logo
RPSG Ventures shares rise over 3% after Manchester Originals acquisition update

RPSG Ventures shares rise over 3% after Manchester Originals acquisition update

Business Upturn29-07-2025
By Aditya Bhagchandani Published on July 29, 2025, 09:41 IST
Shares of RPSG Ventures Ltd jumped over 3.6% on Monday, July 29, to ₹986.00 on the NSE after the company confirmed the acquisition of a majority stake in a UK-based cricket franchise. The surge came after RPSG Sports Ventures Pvt Ltd (RPSVPL), a subsidiary of RPSG Ventures, executed a Share Purchase Agreement with the England and Wales Cricket Board (ECB) to acquire a 70% equity stake in Manchester Originals Ltd.
According to the company's stock exchange filing, the acquisition was completed on July 28, 2025, for a total consideration of GBP 81.21 million (approx ₹941 crore), to be paid over a period of 24 months. This strategic move brings Manchester Originals—a team in 'The Hundred' cricket tournament—under RPSG's sports portfolio.
The management said the acquisition is part of RPSG's long-term vision to strengthen its footprint in the global sports ecosystem, particularly in cricket. It expects the deal to open up international growth opportunities through collaborations, talent development, and increased brand visibility across major cricket markets.
Manchester Originals, founded in 2019, had a turnover of GBP 2.04 million for the financial year ending January 2024. The team represents Lancashire in The Hundred and becomes a step-down subsidiary of RPSG Ventures following the acquisition.
The announcement was well received by investors, pushing the stock to its day's high of ₹988.65. RPSG shares have now gained over 45% in the past six months.
Ahmedabad Plane Crash
Aditya Bhagchandani serves as the Senior Editor and Writer at Business Upturn, where he leads coverage across the Business, Finance, Corporate, and Stock Market segments. With a keen eye for detail and a commitment to journalistic integrity, he not only contributes insightful articles but also oversees editorial direction for the reporting team.
Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

Renewable energy is a big opportunity for our region–we must seize it
Renewable energy is a big opportunity for our region–we must seize it

Yahoo

time15 minutes ago

  • Yahoo

Renewable energy is a big opportunity for our region–we must seize it

East Anglia is at the centre of the UK's renewable energy sector. Take offshore wind alone – we are home to 44% of the UK's existing offshore wind farms. With 3.6 GW already operational and a further 10 GW of projects in the pipeline, we have a crucial role to play. Data from CBI Economics (2025) show that almost one million full-time equivalent (FTE) jobs are currently supported by the clean energy and net zero economy — that includes 83,400 in the East of England. The Labour Government is serious about seizing the opportunity of renewable energy for our country and our region, delivering clean energy jobs for the UK, boosting energy security and economic growth. Recently, I visited Scroby Sands, a wind farm, which lies off the coast of Great Yarmouth with the company RWE. Commissioned in 2004, Scroby Sands powers the electricity needs for approximately 48,000 homes – the equivalent of all the homes in King's Lynn and already provides many jobs. We have even greater potential in East Anglia. The proposed Norfolk Offshore Wind Zone is made up of Vanguard West, Vanguard East, and Boreas. Once up and running, it would be one of the biggest wind clusters in the world with a combined generating capacity of 4.2 GW (1 GW more than Sizewell), producing enough electricity to power the equivalent of more than 4 million UK households. This project would bring billions of pounds of investment and employment to Norfolk. Many jobs would be created during the construction phases as well as long-term jobs during the operations phases. There are also many more opportunities in the broader supply chain. Those jobs are much needed in Norfolk and would create opportunities for our young people now and in the future. To progress these projects, it is vital that the upcoming Contract for Difference (CfD) Allocation Round 7 (AR7) auction is a success. The CfD scheme guarantees a fixed price to energy companies for their electricity, helping to encourage investment in clean energy projects. Yet, recently Reform's Deputy Leader Richard Tice wrote to the heads of eight energy companies, including RWE, putting them 'on notice' and warning that any companies entering this auction round would do so at their 'own risk'. In May, he also said that Reform would use "every lever" to block renewable energy developments in the councils they now control. This would be bad for jobs, bad for consumers, bad for energy security and bad for our country. In 2024, the clean energy economy nationally grew by more than 10% and generates £83.1 billion in Gross Added Value for Britain. It would also have a retrograde impact on Norfolk and the East of England more broadly, putting the thousands of jobs that could be created through clean energy at risk. Failing to back renewable energy is not only failing to back British jobs but also failing to back the action needed to get energy bills down and to tackle climate change. We have seen what happens when we are too dependent on foreign gas – bills soar and we have little control. That's one of the reasons why investing in British renewables is vital. So, is Reform happy for us to remain reliant on expensive foreign fossil fuels rather than cheap, homegrown, clean energy? I know that my constituents in Norwich North want lower energy bills and investing in renewables is part of the answer. So, why would a political party oppose such vital projects and the opportunities they would bring to regions like ours? It seems to me that they are willing to put ideology before our national interest. This is in stark contrast to the Labour Government's clean energy mission. Since July 2024, over £40 billion of private investment has been announced into the UK's clean energy industries - helping to kick-start the economy to put more money in people's pockets. The economic scale of the opportunity and prize on offer to Norfolk and the wider region is huge. Backing investment in clean energy is backing Britain. *Alice MacDonald is the MP for Norwich North.

