Latest news with #hybridmodels
ArabGT
10 hours ago
- Automotive
- ArabGT
Dodge Brings Back the V-8 Era with the 2026 Durango
The electric revolution isn't dead — but it's definitely slowing down. With EV sales growth cooling and drivers asking for more range, lower prices, and familiar refueling convenience, many automakers are pumping the brakes on their all-electric timelines. GM, BMW, Mercedes-Benz, Porsche — they're all rolling out fresh gas-powered and hybrid models to meet surging demand. In fact, hybrid production is set to jump by more than 40% in 2025. Fueling this trend are big political and regulatory shifts. The Trump administration has recently rolled back several key environmental measures — scrapping stricter federal emissions rules, removing California's special authority to set tougher standards, and easing penalties for lower fuel economy. The result? Carmakers suddenly have more breathing room to build powerful, less efficiency-focused engines without the same financial hit. Enter Dodge — and its all-in bet on V-8 power. While other brands are mixing hybrids into their lineup, Dodge is doubling down on raw muscle. For 2026, the Durango will completely drop the V-6 option, making a V-8 the only choice — no matter which trim you pick. Even the entry-level Durango GT now comes with the 5.7-liter V-8, delivering 360 horsepower, a big 65-hp bump over the old Pentastar V-6. Of course, that extra punch comes with a higher price tag — $44,490 to start, about $4,000 more than before. Move up to the R/T, and you get the mighty 6.4-liter V-8 392, pushing out 470 hp and 475 lb-ft of torque — that's a 115-hp leap compared to last year's 5.7-liter. Dodge says the R/T will stay 'under $50,000,' which likely puts it right around last year's $49,995 mark. And then there's the headline act: the 2026 Durango SRT Hellcat. Still packing the insane supercharged 6.2-liter V-8 with 710 hp and 645 lb-ft of torque, it now gets a refreshed Jailbreak package with six wheel designs, five interior color schemes, and more personalization options. Best of all? It's actually $6,000 cheaper this year, starting at $81,990. Orders for the GT and SRT Hellcat open August 13, with deliveries expected later in the year. The R/T AWD will follow shortly after.
Forbes
17-06-2025
- Business
- Forbes
AI Agents Alone Won't Automate The Enterprise—But They Can Make It Smarter
Tarun Raisoni, CEO & Cofounder at Gruve Inc. Enterprise systems follow the rules by design. For decades, platforms like ERP, CRM and HR software have powered the backbone of business. But today's enterprises face new pressure: Move faster, do more with fewer resources and adapt in real time. The problem? Most systems they rely on were built for structure, not flexibility. And while APIs were once thought to be the fastest way to integrate business platforms, they've added more complexity and rigidity. That's where AI agents come in. AI agents bring a new layer of intelligence to enterprise automation. They don't just follow instructions, they interpret context, make decisions and take action with minimal human input. From classifying documents to triaging support tickets, they handle the messy, unstructured work that traditional systems struggle with. But AI agents aren't a standalone solution. While they excel at flexibility, they fall short when managing tasks where consistency and compliance are non-negotiable. That's why the smartest companies aren't choosing between traditional systems and AI. They're building hybrid automation models that combine the best of both. AI adoption is no longer about if but how. With 92% of companies planning to increase AI investment over the next three years, the focus is shifting from deployment to impact. Unlike traditional enterprise platforms, AI agents adapt. They interpret unstructured data, respond to changing inputs and take autonomous action, coordinating across systems without detailed instructions. Think of them less as static tools, more like dynamic teammates who can reason and act. This flexibility shines in context-heavy functions: • Customer Service: Classifying tickets, suggesting responses or resolving issues based on sentiment and history • Finance: Scanning invoices, flagging anomalies and feeding clean data into ERP systems • IT: Monitoring infrastructure and triggering remediation in real time What makes AI agents powerful isn't just their autonomy; rather, it's their ability to reason using enterprise-specific data. They fill gaps that traditional systems can't. Tools like Salesforce's Agentforce recommend the next best actions within CRMs, while Glean's agents let teams automate workflows like onboarding without writing code. These agents don't replace core systems; they extend them, adding intelligence