#digital agent automation

[entrepreneurial1era]

Agentic AI: The Rise of Autonomous Digital Assistants

How Smart Autonomous Agents Are Redefining the Human-AI Relationship

Introduction: A New Era in Artificial Intelligence

Artificial Intelligence (AI) is no longer a distant concept confined to sci-fi novels or the realm of elite researchers. Today, AI is seamlessly woven into our daily lives powering voice assistants like Siri, recommending content on Netflix, detecting fraud in banking systems, and even helping doctors diagnose illnesses faster and more accurately.

But we are now entering a transformative phase in the evolution of AI, one that promises not just efficiency but autonomy, adaptability, and even decision-making capability. At the forefront of this evolution is a new class of systems known as Agentic AI, often referred to as Autonomous Digital Assistants or AI agents.

These next-generation AI systems are not limited to pre-defined scripts or simple automation. Instead, they exhibit goal-oriented behavior, can take independent actions, adapt to feedback, and operate across multiple platforms to complete complex tasks. From managing business operations to coding, designing, researching, and even negotiating, Agentic AI is set to redefine how we work, live, and think.

Why Does This Matter Now?

The rise of Agentic AI is fueled by the rapid advancement of machine learning, natural language processing (NLP), and neural networks. Leading AI models like GPT-4, Claude, and Gemini by Google are already demonstrating capabilities that blur the line between tool and collaborator.

These AI agents aren’t just passive responders they can:

Analyze and interpret vast amounts of real-time data

Make decisions based on defined objectives

Learn from interaction and optimize over time

Perform multi-step tasks autonomously across platforms 

In practical terms, this means we could soon delegate entire workflows from scheduling meetings and writing reports to launching marketing campaigns and conducting customer service to intelligent digital assistants.

A Glimpse Into the Future

Imagine a virtual business partner who not only helps you stay organized but also negotiates contracts, optimizes your website SEO, handles email outreach, and reports performance metrics all without your daily input. This is no longer fiction thanks to innovations in agentic architectures like Auto-GPT, BabyAGI, and tools being developed by OpenAI, this reality is quickly becoming mainstream.

What This Means for You

Whether you're a startup founder, corporate executive, creative freelancer, or student, the rise of Agentic AI signals a massive shift in digital productivity and human-AI collaboration. Understanding how these systems work, their limitations, and their ethical implications will be essential in the coming years.

Stay tuned as we explore how Agentic AI is shaping the future of:

Work and productivity

Entrepreneurship

Customer experience

Education and learning

Human decision-making

Want to stay ahead of the AI curve? Subscribe to Entrepreneurial Era Magazine to get weekly insights on AI-driven innovation, business strategies, and the tools reshaping our world.

What Is Agentic AI?

Agentic AI refers to a new class of artificial intelligence systems that act as autonomous digital agents capable of independently executing tasks, making decisions, and learning from outcomes without constant human oversight. These systems are a significant evolution beyond traditional AI tools like Siri, Alexa, or Google Assistant, which require direct prompts for every action.

Key Concept: Agentic AI possesses "agency" the ability to act on its own in pursuit of a defined goal.

How Agentic AI Works

Unlike rule-based or reactive systems, Agentic AIs:

Plan and prioritize tasks using large language models (LLMs) and advanced reasoning algorithms

Initiate actions proactively based on changing input or context

Monitor and optimize ongoing processes without manual triggers

Adapt to feedback through reinforcement learning or user corrections

Collaborate across systems to accomplish multi-step workflows

This autonomy is what distinguishes Agentic AI from traditional AI. While older systems wait for commands, agentic models can determine “what to do next”, often in real-time.

Real-World Examples of Agentic AI

Here are some powerful tools and frameworks already showcasing the power of Agentic AI:

Auto-GPT: An experimental open-source project that chains GPT-4 calls together to autonomously complete tasks

BabyAGI: A lightweight AI agent that uses a task management loop to accomplish goals

OpenAI’s GPT Agents: Part of OpenAI's Assistant API, these agents can execute code, manage files, and use external tools

Meta’s LLaMA Agents: An open-source effort pushing the boundaries of multi-agent collaboration

From Tools to Teammates

The fundamental shift with agentic systems is that AI is no longer just a tool it becomes a collaborator. These agents can:

Work independently in the background

Schedule and send emails based on intent

Analyze and summarize reports

Interact with APIs and databases

Monitor key metrics and trigger actions based on thresholds 

This shift has vast implications for entrepreneurs, marketers, developers, and enterprise teams, making work faster, smarter, and more human-centric.

Why It Matters

As businesses increasingly adopt automation and AI-driven workflows, the value of Agentic AI lies in:

Scalability: They handle thousands of micro-tasks in parallel

Productivity: Human teams are freed up for creative and strategic work

Cost-efficiency: Tasks traditionally requiring manual labor can be automated

Consistency: No missed follow-ups or human fatigue 

The rise of agentic systems also aligns with major trends in autonomous agents, self-learning AI, and multi-modal interaction the future of digital workspaces.

Learn more about the difference between Generative AI and Agentic AI from Stanford HAI and how it's expected to shape productivity in the next decade.

The Technological Leap Behind Agentic AI

The rise of Agentic AI is not a coincidence, it's the result of rapid advances in multiple fields of artificial intelligence and computing. These systems are driven by a convergence of technologies that allow machines to think, act, and evolve much like human collaborators.

1. Large Language Models (LLMs)

The foundation of agentic AI lies in powerful large language models like OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini. These models can:

Understand complex instructions

Generate human-like text

Analyze unstructured data

Hold multi-turn conversations with contextual awareness 

LLMs give agents the language understanding and generation power to reason and communicate independently.

2. Reinforcement Learning and Agentic Planning

Reinforcement learning techniques like RLHF (Reinforcement Learning from Human Feedback) and goal-based optimization equip agentic systems with the ability to:

Set internal objectives

Learn from trial and error

Optimize decision-making over time 

This makes agents smarter with each interaction, similar to how humans learn through experience.