Scaling Production AI: 20 Surprising Hurdles And How To Overcome Them
Scaling Production AI: 20 Surprising Hurdles And How To Overcome Them

Forbes

time17 minutes ago

  • Forbes

Scaling Production AI: 20 Surprising Hurdles And How To Overcome Them

Scaling AI from prototype to production is a major hurdle for many organizations. Even with skilled teams and powerful models, companies often hit unexpected bottlenecks—such as disjointed data pipelines, unclear ownership or low user trust—that slow or stall progress. To scale AI successfully, businesses need more than just technical expertise; they must invest in robust infrastructure, change management and cross-functional alignment. Below, members of Forbes Technology Council reveal common hurdles teams face when scaling production AI—and share practical strategies for overcoming them. 1. Instilling Digital Literacy And Trust Among Team Members A major bottleneck in scaling AI is organizational resistance due to fear and digital skill gaps. Many employees worry that AI will replace their roles or feel unprepared to use it. This often leads to low engagement and stalled adoption. To overcome this, companies must invest in AI literacy and upskilling. Involving staff early in the AI journey fosters trust, making scaling smoother. - Praveen Tomar, UK Civil Services (Ofgem) 2. Choosing The Right Problems To Tackle AI holds promise in enterprise restaurants—from predicting inventory to personalizing upsells. However, a common bottleneck isn't technical; it's misalignment between what's built and what drives value. Too often, teams chase flashy use cases without validating impact. Scaling AI starts with choosing the right problems and having the right data. - Peter Kellis, TRAY Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. Providing Frontline Access One key bottleneck is providing direct access for the frontline workers who would benefit from it. Providing frontline workers with AI solutions to efficiently and effectively complete their tasks is critical in today's landscape, where time is of the essence. - Mike Zachman, Zebra Technologies Corporation 4. Building Trustworthy Data Architecture Fragmented data architectures fragment business context. Without integrated enterprise knowledge, AI lacks organizational understanding. Success requires a trusted data architecture that's operationally simple yet preserves business meaning. - Louis Landry, Teradata 5. Avoiding Prompt Injection And Tool Misuse Prompt injection and tool misuse are critical bottlenecks. Attackers can trick LLMs into leaking sensitive data, performing unauthorized actions or amplifying these to widen impact. This slows deployment and can lead to costly breaches. You can mitigate risk with the same security best practices that you use elsewhere in the organization: least privilege, sandboxing, audits and adversarial testing. - Willem Delbare, Aikido 6. Managing AI-Generated Content Overload AI will generate more content than existing review and approval processes are designed to handle. Companies will need to decide how to proceed—they can either accept the risk, dramatically slowing adoption, or find and/or build tools to help them manage it. - Larry Bradley, SolasAI 7. Identifying And Managing ML Pipeline Cost And Performance Issues Companies often overlook MLOps when they take on AI-related development. ML pipelines can have very different cost and performance challenges than traditional development domains. Understanding the true unit cost of model performance is essential and can mean the difference between a model that scales well and one that doesn't. - Siamak Baharloo, Labviva 8. Linking AI Spend To Business Value One big bottleneck? The CFO calling out the inference cost line on the P&L. If teams can't clearly link AI costs to business value, there's a real risk that projects will stall. The solution? An AI Command Room. Start by building a clear business case tied to at least one normalized metric, such as labor dollars saved through AI agents and automation. Then, track and report impact—from hypothesis to daily execution. - Matt Kesby, Multiplai Tech 9. Establishing Shared Data Definitions As enterprises deploy agentic AI, agents make decisions based on data no one fully trusts. In pilots, humans catch issues. At scale, autonomous systems pull from disconnected sources with conflicting rules, and small cracks become major failures. The issue isn't the model; it's the lack of data trust. Scaling AI requires shared definitions and automated checks to ensure a reliable foundation. - Jay Limburn, Ataccama 10. Grounding Data In Real-Time, Trusted Context A key bottleneck in scaling AI is context fragmentation—models can't reason effectively without access to clean, connected and secure organizational data. Grounding data in real-time, trusted context is the real challenge. The fix is better infrastructure: unified knowledge layers, privacy-preserving compute and retrieval pipelines that align AI with the right data. - Regan Peng, PINAI 11. Addressing Data Lineage And Quality Debt One unexpected bottleneck is data lineage and quality debt. Many organizations assume that once a model is trained and performs well in testing, scaling it into production is mostly an engineering and computing problem. In reality, the biggest bottleneck often emerges from inconsistent, incomplete or undocumented data pipelines—especially when legacy systems or siloed departments are involved. - Sandeep Uthra, OneAZ Credit Union 12. Working With Outdated Infrastructure A bottleneck would be inadequate data infrastructure, which can be overcome by investing in modern, scalable data platforms and robust engineering practices. Companies should be establishing robust data governance practices and centralized data infrastructure early in the development lifecycle. - Nazih Chamtie, KMicro Tech, Inc. 13. Managing Model Drift Even though data quality is an obvious bottleneck, when we scaled AI in production, I primarily expected challenges with data and infrastructure. However, what caught us off guard was model drift. For example, in finance, market shifts rendered our fraud models stale. In healthcare, evolving patient data skewed predictions. Real-time monitoring and retraining turned out to be just as critical as building the models. - Dr. Suresh Rajappa, KPMG LLP 14. Overcoming Resistance To AI Integration Change management within the existing user experience is a significant challenge when scaling AI. While AI has enormous potential to enhance user experience, effective integration requires more than technical implementation. Organizations can proactively address this issue by increasing user adoption, reducing resistance and unlocking AI's full transformative potential. - Shivaprakash S Nagaraj, Digit7 15. Struggling To Find Skilled Architects And Operators Designing and building infrastructures that support AI initiatives requires a different set of skills. While many current IT professionals will get up to speed over time, there are significant nuances that come with AI that they may have never experienced before. Struggling to find skilled infrastructure architects and operators who already understand these nuances will be the reality for a few years. - Mike Wong, Accton Technology 16. Developing A Single Source Of Truth AI can be hindered by a lack of data access, data silos, data quality issues, missing context and so on. A lack of a single source of truth is also a common issue—especially if data connections are intermittent and complex—and AI models need to be run locally. Thinking strategically about the desired business outcomes and implementing a data fabric will improve data management and help scale AI end-to-end. - Heiko Claussen, Aspen Technology, Inc. 17. Identifying And Patching Security Gaps Security is the most significant challenge, because LLMs can't be controlled. Even foundational model providers' system prompts and training instructions have leaked. Strengthening AI security requires identifying potential vulnerabilities and testing against them, but we do not yet have an exhaustive list of those vulnerabilities, as AI systems remain a black box. - Rohit Kapoor, Tekmonks 18. Connecting AI Outputs To Decision-Making Processes A major bottleneck is the lack of integration between AI models and core business workflows. Even the best models fail to deliver value if their outputs don't connect to decision-making tools or processes. You can overcome this by embedding AI via APIs, aligning with existing systems and designing with end users to ensure seamless adoption and continuous feedback loops. - Motasem El Bawab, N3XT Sports 19. Detecting Performance Degradation Companies can build decent AI models but struggle to scale them due to poor production monitoring and deployment capabilities. Unlike deterministic code, where 2 + 2 always equals 4, AI models produce variable outputs, making it hard to detect performance degradation. Companies can overcome this by implementing robust MLOps pipelines with continuous monitoring, A/B testing, and automated alerts for model drift detection. - Mia Millette, Skyline Technology Solutions 20. Ensuring Organizational Alignment Accelerating AI initiatives without addressing data quality, readiness and strategic alignment will undoubtedly result in significant roadblocks. To scale successfully, organizations must prioritize use cases based on business impact, ensure data is properly prepared, and tightly align each initiative with clear, measurable outcomes. - Sean Nathaniel, DryvIQ

