AI areas:

  • MLOps
  • Low-code/no-code machine learning (ML)
  • Web-scale content analysis
  • Empathy and emotions simulation
  • Artificial Emotional Intelligence (AEI)
  • Serverless computing
  • Data synthesis 
  • Digital twins
  • Advanced AI chipsets, etc.

Edge Computing

AI-powered decision making and processing happens closer to the data source generation, in contrast to the cloud, a technique known as “edge computation”. Internet of things and its billions devices combined with 5G network and increased processing power, has made large-scale AI possible at the edge of the possible.

Data processing directly on devices will be important in the future for healthcare, automotive and manufacturing applications as it is potentially faster and safer.

 

In 2021, US tech giant Dell Technologies has officially launched a $66 million R&D center in Singapore to spur edge computing innovation.

Source: TNP Tweet

Mixed Reality (MR)

Microsoft designed HoloLens 2 head-up display (HUD) specifically for business solutions leveraging cloud and AI capabilities, interoperability with industry applications, and a suite of developer tools.

The device has already been adapted for the US Army and many of its advanced features include thermal imaging and night vision.

Smart glasses and headgear maker Nreal has announced an Android-compatible all-in-one headset for enterprises that looks more like a helmet than a pair of goggles, with built-in edge computing capabilities. These business-focused headsets are used in a wide variety of applications, from monitoring supply chains and complex equipment with digital twins, to conducting remote 3D meetings and providing augmented reality guides in on-the-job training.

These and other solutions have already motivated many R&D teams to experiment with AR, VR, Diminished Reality (DR) and integrate them with AI, machine learning, and Cloud.

Our rinf.tech R&D team is no exception. We’ve recently built a HoloLens-based digital twin of the robotic arm used for touchscreen testing.

Future IIoT architecture, as per McKinsey

future IoT architecture

How to start developing an IIoT R&D project

Step 1: Discover and deploy innovative value-driven use cases that deliver positive impact

Deploying early use cases is an investment, and therefore it is important to think about the deployment in a strategic manner. In most situations, it is recommended to deploy use cases based on value/feasibility.

First, select use cases that create immediate value and avoid use cases that require building databases from scratch or other complex prerequisites.

While the focus is usually on low-effort, high-value use cases, there is a strong case for using use cases with little or no ROI. For example, a use case may not result in immediate impact, but it may allow future use cases to be used.

This decision is pre-analyzed, and the rationale must be clearly stated so that all relevant stakeholders are aware of the investment and its value, even if that value is delayed.

Step 2. Begin collecting and aggregating data while building important features in parallel

Not all items need to be securely fastened before the deployment. In particular, even before the required data is connected, prioritization of use cases provides an overview of the required data sources and an understanding of what data is available and can be connected. The data collection architecture can be customized or defined for a specific OT equipment and data source based on this information.

However, in parallel, the development of the platform core should not be neglected and should start in sync with the development of the use cases. It is necessary to connect sources for automatic data collection as soon as possible. Depending on the implementation state of the target architecture, a use case can be deployed to a local server based on a specific architecture. This assumes the use of frameworks that will eventually be used on the back end.

But it won’t take long to implement the final architecture prioritizing the first use cases, and the migration of the first use cases will be possible in just a few weeks. Further, each use case is implemented in the target architecture. In preparation for this, consider and engage the next wave of use cases by connecting to the required data sources in the previous wave (s).

The point to keep in mind here is that many use cases take longer to prepare simply because some data sources do not yet exist. To compensate for this, it may be necessary to add sensors, change the PLC logic of the machine, update systems, and so on.

Step 3. Establish processes and collaboration that will ensure the required deployment

A standard process of connecting to new businesses should be established to ensure that the team is in place when deployments begin there. Implementation is also about changing how an organization works and thinks; thus, it is important to make informed change management the same priority as the deployment itself. The success of a use case can be undermined if the benefit has not been clearly identified and people are either not using a new application or system in their day-to-day work or are using it incorrectly.

A central IIoT deployment team takes care of the needs across the enterprise, making sure everything is in place prior to deployment. The list of needs includes the skills and the technical background required for a use case. The R&D team identifies the right people on-site to manage the deployment and resolves issues.  The central team also monitors deployment status and emerging issues.

This provides a holistic view of benefits, implementation status and problem solutions to generate best practices for IIoT innovation.