3. Memory & Long-Term Context

Unlike traditional AI that operates in isolated prompts, agentic systems use memory modules to:

Track goals and user preferences

Recall past conversations and actions

Build on previous outcomes to refine future performance 

For example, tools like LangChain and AutoGPT include memory systems that make agents feel persistent and context-aware, bridging the gap between sessions.

4. APIs and System Integration

Thanks to seamless integration with APIs, webhooks, and automation platforms, Agentic AI can:

Schedule meetings (e.g., via Calendly)

Send emails through Gmail or Outlook

Pull data from CRMs like HubSpot

Update spreadsheets or dashboards

This connectivity turns AI agents into autonomous digital workers embedded across tools and platforms you already use.

5. Multi-Modal Data Understanding

New-generation agents are not limited to text. With multi-modal capabilities, they can process:

Images (object recognition, design feedback)

Audio (voice commands, transcription)

Video (gesture recognition, editing suggestions)

Code (debugging, deployment assistance)

Projects like OpenAI's GPT-4o and Google’s Gemini 1.5 are pushing the boundaries here, enabling agents to perceive and act across sensory input channels.

Continuous Learning & Evolution

Perhaps the most transformative leap is how agentic AIs grow over time. They:

Track long-term goals

Adjust their strategies

Learn from failed outcomes

Reuse patterns that work 

This adaptive behavior, fueled by feedback loops and self-correction, mirrors key traits of human cognition making agentic systems more than tools; they become intelligent teammates.

Use Cases of Agentic AI: Beyond Virtual Assistants

Agentic AI is quickly becoming one of the most transformative tools in both consumer and enterprise landscapes. These AI-powered digital agents go far beyond simple voice commands or chatbot interactions; they're redefining how work gets done across sectors. From automating business operations to revolutionizing healthcare and education, Agentic AI applications are unlocking efficiency, creativity, and personalization at scale.

Business & Marketing: The Next-Gen Workforce

In the business world, agentic AI is functioning as a full-stack digital worker. These intelligent agents can:

Automate CRM tasks by managing leads, sending follow-up emails, and updating pipelines in tools like HubSpot or Salesforce.

Draft personalized marketing content for emails, blogs, or ad campaigns using platforms like Jasper AI or Copy.ai.

Schedule and coordinate meetings across time zones by integrating with calendars and apps like Calendly.

Conduct competitive analysis and summarize market trends in real time, giving businesses a strategic edge.

Software Development: AI That Codes & Maintains

For developers, agentic AI acts as a proactive coding partner. It can:

Debug errors autonomously using tools like GitHub Copilot.

Generate new features based on project specs and user feedback.

Run performance tests, monitor infrastructure health, and auto-scale cloud resources.

Agents can even integrate into CI/CD pipelines to push updates and manage deployment cycles without human intervention.

Education: Personalized, Self-Updating Tutors

In the realm of education, agentic AI is redefining personalized learning. These digital tutors can:

Adapt to a student’s pace and learning style using real-time analytics.

Assign dynamic exercises that reinforce weak areas.

Grade assignments, provide feedback, and curate study materials aligned to the curriculum.

Help teachers reduce administrative load while increasing student engagement.

Explore how Khanmigo by Khan Academy is already pioneering this approach using GPT-based tutoring agents.

Healthcare: Real-Time Patient Support

In healthcare, agentic AI offers solutions that improve both efficiency and patient outcomes:

Triage symptoms and suggest next steps based on input and health records.

Automate follow-up scheduling and prescription reminders.

Monitor vital metrics and send alerts for potential risks in chronic care patients.

Agents can act as digital nurses, assisting medical professionals with real-time insights while improving access for patients especially in underserved areas. Check out how Mayo Clinic is exploring AI-driven care pathways using autonomous agents.

Creative Industries: Empowering Human Imagination

Agentic AI is also reshaping the creative world, helping artists, writers, designers, and marketers create faster and smarter. These tools can:

Draft blog posts, scripts, or story outlines for content creators.

Generate visual ideas or even full designs using tools like Adobe Firefly.

Offer real-time editing suggestions, freeing up time for deeper storytelling or branding work.

Create music, edit videos, or write code snippets for creative tech solutions.

This fusion of human creativity and AI support leads to faster production cycles and higher-quality output.

From Assistance to Collaboration

One of the most profound shifts that agentic AI brings is the transition from tool to teammate. Where older AI models acted like sophisticated calculators or search engines, the new generation behaves more like colleagues who understand context, maintain continuity, and offer proactive input. These agents don’t just wait for tasks, they suggest them. They don’t merely execute, they optimize and innovate.

This changes the human-machine relationship fundamentally. It opens the door to collaborative intelligence, where humans provide vision and judgment, while AI agents handle execution and refinement. The result is a synergistic model where productivity, creativity, and efficiency are amplified.

Challenges and Ethical Considerations

Despite its potential, the rise of agentic AI raises important ethical and operational questions. Trust becomes a central issue. How do we ensure that autonomous systems make decisions aligned with human values? Who is accountable when an AI agent makes a costly mistake? As these agents become more autonomous, there is a pressing need for transparency, auditability, and control mechanisms to prevent unintended consequences.

There’s also the risk of over-dependence. If individuals and organizations begin to rely too heavily on agentic AI, critical thinking and hands-on skills may decline. Furthermore, job displacement in certain roles is inevitable, which necessitates rethinking how education and workforce development can evolve alongside AI.

Privacy is another concern. Autonomous assistants often require access to sensitive data emails, calendars, and financial records to function effectively. Ensuring that this data is used ethically and securely is paramount. Regulation, informed design, and public awareness must evolve in step with these technologies.

The Future: Where Do We Go From Here?