where structure alone falls short. AI agents thrive when they're not working in isolation. To work effectively, they need orchestration. They must connect to the enterprise systems they rely on and the human teams that guide them. This orchestration layer is what enables hybrid automation to function. It ensures that: • AI agents hand off tasks to structured systems (like ERPs or CRMs) for compliant execution. • Human reviewers can step in when needed, especially for exceptions or sensitive decisions. • Processes remain auditable, traceable and aligned with business logic. We already see this across HR systems, IT workflows and facilities teams through automatically generating accounts, scheduling orientation sessions and provisioning equipment. However, those actions are governed by a central orchestration layer that ensures each step follows company policy, involves human oversight where needed and keeps the process compliant and consistent across regions. This isn't just automation; it's automation with guardrails that enable AI agents to move from experimentation into production. The more enterprises deploy AI agents, the more they need to think of them as a new kind of digital workforce. That means: • Oversight: AI agents need clear boundaries on what they're allowed to do and when to escalate. • Training: Just like employees, agents need context: domain-specific vocabularies, evolving workflows and access to the correct data. • Performance Management: Teams should monitor, evaluate and refine agents over time to improve output and prevent drift. Security and trust are also critical. Unlike rule-based systems, agents can make novel decisions, which makes explainability, audit trails and human-in-the-loop (HITL) frameworks essential. AI agents aren't a replacement; they're a multiplier. Their real value lies in being woven into the systems and processes that businesses already trust. Hybrid automation makes that possible, blending structure with intelligence to unlock new levels of scale and speed. In a landscape defined by pressure to move faster and do more, this isn't just a smarter model. It's the one built to last. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Finextra
03-06-2025
- Business
- Finextra
Smarter Debt Collection Strategies: How Human and AI Collaboration is Transforming the Future of Col: By Naina Rajgopalan
In today's dynamic financial environment, lending businesses are under growing pressure. Rising delinquency rates, shifting customer expectations, and economic volatility are making traditional, one-size-fits-all collections strategies increasingly ineffective. To stay ahead, organizations must embrace smarter, more adaptive approaches to debt collection—ones that combine the power of technology with the irreplaceable nuance of human judgment. The most forward-thinking financial institutions are now adopting hybrid collections models—a strategic blend of human expertise and AI-powered automation. This approach not only boosts efficiency and recovery rates but also enhances customer satisfaction and ensures stronger compliance with regulatory standards. In this blog, we'll explore how this human-AI synergy is revolutionizing collections and why it's becoming essential for long-term success. Why Traditional Collection Strategies Are No Longer Enough Conventional debt recovery methods—manual outreach, static campaigns, and rigid scripts—may have worked in the past. But as debt portfolios grow and borrower behavior evolves, these strategies are hitting their limits. Lenders today face: Operational scalability issues Inconsistent outcomes Low engagement rates Compliance risks The path forward lies in moving beyond outdated approaches and embracing a smarter, more integrated system—one that merges human empathy with AI precision. Striking the Balance: Human vs. AI in Debt Collection Let's examine the unique strengths and limitations of human-only and fully automated collection approaches. Human-Only Collections Strengths: Empathy & Emotional Intelligence: Humans build trust through compassion and personalized engagement. Complex Judgments: Skilled collectors use instinct and experience to tailor flexible solutions. Negotiation Skills: Humans can navigate sensitive conversations and create win-win payment plans. Limitations: Limited Scalability: One agent can manage only a finite number of accounts. Inconsistency: Outcomes vary depending on individual collectors. Burnout Risk: High emotional load can reduce long-term productivity and morale. Fully Automated Collections Strengths: High-Speed Scalability: AI can manage thousands of accounts simultaneously. Consistency: Processes are standardized, reducing human error. Efficiency: Automation speeds up resolution time and operational