A deeper look at use cases will almost certainly reveal a need for skills that may not currently be available in an organization or individual business. Hiring new talent or training existing employees should begin as early as possible so that skills are in place when they are needed.

Skill development and capacity building is an ongoing effort because deploying use cases never ends. Investing in these areas is a must for an organization that wants to succeed in its Industry 4.0 efforts.

It is also important to have a clear collaboration model that identifies one person for each workstream with clear accountability. Effective communication must be ensured within the organization, such as the exchange of information between enterprises and between the organization and its partners. Important archetypes of value chain partners are industrial equipment suppliers, technology providers and integrators, as well as specialists in functional areas.

Employee training should begin before deployment (or even concurrently) to ensure a shared vision and understanding of each use case capabilities and advantages. Training sessions are critical as time is devoted to answering employee questions and concerns. In addition, getting the people who will be working on a new application or tool into the development process and getting their feedback can create a tool that better suits their needs and provides broader acceptance and interest.

It will also be important to define the expectations and responsibilities of the use case owners as they will define the subsequent sprints within the use cases and report status updates and issues. Consistency of processes is very important, such as how communication occurs and measuring status and progress.

 

R&D is an integral part of any IoT project, and expanding it can be the first step to project success. By leveraging research and development, your business can optimize costs, reduce risk, and minimize time-to-market for various IoT applications. R&D teams manage both the technical and business elements of the product. Their responsibilities include evaluating both the usefulness of the product and the technology itself.

In addition to this, R&D teams can see future business opportunities. The end result of this should be research, prototyping, and clarification on the IoT product. The R&D team can study the idea before it becomes a product and support the transformation of the concept into a functional asset that solves the user’s problems.

Benefits of utilizing R&D for IIoT projects

Fast time to POC, Prototype and MVP

IoT technology is evolving at a rapid pace. Why not save time and effort by testing ideas with a Proof of Concept (POC), Minimum Viable Product (MVP), or Prototype before launching a specific product?

POC is the practice of deep product interpretation. This happens before any planning for a product. POC allows you to learn a concept without spending too much time and money.

Prototype has the same basic goal of helping companies understand if a product is potentially good enough to get the job done. 

The R&D team can test the product’s performance, design, and benefits by creating a prototype. This way, you can use the prototype with a limited group of people to test it out before going to market for multiple users.

MVP is a product with a sufficient number of features that can be implemented in the market. The goal of MVP is to provide feedback for the future development of IoT projects. Choosing between one or all of these three can be a little tricky, and doing one of them requires specialists and some IoT equipment.

Enterprise innovation cost optimization

Digital transformation and innovation projects often lead to overhead costs. This includes administrative costs in excess of the total cost of the equipment. Using R&D for IIoT projects will help reduce these costs. Many tech companies provide their R&D services to businesses looking for a variety of outsourcing options. Some of these companies even have entire departments that focus on experimenting with cutting edge technology. They investigate and scrutinize IoT solutions, deep learning, robotics, digital twin and other technologies to create solutions, POCs and MVPs for themselves and their customers.

Better Control and Risk Management

The company must interpret risks and be able to assess or mitigate them immediately. Using R&D for IoT projects can help you mitigate these risks. R&D engineers need to understand well how new IoT devices or functions will work and whether they can meet your business goals and requirements.

Like any other technology, IoT solutions need deep research and development and are best done by professionals. R&D outsourcing allows you to optimize costs, get an idea to market faster and avoid life-saving risks.

Final Thoughts

Wrapping up, expanding market participation, cost management benefits, developing marketing capabilities and keeping up with trends are all reasons why companies invest in R&D. Research and development can help a company follow or stay ahead of market trends and keep the company up-to-date.

While R&D requires complex engineering-heavy resources, quality certifications, compliance with the industry standards and more, the innovations resulting from it may actually work to lower costs through more efficient manufacturing processes or more efficient and higher-quality products.

To accelerate enterprise innovation and create a unique and compelling IIoT value proposition, i’s highly recommended that manufacturing companies establish technology partnerships with companies that already have well-established R&D centers or innovation labs and that have accumulated best practices of leveraging the newest technologies and methodologies.

We'll share tips for choosing the right custom IoT software development partner in our next blog post. Meanwhile, check out what it takes to build a highly effective IoT development team in-house or offshore.

Looking for a technology partner?

Let’s talk.

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