Agentic AI is still in its early stages, but the trajectory is clear. As models become more capable and integration becomes seamless, these digital agents will increasingly handle complex workflows with minimal oversight. The near future could see agents managing entire departments, running online businesses, or supporting elderly individuals with daily tasks and health monitoring.

Imagine logging off work and knowing your AI teammate will monitor your email, respond to routine inquiries, update your CRM, and prepare your reports for the next day all without a single prompt. That’s not science fiction, it's the very real promise of agentic AI.

What this future demands from us is not fear, but responsibility. We must guide the development of these technologies to serve human goals, amplify ethical intelligence, and build a world where AI doesn’t just mimic thought but supports human flourishing.

Conclusion: Empowering the Human Mind Through Agentic AI

The rise of agentic AI signals a fundamental shift in the way we interact with technology. These autonomous digital agents are not here to replace human intelligence, they are here to augment it. By moving beyond simple, reactive tools to proactive and context-aware collaborators, agentic AI extends human capability in areas ranging from decision-making to creativity, productivity, and innovation.

This evolution marks the next chapter of the AI revolution, one where machines are not merely assistants, but intelligent teammates capable of managing complex workflows, learning from feedback, and evolving with us.

As we stand at the edge of this new era, the most important question is no longer “Will agentic AI change our lives?” it’s “How will we choose to harness it?”

With thoughtful design, strong ethical frameworks, and a focus on human-AI collaboration, these technologies can:

Empower entrepreneurs and startups to do more with less.

Revolutionize industries like healthcare, education, and creative media.

Enhance learning, innovation, and accessibility on a global scale.

Want to go deeper? Explore how OpenAI’s AutoGPT and Google’s Project Astra are shaping the next generation of intelligent agents.

Final Call to Action

Are you ready to embrace the future of AI?

Subscribe to Entrepreneurial Era Magazine for more practical insights, case studies, and strategies on integrating Agentic AI into your business, career, or creative journey.

Let’s shape the future together with AI as our co-pilot.

FAQs

What is Agentic AI, and how is it different from regular AI? Agentic AI refers to systems that can operate independently, make decisions, and pursue goals without continuous human guidance. Unlike traditional AI that reacts to commands, Agentic AI takes initiative, plans tasks, and adjusts its behavior based on outcomes. Think of it like a digital assistant that doesn’t just wait for instructions but proactively helps you manage your day, automate work, or optimize decisions. This makes Agentic AI ideal for complex workflows, business automation, and even personal productivity offering a significant upgrade over static or rule-based AI models.

How can Agentic AI benefit my small business? Agentic AI can automate repetitive tasks, manage customer interactions, and even analyze business data to improve operations. For instance, it can handle scheduling, automate emails, manage inventory alerts, and recommend actions based on real-time data. Unlike basic automation tools, Agentic AI acts more like a virtual employee identifying bottlenecks, adjusting priorities, and learning from each decision. This reduces human error, saves time, and allows small business owners to focus on strategy and growth instead of operations. The longer it runs, the smarter and more efficient it becomes.

Can Agentic AI integrate with existing tools like CRMs or project managers? Yes, most Agentic AI platforms are designed to work with existing software like CRMs, task managers, email platforms, and data tools. Integration may involve APIs, plugins, or native connectors that allow the AI to read, analyze, and act on your business data. Once connected, the AI can schedule follow-ups, organize leads, assign tasks, and suggest process improvements without manual input. This seamless integration empowers teams to operate more efficiently, using the tools they already know supercharged by intelligent automation.

Is Agentic AI safe to use with sensitive information? Agentic AI systems are generally built with advanced encryption, access controls, and compliance with data protection regulations (like GDPR or HIPAA, depending on the use case). However, safety depends on the platform you choose. Reputable providers ensure that the AI only accesses necessary data and follows strict protocols for storing and processing sensitive information. Always verify a platform’s security standards, opt for role-based access, and audit activity logs regularly. When implemented correctly, Agentic AI can actually improve security by reducing human error in data handling.

Do I need technical skills to use Agentic AI effectively? No, most modern Agentic AI platforms are designed with user-friendly interfaces, guided onboarding, and natural language instructions. You don’t need to code or understand machine learning. For example, you can ask the assistant to “automate follow-ups for new leads” or “summarize this week’s tasks.” Many systems even learn your preferences over time, making suggestions tailored to your workflow. However, understanding your business processes and goals clearly is important because the AI works best when it knows what outcomes you're aiming to achieve.

How does Agentic AI learn and improve over time? Agentic AI uses machine learning algorithms that analyze data, decisions, and results to improve its performance over time. It tracks patterns, adapts to user preferences, and optimizes processes based on feedback loops. For instance, if you reject certain suggestions, it learns to adjust future recommendations accordingly. Some advanced Agentic AIs also conduct trial-and-error planning, known as reinforcement learning, to fine-tune their strategies. This makes them highly effective in dynamic environments where flexibility, personalization, and long-term optimization are valuable.

Can Agentic AI replace human employees? Agentic AI is designed to augment human workers, not replace them. While it can automate repetitive or data-heavy tasks, humans are still essential for creativity, judgment, and emotional intelligence. For example, the AI might prepare reports, manage appointments, or send follow-ups, but humans will still lead decision-making, handle complex negotiations, and ensure alignment with business values. Think of Agentic AI as a digital teammate, one that handles the busywork so your team can focus on innovation, strategy, and relationship-building.

What industries benefit most from Agentic AI? Virtually every industry can benefit from Agentic AI, but it's especially transformative in areas like customer service, sales, marketing, healthcare, logistics, and finance. For example, in healthcare, an Agentic AI can manage patient follow-ups, insurance verification, and medical reminders. In e-commerce, it can optimize inventory, automate promotions, and analyze customer behavior. Its strength lies in cross-functional utility wherever workflows are repeatable and data-driven, Agentic AI can create massive efficiencies and improve decision quality without ongoing micromanagement.

