Latest news with #IDC
News24
21 hours ago
- Business
- News24
Rallying South Africa towards the Common Good
The Social Employment Fund (SEF), managed by the Industrial Development Corporation (IDC), is a transformative initiative aimed at combating unemployment in South Africa by supporting work that serves the 'common good.' It partners with civil society organisations like NGOs, community kitchens, ECD centres and faith-based groups to provide meaningful work and promote social cohesion. Since its inception in 2023, over 150,000 people have participated in the programme. Many have never worked before – but have been able to use the skills, capabilities, self-esteem and confidence gained to take the next steps on their pathways out of poverty – into better jobs – or into livelihood and micro-enterprise activity. Building agency at community level SEF-funded projects have led to significant skills transfer in areas such as: Health and Care: Home-based care, maternal health support, psycho-social services. Agriculture: Organic farming, composting, food garden management. Education: ECD facilitation, after-school tutoring, sports programmes. GBV Support: Community-based paralegals, counselling, awareness raising campaigns. Greening and Cleaning: Waste management, Beneficiation of biomass, invasive species removal. Community and public art: Presentation skills, social media usage, Arts and Crafts showcasing and performance Digital Inclusion: Computer & research skills, data gathering and survey process These programmes not only provide income, but also foster community agency and resilience, reinforcing a sense of purpose among participants. Case studies of impact 1. Johannesburg Inner City Partnership (JICP) In partnership with the City of Johannesburg, JICP addresses urban regeneration by employing 1,600 participants through NGOs like Urban Space Management and Clean City SA. Participants clean public spaces, plant gardens and help reduce crime through placemaking. Youth-focused programmes such as Sport for Social Change combine recreation with life skills training, promoting safety and community pride. 2. Hlanganisa Institute: Sisterhood Advocates Operating in provinces like Free State and Limpopo, Hlanganisa has trained over 2,600 'Pinkies'—female paralegals who support survivors of gender-based violence (GBV). With SEF support, their reach expanded from 391 to 6,000 GBV cases handled monthly, highlighting how social employment can significantly scale up effective community initiatives. 3. Food Security: Seriti Institute and Solidaridad Network Addressing hunger, Seriti developed an agri-node in Deelpan, North West, offering fresh produce and reducing clinic visits due to improved health. Solidaridad empowered 1,650 smallholder farmers with technology and market access in Gauteng, Eastern Cape and North West, contributing to long-term household food security. The Social Employment Fund is part of the Presidential Employment Stimulus, in support of the social economy strategy of the Department of Trade, Industry and Competition. The Industrial Development Corporation is the fund manager.
Techday NZ
2 days ago
- Business
- Techday NZ
Genesys unveils AI Studio to power safe, scalable virtual agents
Genesys has launched Genesys Cloud AI Studio, a platform designed to help organisations build, manage, and scale AI-powered customer engagement initiatives with a focus on governance and responsible deployment. Genesys Cloud AI Studio serves as a central hub for organisations to innovate using no-code tools and built-in compliance measures, introducing agentic AI features intended to improve personalisation and efficiency. Its first released capability, AI Guides, enables teams to design, deploy, and manage autonomous virtual agents across complex customer journeys at scale. According to Genesys, AI Guides distinguishes itself from existing solutions by supporting virtual agents that can adapt in real-time and manage multi-step interactions, rather than being restricted to pre-programmed flows. Governance and transparency are embedded in the design to address concerns over trust, ethics, and explainability—challenges that have hindered the wider adoption of AI among enterprises. Hayley Sutherland, Research Manager for Conversational AI at IDC, commented, "Scalable oversight of AI is critically important as we move into agentic decision-making and operation at lightning speed. For enterprises to leverage agentic AI, they must evolve how they approach AI governance and adoption. Genesys is offering organisations a robust entry point for autonomous AI, and the tools to help ensure every AI-powered customer interaction is secure, ethical and aligned to their strategy and values." Research from the IBM Institute for Business Value indicates that 80% of business leaders see explainability, ethics, bias, or trust as significant challenges