throughput. Limitations: Lack of Nuance: AI often fails in emotionally complex or ambiguous scenarios. Impersonal Interactions: Automated messages can feel robotic and disengaging. Limited Flexibility: AI struggles with novel or unpredictable cases. The Hybrid Approach: Where Human Insight Meets AI Intelligence The sweet spot lies in integrating AI with human decision-making—leveraging the strengths of both to create a more effective, adaptive collections model. How It Works: AI-Augmented Human Interactions: Collectors are equipped with real-time AI recommendations, helping them tailor communication strategies, suggest payment plans, or flag regulatory concerns. Human-Guided AI Learning: Human feedback is used to continuously refine AI algorithms, improving accuracy and decision-making over time. Smart Case Distribution: AI automatically handles routine and low-risk accounts, while complex cases are routed to experienced human agents—ensuring optimal resource allocation. Why This Hybrid Model Works Adopting a human-AI collaboration model delivers measurable benefits across key business metrics: Higher Recovery Rates Organizations using hybrid strategies report a 15–25% improvement in collections, thanks to better personalization and smarter outreach. Enhanced Customer Experience Empathetic conversations, guided by AI insights, create more relevant and respectful interactions—boosting customer trust and loyalty. Greater Operational Efficiency Routine tasks like follow-ups, reminders, and document processing are automated, freeing up human agents for high-impact work. Stronger Compliance Real-time regulatory checks embedded in AI systems help minimize legal risks while enabling human oversight in complex cases. Improved Employee Satisfaction Collectors experience less burnout and more job satisfaction when they can focus on meaningful interactions rather than repetitive tasks. AI in Action: Industry Trends According to TransUnion research, 57% of debt collection agencies have already integrated AI into their operations—primarily for account segmentation and predictive analytics. The adoption of self-service portals has also surged, growing from 79% to 88% in 2024 alone. This trend signals a clear shift toward smarter, more customer-centric collection practices across the industry. Recommend Read: "Good debt vs. bad debt in India: How to make smart borrowing decisions" How to Implement a Smarter Debt Collection Strategy Here's a roadmap to help your organization adopt a hybrid collections model: Evaluate Current Processes: Identify high-effort, low-value activities suitable for automation and those needing human intervention. Define a Clear Hybrid Strategy: Set rules for AI-human collaboration, including escalation criteria and feedback loops. Adopt the Right Technology: Choose platforms with real-time analytics, configurable workflows, and robust compliance features. Train Your Team: Educate collectors on how to effectively use AI tools to enhance their performance—not replace it. Measure, Learn, Improve: Monitor KPIs, gather user feedback, and continuously optimize your model for better outcomes. Looking Ahead: The Future of Collections is Collaborative In the evolving world of finance, the most successful lenders will be those who can blend human empathy with AI precision. The question is no longer whether to use AI in collections—but how to use it most effectively in tandem with human expertise. By adopting smarter, hybrid debt collection strategies, your organization can: Reduce delinquency Enhance customer experience Improve compliance Boost operational performance The future of collections is not human or AI—it's human and AI working together to achieve smarter outcomes. Recommend Read: "Role of Artificial Intelligence (AI) in debt collections" Frequently Asked Questions (FAQs) 1. What are smarter debt collection strategies? They combine AI and human expertise to personalize outreach, optimize efficiency, and ensure regulatory compliance—essential in today's rapidly changing lending environment. 2. How does human-AI collaboration improve recovery rates? It pairs data-driven targeting with empathetic human conversations, increasing customer engagement and driving better resolutions. 3. What is a hybrid collections model? It's a strategic approach where AI handles routine tasks while humans focus on complex negotiations—creating a balanced, scalable workflow. 4. Can AI replace human collectors entirely? No. AI excels in automation but lacks emotional intelligence. Human collectors remain vital for sensitive cases and complex decision-making. 5. How can my business implement this strategy? Start with process evaluation, adopt intelligent tools, train your team, and iterate based on performance data and feedback.