What should I consider before implementing Agentic AI? Before adopting Agentic AI, define your goals clearly: Do you want to automate tasks, improve decision-making, or scale operations? Evaluate your current workflows to identify areas where autonomy adds the most value. Choose a platform that supports integration with your existing tools, offers robust security, and aligns with your industry needs. Also, prepare your team for collaboration with AI by promoting a culture of experimentation and learning. A thoughtful implementation ensures the AI complements human roles, enhances productivity, and delivers real ROI.

What is the future of Agentic AI? The future of Agentic AI lies in more human-like decision-making, proactive problem solving, and deeper collaboration with both humans and other AIs. We're moving toward AI agents that understand context, maintain long-term goals, and self-optimize with minimal input. In the near future, these assistants will run entire business functions, conduct autonomous research, negotiate contracts, or even design products. They’ll act as intelligent extensions of individuals and organizations blending autonomy with accountability. This evolution marks a shift from using tools to partnering with intelligent agents that think and act independently.

[certivo]

Digital Turnaround: How Tech Transforms Legacy Companies | Kunal Chopra on Shift AI

In this episode of the Shift AI Podcast, Certivo CEO Kunal Chopra joins Boaz Ashkenazy (CEO of Augmented AI Labs) to explore how legacy, “pen-and-paper” companies can undergo complete digital transformation. From eliminating manual processes to embedding AI agents directly into workflows, Kunal shares how he led old-school organizations into the future using technology and operational redesign.

At Certivo, AI isn’t just a tool — it’s a team member. CORA, our AI compliance agent, collaborates with human teams to automate the tedious, surface what matters, and help manufacturers stay always compliant and always market-ready.

This episode is a must-watch for anyone leading change in traditional industries, compliance management, or AI-driven operations.

🎧 Watch the full podcast: https://www.youtube.com/watch?v=KDTCN5Jyjfw

🌐 Learn more about Certivo: https://www.certivo.com/

[datapeakbyfactr]

Swimming in a Sea of Data: From Overload to Opportunity

Data has now become both a business’s greatest asset and its most formidable challenge. It’s the new oil, but like crude oil, raw data is messy, unstructured, and often unusable without the right systems in place. 

Consider this: in 2012, IBM reported that the world was generating 2.5 quintillion bytes of data each day. Fast forward to 2025, and we’re creating 2.5 quintillion bytes every single minute. This explosive growth is staggering, and for most organizations, overwhelming. 

Today, over 80% of enterprise data is unstructured, buried in emails, PDFs, videos, audio files, documents, chat logs, and more. It’s scattered across systems, departments, cloud drives, and inboxes, making it impossible to manage through manual processes. The result? Businesses are drowning in information, unable to find or use the data that matters most. 

We’ll discuss why unstructured data is such a massive problem, how it poses risks to organizational health, and what you can do through smart, scalable data management strategies to turn chaos into competitive advantage. 

The Hidden Dangers of Unstructured Data Overload 

Unstructured data is any data that does not have a predefined model or schema. Unlike structured data (think spreadsheets or SQL databases), unstructured data is messy, varied, and hard to index or analyze using traditional tools. 

Why It’s a Problem: 

Data Silos Are Everywhere  Information is often scattered across fragmented systems; CRMs, email inboxes, file shares, messaging platforms, and individual desktops. Without integration, these silos hinder collaboration, duplicate efforts, and obscure valuable insights. 

Time Waste and Productivity Loss Employees spend 20–30% of their workweek just searching for information, according to IDC. That translates to roughly 8–12 hours per employee, per week. In a 500-person organization, this results in over $2 million annually in lost productivity. 

Data Security and Compliance Risks Unmonitored, unstructured data significantly increases the risk of regulatory non-compliance and data breaches. The average cost of a data breach has reached $4.45 million, according to IBM. These incidents bring additional costs in legal fees, operational disruption, and long-term damage to reputation and customer trust. 

Inaccurate Analytics Poor data quality caused by duplicates, outdated entries, or inconsistency leads to flawed analytics and unreliable AI outcomes. Gartner estimates that the financial impact of bad data costs organizations an average of $12.9 million per year due to misguided decisions and wasted resources. 

Missed Strategic Value Buried within emails, customer reviews, support tickets, and reports are key insights that could influence strategic direction. Without tools to unlock these insights, companies risk losing competitive ground to more data-savvy organizations. 

The Case for Proactive Data Management 

To combat these issues, businesses must embrace enterprise-wide data management strategies; not as a tech upgrade, but as a strategic imperative. 

At the core of this transformation are several key pillars: 

1. Data Governance 

Establish rules, roles, and responsibilities for how data is managed, accessed, and used. Governance ensures compliance and provides a framework for accountability. 

2. Metadata Management 

Metadata (data about data) helps catalog, classify, and make sense of vast content repositories. With strong metadata, you can track origin, context, usage, and structure of data assets. 

3. Master Data Management (MDM) 

MDM ensures consistency and accuracy of core data across all systems (like customer or product data). It eliminates duplication and provides a single source of truth. 

4. Data Quality & Cleansing 

Identify and fix inconsistencies, duplicates, and errors. High-quality data is essential for reliable analytics and AI. 

5. Centralized Repositories 

Move from fragmented storage to centralized, searchable data lakes or warehouses. Enables better access, security, and data lifecycle management. 

Using AI to Tame the Unstructured Data Monster 

Managing unstructured data manually is no longer feasible. Fortunately, AI and machine learning are now powerful allies in imposing order on the chaos. 

How AI Transforms Data Management 

Automatic Classification and Tagging 

Natural language processing (NLP) tools can scan and automatically categorize documents, emails, and files by subject, department, or sensitivity level. This automation drastically reduces manual sorting and accelerates digital organization. 

Efficiency Gain: Up to 80% reduction in manual data classification time, enabling staff to focus on strategic tasks rather than clerical work. 