to adopting AI, even with conventional deterministic forms such as generative AI. Half of those surveyed admit their organisations do not have the governance structures in place to adequately manage these risks. No-code tools and governance AI Guides is designed for businesses looking to scale semi-autonomous customer interactions with minimal technical resources. Unlike most virtual agents that rely on rigid programming and break down under complex scenarios, Genesys claims its new system allows virtual agents to reason, adapt, and make decisions dynamically within guardrails set by the business. This capacity is intended to enable agents to handle more complex, multi-step customer interactions, trigger enterprise-wide workflows, and act in key moments on behalf of brands, while maintaining compliance and oversight. The features of AI Guides include the ability to build or refine virtual agents using natural language or existing documentation, eliminating the need for coding. Users can design an interaction once and deploy it across different channels, which the company says will promote consistency and reduce duplication of effort. Other capabilities enable seamless integration with front, middle, and back-office systems, automate workflows, and implement testable safety controls to ensure accurate and policy-compliant engagement. Genesys highlights the model-agnostic architecture of AI Guides, which allows continuous A/B testing and iterative improvements as new language models become available. The Genesys Cloud platform supports integration with proprietary, open-source, and third-party models, including those available via Amazon Bedrock, as well as customer-provided models. Extending agentic AI AI Guides is intended to complement existing conversational, generative, and predictive AI functions within Genesys Cloud. Central to the suite is the Genesys Event Data Platform, which transforms raw interaction data into structured insights such as user intent, outcomes, and behaviour to improve customer profiles. Genesys suggests that this enhanced context leads to smarter orchestration and more effective training and operation of AI models. Further development is planned, with the forthcoming Genesys Cloud Custom Conversation Summaries tool touted to allow organisations to generate interaction summaries tailored to their unique brand voice, compliance needs, and formatting standards. Olivier Jouve, Chief Product Officer at Genesys, stated, "Many organisations lack the infrastructure and governance framework for the responsible use of agentic AI with their customers today. We're focused on giving every business an accessible and safe path forward. With AI Guides, Genesys is delivering the foundation for agentic orchestration with guardrails and trust embedded so our customers can accelerate innovation and position themselves to drive ROI and differentiation." Genesys anticipates that AI Studio capabilities, including AI Guides and Custom Conversation Summaries, will be available during the second quarter of its 2026 fiscal year, between 1 May and 31 July 2025.
Forbes
2 days ago
- Business
- Forbes
Edge AI Applications As The Catalyst For AI PC Market Growth
Ajith Sankaran, Executive Vice President, C5i. getty Despite all the buzz, the adoption of high-performance AI PCs with powerful neural processing units (NPUs) has been especially sluggish. Since their launch in mid-2024, these devices have captured just 5% of AI PC market sales. This can be attributed to several factors: • AI PCs typically command a significant price premium without clearly articulated benefits. Many users remain unconvinced that these costs translate to meaningful improvements in computing experiences. • Compatibility concerns persist, particularly with first-generation advanced RISC machine (ARM)-based systems that may not support legacy software. • There is a scarcity of software applications that fully harness AI PC capabilities. According to a 2024 ICD report, the global market for personal computing devices was "set to grow 3.8% in 2024, reaching 403.5 million units." However, this growth is primarily driven by a nearly double-digit growth in tablets. According to Jitesh Ubrani of IDC, 'There seems to be a big disconnect between supply and demand as PC and platform makers are gearing up for AI PCs and tablets to be the next big thing, but the lack of clear use cases and a bump in average selling prices has buyers questioning the utility.' I believe the answer to realizing the potential of AI PCs in enterprise scenarios lies in understanding and utilizing edge AI. To understand why, let's take a closer look at how these systems operate. Edge AI And Its Relationship With AI PCs Edge AI represents the convergence of AI and edge computing, enabling AI algorithms to run directly on local devices rather than in remote data centers. This approach