Lotus to join show of F1 championship winning cars
Lotus to join show of F1 championship winning cars

Yahoo

timean hour ago

  • Yahoo

Lotus to join show of F1 championship winning cars

A "legendary" Formula 1 team will showcase some of its former world championship winning race cars at a motorsport festival, organisers said. The Silverstone Festival in Northamptonshire, due to be held over the August bank holiday weekend, has announced four cars from the former Norfolk-based Team Lotus will be part of display featuring F1 cars raced by all 34 of the sport's world champions. The showcase is part of a special celebration to mark the 75th anniversary of the Formula 1 World Championship. Nick Wigley, the event's director, said the display was an "ambitious challenge that no one has ever attempted before". He said: "Now, the star-studded collection is not only nearly complete, but it also features an incredible number of title-winning cars. McLaren, Mercedes, Red Bull, and Williams have already confirmed their support for the showcase, contributing title-winning cars driven by illustrious champions including Ayrton Senna, Nigel Mansell, Fernando Alonso, Sir Lewis Hamilton and Max Verstappen. Team Lotus was founded by Colin Chapman and recorded its first F1 entry in 1958. Based at Hethel, near Norwich, the team counted world champions Jim Clark, Jochen Rindt, Emerson Fittipaldi, and Mario Andretti among its drivers. British driver Clark was intrinsically linked with Team Lotus in its formative years, winning titles in 1963 and 1965 and the "iconic" Lotus 25/R4 in which he won his first crown will join the display, alongside those driven by Rindt, Fittipaldi and Andretti. The festival will also feature performances from Natasha Bedingfield and Craig David presents TS5. The Silverstone Festival runs from 22-24 August. Follow Northamptonshire news on BBC Sounds, Facebook, Instagram and X. More on this story Festival to showcase biggest display of Senna cars Related internet links Formula 1

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into a world of global content with local flavor? Download Daily8 app today from your preferred app store and start exploring.
app-storeplay-store