Content Extraction 

AI-driven tools use optical character recognition (OCR) and speech-to-text technology to extract relevant information from documents, images, videos, and audio files. 

Cost Impact: Organizations can reduce document handling costs by as much as 70%. Processes like onboarding, claims processing, and invoice management become 3–5 times faster. 

Semantic Search 

Unlike traditional keyword search, semantic search understands the context and intent behind a query. It retrieves the most relevant documents (even when the phrasing differs) leading to significantly faster access to needed information. 

Time Savings: Cuts average search time by 50–60% and reduces duplicated work across departments. 

Sentiment and Topic Analysis 

AI can analyze customer-facing content like support tickets, emails, and reviews to extract sentiment and detect patterns in feedback, complaints, or requests. 

Strategic Value: Helps companies prioritize product improvements, reduce churn, and proactively address customer issues. Also supports better alignment between customer sentiment and business priorities. 

Anomaly Detection 

AI algorithms monitor data access and usage patterns to identify irregular behaviour such as unauthorized access attempts or suspicious downloads before they become serious breaches. 

Risk Mitigation: Reduces incident response times by up to 90% and helps prevent financial losses associated with fraud or data misuse. 

“Companies have tons and tons of data, but success isn’t about data collection, it’s about data management and insight.”

— Prashanth Southekal, Business Analytics Author & Professo

Real-World Impact: From Data Swamp to Strategic Insight 

Financial Services 

A mid-sized regional bank was facing serious delays and inefficiencies in its customer onboarding process. New customer documents such as proof of identity, income verification, and compliance forms were arriving in multiple formats via email, fax, and scanned PDFs. Employees were manually reviewing and uploading them into the system, often duplicating efforts across departments. 

The Solution: 

The bank deployed an AI-powered document management system that used natural language processing (NLP) and optical character recognition (OCR) to automatically extract key information from incoming documents. The system then categorized and routed files based on compliance requirements and customer profiles. 

The Result: 

Onboarding time reduced by 50% 

Manual document handling decreased by 70% 

Improved audit readiness and regulatory compliance 

Better customer experience through faster service and reduced paperwork errors 

Manufacturing 

A global manufacturing firm was grappling with unexpected equipment failures across its production lines. While structured data from sensors was being analyzed regularly, thousands of unstructured maintenance logs, technician notes, and incident reports were being ignored due to lack of standardization. 

The Solution: 

Using AI and machine learning, the company processed years of maintenance notes and equipment logs to identify recurring keywords, root cause patterns, and correlations with sensor anomalies. NLP was used to classify issues, link them to specific machines or parts, and rank their criticality. 

The Result: 

30% reduction in unplanned downtime 

Identification of high-risk components before failure 

Maintenance schedules optimized based on real failure trends rather than fixed intervals 

A unified dashboard displaying both structured and unstructured diagnostics for better visibility 

Healthcare 

A hospital system serving thousands of patients annually found that much of its most valuable clinical information such as patient symptoms, treatment outcomes, and physician notes, were buried in unstructured electronic health records (EHRs). These narrative-based inputs were not being utilized in broader health analytics or treatment optimization efforts. 

The Solution: 

By integrating advanced NLP models trained on medical terminology, the hospital was able to extract structured insights from physician notes, diagnostic reports, and patient history narratives. These were then fed into a decision support system to assist doctors in real time. 

The Result: 

Enhanced diagnostic accuracy and treatment recommendations 

Earlier identification of at-risk patients based on symptom patterns 

Reduction in duplicated tests and procedures 

Accelerated medical research through improved data accessibility and linkage 

No matter your industry, if your business generates large volumes of documents, emails, support tickets, or reports, there’s likely a goldmine of insight hiding in plain sight. 

Building a Sustainable Data Management Strategy 

Transitioning from data chaos to clarity requires more than buying the latest tool—it requires cultural and operational change. 

Key Steps for Implementation: 

Audit Your Data Identify where data resides, what formats it’s in, and who uses it. Evaluate current risks and opportunities. 

Define Goals Are you aiming to improve searchability? Reduce compliance risk? Drive analytics? Clarify your priorities. 

Choose the Right Tools Use platforms that integrate AI/ML, allow centralized storage, and support automation. 

Upskill Teams Train employees in data literacy and involve them in crafting data management policies. IT and business units must collaborate—this is not just a tech project. 

Monitor & Evolve Data strategies aren’t static. Continuously monitor quality, usage, and security—and adapt as your business grows. 

The exponential growth of unstructured data isn’t going to slow down, it will only accelerate. For businesses, the choice is clear: either continue to drown in a sea of disconnected data or learn to ride the waves with strategy, tools, and intent. 

When managed well, data becomes a powerful force, enabling faster decisions, stronger customer experiences, and deeper insights. 

So, are you managing your data or is your data managing you? 

Take action today to build a smarter, safer, and more strategic approach to data management before the next wave hits. 

Learn more about DataPeak:

[lk-freelance]

Why Every Real Estate Agent Needs Zoho CRM to Close More Deals

As a real estate agent, your day is packed with open houses, showings, client calls, paperwork, and marketing tasks. The one thing you don’t have time for? Losing leads or missing follow-ups. That’s where a reliable real estate CRM like Zoho CRM becomes your secret weapon. What Is Zoho CRM? Zoho CRM is a cloud-based customer relationship management tool designed to help agents like you…

[authorebookman]

Unlock your time and profits with E1ULife.com—your all-in-one AI business partner. Automate daily tasks, convert more leads, and grow your brand effortlessly—no coding or experience needed. Whether you’re new or scaling up, our tools run your business like a pro. Visit now and work smarter, not harder.

[precallai]

Migrating Legacy Contact Centers to Smart AI Solutions

Introduction

In an era dominated by digital transformation, businesses are rapidly shifting from traditional, on-premise contact center systems to smart, AI-powered platforms. This migration is not merely a trend—it’s a strategic imperative. Legacy contact centers, while once reliable, often struggle to keep up with the demands of modern customers who expect seamless, real-time, omnichannel support. Smart AI solutions offer a scalable, efficient, and intelligent approach to managing customer interactions while significantly improving the overall customer experience (CX).