processes data where it's generated, eliminating the need to send information to the cloud for analysis and returning results almost instantaneously. AI PCs are well-positioned to serve as powerful edge AI platforms due to their unique hardware architecture. They integrate three processing components: • A central processing unit (CPU) for general computing tasks. • A graphics processing unit (GPU) for parallel processing workloads. • A neural processing unit (NPU) optimized for AI computations. This triad of capabilities allows AI PCs to handle edge AI applications with efficiency. The performance benefits can be substantial; security company CrowdStrike reported that its software's CPU consumption dropped from 35% to 1% when running on machines equipped with Intel NPUs. Global shipments of AI PCs are projected to reach 114 million units in 2025, accounting for 43% of all PC shipments. I believe that edge AI that incorporates the latest advances in generative AI and agentic AI could provide tangible benefits that justify the premium pricing of AI PC for consumers and enterprises. As more developers create software that leverages NPUs and other specialized AI hardware, the value proposition should become clearer, driving increased adoption across both consumer and enterprise segments. Emerging Edge AI Applications Driving AI PC Demand • Manufacturing Intelligence Manufacturing environments are proving to be fertile ground for edge AI applications. AI systems running locally on AI PCs can monitor equipment health in real time, detecting anomalies and predicting potential failures before they occur. This can reduce costly downtime. Quality control represents another application. AI-powered cameras connected to edge computing systems can inspect products for defects with precision and consistency. • Healthcare Innovations The healthcare sector also stands to benefit from edge AI. Portable diagnostic devices equipped with edge5 AI can analyze medical images such as X-rays, MRIs, and CT scans locally, providing rapid insights without requiring cloud connectivity. This is particularly valuable in remote areas. And wearable health devices using edge AI can analyze biometric data locally, detect anomalies and alert healthcare providers without transmitting sensitive patient information to remote servers. • Retail Transformation In retail, edge AI applications are revolutionizing operations and customer experiences. AI-powered cameras and sensors can track inventory levels in real time, optimizing stock replenishment. The same infrastructure can analyze customer behavior patterns, enabling retailers to deliver personalized recommendations and promotions. These capabilities require significant local processing power that can be provided by AI PCs to analyze video feeds and sensor data in real time. • Security and Privacy Protection Edge AI can deliver faster performance while keeping sensitive data local instead of sending it to cloud services. For example, Bufferzone NoCloud "uses local NPU resources to analyze websites for phishing scams using computer vision and natural language processing." Edge AI applications can enhance banking security by detecting unusual transactions and immediately alerting users. Recommendations For Effective AI PC and Edge AI Adoption 1. Develop edge-native AI applications for real-time decision-making. Prioritize building edge-native AI applications that leverage the NPUs in your organization's AI PCs to execute machine learning models locally. For example, manufacturing firms can deploy vision systems on AI PCs to perform real-time quality inspections directly on production lines, reducing defect rates while eliminating cloud dependency. 2. Deploy agentic AI systems for autonomous workflow optimization. Agentic AI excel at autonomously managing complex, multi-step processes. In supply chain, running agentic AI systems on AI PCs can allow you to dynamically reroute shipments based on real-time traffic data processed at the edge, reducing delivery delays. Financial institutions can also combine agentic AI with edge computing to autonomously monitor transactions for fraud patterns, triggering immediate alerts while keeping sensitive financial data localized. 3. Implement privacy-centric AI architectures for regulated industries. Consider adopting hybrid edge-cloud AI architectures to balance computational demands with regulatory compliance. For example, banks can deploy on-premise AI PC clusters to run agentic AI fraud detection systems, ensuring customer transaction data never leaves internal networks. 4. Build scalable edge AI infrastructure with modular hardware. Invest in AI-optimized hardware ecosystems that support both current and emerging workloads. For instance, consider deploying AI PCs with dedicated NPUs for employee productivity tools and pairing them with edge servers containing GPU/TPU arrays for heavy computational tasks. 