Why Legacy Contact Centers Fall Short

Legacy contact centers were built to handle voice calls through physical infrastructure and manual workflows. These systems are rigid, expensive to maintain, and lack the flexibility needed for today’s fast-paced digital environment. Some key limitations include:

Limited scalability

High operational costs

Minimal integration with digital channels

Lack of real-time data analytics

Inability to support remote agents effectively

Moreover, legacy systems are often siloed, making it difficult to provide a unified customer experience across channels such as email, chat, social media, and messaging apps.

The Case for AI-Powered Contact Centers

AI contact centers leverage technologies like machine learning, natural language processing (NLP), and robotic process automation (RPA) to enhance and automate customer interactions. These platforms can intelligently route queries, provide self-service options, and analyze customer sentiment in real time.

Key benefits of migrating to a smart AI solution include:

Enhanced customer experience (CX) with personalized, context-aware interactions

24/7 availability through AI-powered virtual agents and chatbots

Omnichannel support that unifies communication across voice, email, chat, SMS, and social platforms

Cost savings through intelligent automation and reduced reliance on live agents

AI-driven analytics for better decision-making and performance optimization

Key Technologies Powering Smart AI Contact Centers

Natural Language Processing (NLP) NLP enables AI to understand and respond to human language more effectively. It powers chatbots, virtual assistants, and intelligent IVRs, making interactions more human-like and intuitive.

Machine Learning and Predictive Analytics Machine learning models analyze historical data to predict customer behavior, enabling proactive service and intelligent routing of interactions to the right agents or systems.

AI-Driven Automation Robotic process automation (RPA) handles repetitive tasks such as data entry, verification, and ticket generation, allowing agents to focus on complex issues.

Cloud-Based Infrastructure Modern AI contact centers are built on the cloud, enabling easy scalability, remote agent support, and seamless updates without downtime.

Speech Recognition and Sentiment Analysis These tools analyze tone and emotion during voice interactions, helping organizations adapt responses in real time to improve outcomes.

The Migration Journey: Key Steps and Best Practices

Migrating to a smart AI contact center requires strategic planning and execution. Here’s a high-level roadmap:

1. Assess Your Current State

Begin with a comprehensive audit of your existing contact center infrastructure, workflows, customer pain points, and technology stack. Identify gaps in CX, agent productivity, and system performance.

2. Define Your Objectives

Clearly define your goals—whether it's improving response times, enabling omnichannel support, or reducing costs through automation. These objectives will guide technology selection and implementation strategy.

3. Choose the Right AI Contact Center Platform

Look for platforms that offer:

Seamless cloud migration

Integration with your existing CRM and support systems

AI-powered virtual agents and intelligent routing

Real-time dashboards and AI-driven analytics

Security and compliance features

Top vendors include Amazon Connect, Google Cloud Contact Center AI, Genesys Cloud, and Five9.

4. Plan for Integration and Data Migration

Ensure that customer data, interaction history, and knowledge bases are migrated securely and accurately. APIs and middleware tools can help integrate legacy systems during the transition phase.

5. Train AI Models and Agents

Leverage historical interaction data to train your virtual assistants and automation tools. Concurrently, provide your human agents with training on new tools and AI-assisted workflows.

6. Monitor, Optimize, and Iterate

Post-migration, continuously monitor system performance, customer feedback, and agent productivity. Use AI-driven analytics to identify areas for improvement and adapt quickly.

Addressing Common Challenges

Data Privacy and Compliance: Ensure your new platform adheres to regulations such as GDPR, HIPAA, or PCI-DSS. AI systems should handle sensitive information responsibly.

Change Management: Prepare your team for the cultural shift. AI is meant to augment—not replace—human agents. Empower them with AI tools to work more efficiently.

Integration Complexity: Work with experienced technology partners or consultants who specialize in cloud migration and AI implementation to reduce friction during integration.

Real-World Impact: AI in Action

A leading telecom company replaced its legacy call center with a cloud-based AI solution. The results included:

35% reduction in average handling time (AHT)

50% increase in first contact resolution (FCR)

40% improvement in customer satisfaction (CSAT)

60% of queries handled by AI-powered virtual agents

This transformation not only enhanced operational efficiency but also empowered agents with real-time insights and support tools, allowing them to focus on high-value interactions.

The Future of AI Contact Centers

As generative AI and real-time voice synthesis continue to evolve, smart contact centers will become even more sophisticated. We can expect:

Hyper-personalized customer journeys driven by behavioral analytics

Real-time agent assist tools offering prompts and next-best actions

Voice bots with near-human conversational capabilities

Deeper integration with enterprise systems like ERP and sales platforms

The AI contact center is no longer a futuristic concept—it is today’s strategic advantage.

Conclusion

Migrating legacy contact centers to smart AI solutions is a transformative move that enables organizations to meet the demands of today’s digital-first customers. By embracing AI-powered tools, businesses can deliver superior customer experiences, improve operational efficiency, and gain a competitive edge.

This transition, while complex, can be managed effectively with the right strategy, technology, and partners. As AI continues to evolve, the future of customer engagement lies in intelligent, adaptive, and scalable contact center platforms.

[ailatestupdate]

Adobe Orchestrator AI is a cutting-edge AI agent designed to revolutionize automation, creativity, and digital workflows. With advanced machine learning, it seamlessly integrates AI-driven processes, enhancing efficiency for businesses and creators. Stay ahead with the future of AI-powered automation!

[aivoicesvcs1]

AI Voice Services by Think AI: Revolutionising Business Communication

AI Voice Services by Think AI is revolutionising the way businesses interact with their customers by offering advanced AI-powered voice solutions tailored for seamless automation, customer engagement, and operational efficiency. Designed to integrate effortlessly into existing systems, Think AI’s voice services provide businesses with a scalable and intelligent approach to automated communication.