5. Integrate generative AI with edge computing for adaptive systems. By fusing generative AI with edge computing, you can enable dynamic system adaptation within your company. For example, manufacturers can deploy small language models on AI PCs to generate equipment repair instructions tailored to real-time sensor data, reducing machine downtime. Conclusion While initial adoption of AI PCs has been slow due to high costs, compatibility issues and a lack of applications, the emergence of edge AI use cases is beginning to demonstrate the value of local AI processing. As developers increasingly leverage NPUs to build edge-native and agentic AI solutions, I believe the value proposition of AI PCs will become more evident, driving broader adoption across consumer and enterprise markets. 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National Post
2 days ago
- Business
- National Post
Lenovo Named a Major Player in IDC MarketScape for Hybrid IT Infrastructure Consulting and Integration Services
Article content MORRISVILLE, N.C. — Lenovo has been positioned in the Major Players Category in the IDC MarketScape: Worldwide Hybrid IT Infrastructure Consulting and Integration Services 2025 Vendor Assessment. * According to the report, 'Lenovo has significantly invested in its portfolio globally over the past three or more years, with a focus on continuing to build services that accelerate its customers' hybrid cloud, multicloud, and AI cloud initiatives.' Article content And: 'Lenovo's investments in the company's tools, processes, and methodologies were reviewed positively among customers and study respondents, highlighting the ability of the company to improve digital infrastructure and increase visibility of infrastructure and enabling its customers' business agility.' Article content Driving Business Impact Article content Enterprises are rapidly adopting hybrid IT models to stay competitive in an AI-driven economy. Lenovo regards its recognition in the IDC MarketScape as a reflection of its unique ability to deliver scalable, AI-optimized hybrid cloud solutions that are redefining business operations. Article content 'Enterprises are seeking clarity when it comes to AI adoption and their high-performance computing and cloud strategies,' said Linda Yao, Vice President of Hybrid Cloud and AI Solutions, Lenovo. 'That's why we've focused heavily on building end-to-end solutions – from advisory to design, deployment, and ongoing support – to help our customers achieve measurable business outcomes aligned with their IT infrastructure needs, as well as their AI strategies and business goals.' Article content Lenovo helps organizations make better use of their data, moving systems to the cloud, and simplifying complex infrastructure. Its services include planning and guidance for cloud, data, and AI projects. These offerings are standardized using Lenovo's proven five-stage methodology with each phase underpinned by services like Lenovo AI Fast Start. Article content The IDC MarketScape also highlighted positive sentiment from Lenovo customers: 'The goals that we had were achieved when working with (Lenovo); we are able to have their support at all times, and we are operational and functional and more efficient because of their services.' Article content And: 'Lenovo offered significant improvement in all negotiated areas of our business and did exactly what we expected within our price and agreed timing.' Article content Additional Information Article content About IDC MarketScape Article content IDC MarketScape vendor assessment model is designed to provide an overview of the competitive fitness of technology and service suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor's position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective vendors. Article content About Lenovo Article content Lenovo is a US$69 billion revenue global technology powerhouse, ranked #248 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world's largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo's continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY). To find out more visit and read about the latest news via our StoryHub. Article content Article content Article content Article content
Forbes
2 days ago
- Business
- Forbes
The Cloud Reset: Why Private Cloud Is Making A Comeback
Pankaj Gupta is Senior Director of Cloud solutions marketing at VMware by Broadcom. For over a decade, public cloud was the default answer to every IT modernization question. It was faster, cheaper and scalable—until it wasn't. Now, as the dust settles and CIOs reevaluate their cloud strategies, a striking trend is emerging: a decisive 'cloud reset.' According to my company's 2025 report—based on insights from 1,800 IT leaders across the globe—organizations are no longer viewing cloud as a one-way journey to hyperscalers. Instead, they are intentionally shifting workloads back into private cloud environments. The reasons? Security, compliance and cost control. A recent IDC research blog corroborates these findings, highlighting the increasing adoption of private cloud or on-premise computing practices. Beyond The Binary: A Strategic Mix The idea that enterprises must choose between public or private cloud is outdated. In fact, 93% of surveyed organizations already use a mix of both, but the shift we're seeing is about intention. IT leaders are starting to prioritize private cloud for the right workloads—particularly where data sovereignty, compliance and cost predictability are paramount. This isn't cloud repatriation driven by nostalgia. It's driven by optimization and business needs. Repatriation: No Longer A Dirty Word A full 69% of organizations are considering repatriating workloads, and 35% have already done so—with security-sensitive applications leading the charge. What's more telling is that 51% of those citing repatriation did so specifically for security and compliance reasons—and 46% for data-intensive workloads. In today's regulatory climate—where GDPR, HIPAA and global data protection laws dominate IT decision-making—control matters. And 92% of respondents say they trust private cloud more for security and compliance than public alternatives. Cost predictability and optimization is another key driver. As per our findings, 94% believe some of their public cloud spend is wasted, and 31% believe more than half of it is. Private Cloud: Agility Meets Control A few years ago, private cloud was considered slow, siloed and too dependent on legacy infrastructure. That's changed. Modern private cloud platforms now offer self-service provisioning, container support and integrated FinOps tools. In fact, 84% of enterprises run both traditional and cloud-native workloads in their private cloud today, demonstrating that private environments are no longer just for legacy applications. More impressively, 53% of enterprises plan to deploy new workloads in private cloud, reflecting growing confidence in its agility and maturity. The Generative AI Factor With generative AI moving up the CIO priority list, private cloud is also emerging as a strategic foundation for AI workloads that require data privacy, low latency and cost control. Hyperscalers offer unmatched scale, but organizations are growing wary of exposing proprietary data to third-party platforms. Running LLMs and inference engines in a controlled, compliant private environment offers a compelling alternative. Why The Reset Now? This 'cloud reset' isn't reactive—it's reflective. IT organizations have gained real-world experience and are recalibrating based on five key realities: 1. Security Is Nonnegotiable: Nearly a third of IT leaders cite public cloud security risks as their top challenge. 2. Financial Waste Is Visible: Cloud cost unpredictability is a board-level issue. CIOs need clarity and control. 3. Compliance Isn't Optional: Global regulations are forcing tighter control over data residency and access. 4. Skills Matter: Running private clouds well requires talent and breaking operational silos. Many are now reskilling and reorganizing their teams accordingly. 5. Application Needs Vary: Back-office, data-heavy, and even modern apps now have viable paths in private cloud. This isn't about undoing cloud migration. It's about optimizing placement—application by application—based on business, regulatory and financial priorities. The Way Ahead To truly harness the power of the private cloud, organizations must do three things: 1. Break Silos: IT teams need to break down their traditional silos to achieve business goals, such as better agility, cost optimization and regulatory compliance. They can do this by encouraging collaboration across different IT and business teams, facilitating better data-sharing and communication practices. 2. Invest In Cross-Skilled Teams: To accelerate private cloud adoption, organizations need to address persistent skill gaps with focused training and vendor collaboration. 3. Treat The Private Cloud As A Platform—Not A Project: Treating the private cloud as a platform is essential for unlocking its full potential, fostering continuous innovation and agility for the entire organization. This strategic shift moves beyond a one-off project mindset, focusing instead on building a foundational environment for ongoing development and value creation. IT leaders should also ask themselves a few questions to embrace this mindset. What workloads make sense to move back? Which ones should stay public? And how do we maximize both? The cloud conversation is evolving. From cost to compliance, and from generative AI to governance, the smart enterprise isn't choosing sides—it's choosing strategy. Forbes Communications Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?