From AI voice agents handling customer queries to automated appointment scheduling, AI-powered call routing, and personalised voice interactions, Think AI’s services are built to enhance customer experiences while reducing costs. By leveraging natural language processing (NLP) and deep learning, these AI-driven voice solutions enable human-like interactions, ensuring smooth and natural conversations.

Think AI's voice automation solutions are ideal for businesses in customer service, healthcare, finance, retail, and beyond, providing 24/7 availability and real-time responses to improve efficiency and customer satisfaction. Whether you need AI-powered call handling, automated voice assistants, or custom voice integrations for CRM and business operations, Think AI delivers state-of-the-art solutions designed for scalability, accuracy, and seamless deployment.

With AI-powered voice agents capable of multilingual support, sentiment analysis, and intelligent decision-making, Think AI ensures that businesses stay ahead in the era of digital transformation. The company also provides custom AI voice models to match brand identity and enhance customer engagement through conversational AI. Visit: https://www.thinkai.co.uk

[northwoodsguru]

Manus AI vs GPT: Discover how a new autonomous, multi-agent system challenges GPT’s global scale & proven performance in AI's next era!

[onlinemarketingcash4ublogspotcom]

Will Vertical AI Agents replace Software-as-a-Service (SaaS)? Explore the future of AI-driven solutions, their impact on SaaS platforms, and whether businesses will transition to specialized AI-powered alternatives. Read our in-depth analysis of this evolving technology landscape.

https://www.onlinemarketingcash4u.blogspot.com/

Vertical AI Agents are specialized artificial intelligence models designed to serve specific industries. Unlike general-purpose AI, these agents are trained on industry-specific datasets, allowing them to perform complex tasks with high accuracy. For example, AI-driven radiology tools in healthcare can analyze medical images faster and more accurately than conventional software, while financial AI agents can assess credit risk more effectively than traditional SaaS-based lending platforms.

[certivo]

Digital Turnaround: How Tech Transforms Legacy Companies | Kunal Chopra on Shift AI

In this episode of the Shift AI Podcast, Certivo CEO Kunal Chopra joins Boaz Ashkenazy (CEO of Augmented AI Labs) to explore how legacy, “pen-and-paper” companies can undergo complete digital transformation. From eliminating manual processes to embedding AI agents directly into workflows, Kunal shares how he led old-school organizations into the future using technology and operational redesign.

At Certivo, AI isn’t just a tool — it’s a team member. CORA, our AI compliance agent, collaborates with human teams to automate the tedious, surface what matters, and help manufacturers stay always compliant and always market-ready.

This episode is a must-watch for anyone leading change in traditional industries, compliance management, or AI-driven operations.

🎧 Watch the full podcast: https://www.youtube.com/watch?v=KDTCN5Jyjfw 🌐 Learn more about Certivo: https://www.certivo.com/

[datapeakbyfactr]

5 AI Workflow Automation Strategies You Need to Implement Today 

Businesses that adopt AI-driven automation increase productivity by an average of 40% while reducing operational costs. Artificial Intelligence (AI) has advanced far beyond basic chatbots or simple data processing and is now a transformative force in revolutionizing workflows across industries. Whether you're an independent entrepreneur, startup founder, or part of a large organization, AI-driven workflow automation can save valuable time and optimize operations in ways that were inconceivable just a few years ago. 

Let’s explore five cutting-edge AI workflow automation strategies you need to implement today to secure your business’s success for the future. Each strategy includes a practical breakdown, industry-relevant tools, and actionable tips you can start utilizing right away. 

1. Intelligent Email and Communication Management 

Managing communications across email and messaging platforms often becomes a frustrating time drain. Between overflowing inboxes and endless Slack notifications, important messages can easily get lost, negatively impacting productivity. AI simplifies communication management by intelligently filtering messages, prioritizing content, and even automating responses, giving you more time to focus on strategic tasks. 

Tools You Can Use: 

Superhuman: A sleek email client with AI-driven prioritization. 

SaneBox: Automatically filters out unimportant emails. 

ChatGPT in Slack: Summarizes conversations and drafts replies. 

Steps to Implement: 

Evaluate your communication needs—are you overwhelmed by emails, messaging platforms, or both? Choose AI tools tailored to your pain points. 

For email management, integrate platforms like Superhuman or SaneBox into your existing inbox. Use their setup guides to connect and configure rules for prioritizing senders and topics. 

Automate responses for routine inquiries using customizable templates and rules within these tools. 

For messaging, connect AI bots like ChatGPT to your Slack or Teams account. Configure them to summarize threads or provide daily updates for key channels. 

Regularly review AI recommendations for prioritization to ensure they align with your workflow. 

Case Study: A mid-sized consulting firm integrated Superhuman and Slack’s ChatGPT bot to streamline daily communications. Within two months, email response times dropped by 35%, and the team reported a 50% improvement in message clarity and prioritization. 

Pro Tip: If your messages are still chaotic, try forwarding an entire day’s worth of emails to ChatGPT to generate a concise action plan. Adjust the parameters to refine results over time. 

2. AI-Powered Customer Support Automation 

Exceptional customer service builds trust and loyalty, but providing 24/7 support can overwhelm teams and lead to bottlenecks. Common challenges include long response times and a lack of scalability for high-demand periods. AI addresses these issues by automating FAQs, routing queries to the right departments, and escalating complex cases to human agents. This results in faster resolutions, reduced costs, and enhanced customer satisfaction. 

Tools You Can Use: 

Zendesk + Ada: Combines advanced ticket routing with AI-driven query handling. 

Intercom: Features GPT-enabled chatbots for instant support. 

Tidio: Offers AI-powered automation combined with live chat for personalized service. 

Steps to Implement: 

Audit your support channels and identify recurring questions or issues. 

Select a chatbot platform based on your specific needs (e.g., Zendesk for ticket routing or Tidio for live chat integration). 

Import historical support logs into the AI platform to train it on your company’s tone, style, and escalation protocols. 

Create detailed escalation workflows to ensure complex cases are routed to human agents with all relevant context. 

Deploy chatbots on your website, app, or social media pages for seamless customer interactions. Monitor analytics and feedback regularly to improve functionality. 

Case Study: An e-commerce company implemented Intercom's AI chatbot to handle customer inquiries. Within 60 days, the bot resolved 70% of tickets without human involvement, cutting support costs by 40% and boosting customer satisfaction scores. 

Pro Tip: Start small by launching your chatbot with a focus on top 10 FAQs. Gradually expand its knowledge base as you identify additional customer needs. 

3. Automated Data Analysis and Reporting 

Data drives decision-making, but manually gathering and analyzing information can be tedious and prone to errors. Businesses often struggle to extract meaningful insights quickly enough to stay ahead. AI tools simplify data analysis by automating collection, cleaning, and visualization while providing actionable insights in real time. This enables faster, smarter decisions and frees up resources for high-value tasks. 

Tools You Can Use: 

MonkeyLearn: No-code AI for easy text analysis. 

Power BI + Azure AI: Integrated platform for visualization and smart analytics. 

Narrative Science: Converts complex data into readable summaries. 

Steps to Implement: 

Connect your CRM or data storage systems (e.g., Excel, Salesforce) to an AI-powered analysis tool like Power BI. 

Configure AI models to clean data automatically, removing duplicates and inconsistencies. 

Define key metrics and KPIs, such as sales trends or customer retention rates, to focus on actionable insights. 

Set up automated reports that refresh with real-time data and include visual dashboards. 

Use tools like Narrative Science to translate these insights into summary reports for stakeholders. 

Case Study: A SaaS company used Power BI with Azure AI to automate weekly performance dashboards. Analysts saved over 15 hours per week, and leadership had instant access to visualized metrics that improved decision-making speed by 30%. 

Pro Tip: Ask ChatGPT or similar AI tools to analyze a CSV file containing your sales data. Compare its insights with traditional analysis to evaluate accuracy and usefulness. 

4. AI-Driven Marketing Automation 

Marketing teams often find themselves bogged down by repetitive processes like audience segmentation, email campaigns, and A/B testing. These tasks can divert resources from more strategic initiatives. AI solves this problem by automating the workflow, from creating content to optimizing campaign performance. It helps teams launch impactful campaigns with greater efficiency and personalization. 

Tools You Can Use: 

Jasper: AI-powered writing assistant for blogs, ads, and more. 

Seventh Sense: Optimizes email send times for higher engagement. 

HubSpot: Offers built-in AI tools for segmentation and outreach automation. 

Steps to Implement: 

Use Jasper or ChatGPT to brainstorm ideas for blog posts, social media copy, and ad campaigns. Refine the content for SEO optimization or audience targeting. 

Upload your customer data into AI-powered platforms like HubSpot to automate segmentation based on behavior and demographics. 

Use AI tools for A/B testing email subject lines, landing pages, and visuals to identify top-performing versions. 

Automate email sequences and personalized content delivery based on real-time engagement data. 

Monitor campaign performance through AI dashboards and continuously refine strategies based on insights. 

Case Study: A digital agency used Jasper and HubSpot AI features to streamline campaign creation. Email open rates rose by 25%, and campaign setup time decreased by 60%, allowing the team to scale output without hiring additional staff. 

Pro Tip: Transform a single blog post into various formats—short videos, LinkedIn articles, and Instagram carousel posts—using AI tools to maximize reach. 

5. Smart Task and Project Management 

Managing tasks and projects is challenging, especially for distributed teams juggling multiple deadlines. From missed deadlines to unclear priorities, inefficiencies can quickly derail progress. AI-powered tools revolutionize project management by learning your workflows, identifying bottlenecks, and dynamically adjusting timelines and resources. They ensure tasks are prioritized effectively and team members stay aligned. 

Tools You Can Use: 

ClickUp with AI: Provides intelligent recommendations and goal tracking. 

Motion: Automatically organizes tasks and schedules based on priorities. 

Notion AI: Helps summarize updates, generate ideas, and streamline organization. 

Steps to Implement: 

Import your project backlog into AI-enhanced management tools like ClickUp or Motion. Organize tasks by priority and deadlines. 

Use AI features to generate timelines and reallocate tasks based on workload analysis. 

Set up recurring updates and milestone alerts to keep teams aligned. 

Use the platform to predict potential delays and recommend solutions, such as reallocating resources or adjusting timelines. 

Regularly review AI insights to optimize workflows and maintain efficiency. 

Case Study: A remote-first software company adopted Motion and ClickUp AI features for sprint planning. Task completion rates improved by 20%, and team members reported higher clarity in roles and deadlines during weekly retrospectives. 

Pro Tip: Test Motion for a day and let it take over your task prioritization. Compare its dynamic scheduling with your manual process and identify where AI improves productivity. 

ROI of AI Workflow Automation: Why It’s Worth the Investment 

Beyond convenience and speed, AI workflow automation delivers measurable financial impact. Here’s what businesses typically experience when they adopt AI across functions: 

The more you automate with AI, the more compounding your ROI becomes—especially when savings span multiple departments. 

AI workflow automation is reshaping the way businesses tackle everyday challenges, turning complex processes into streamlined systems that drive results. By adopting even a few of the strategies outlined here, organizations can unlock remarkable efficiencies—saving precious time, minimizing costly errors, and freeing up teams to focus on creativity, innovation, and strategic priorities. 

Let AI handle the busywork so your team can stay focused on the big picture. 

Learn More about DataPeak:

[digiworkforce]

Mikko Lampi M.Sc. (Eng.) has been appointed Chief Operating Officer (COO) and member of the Management Team of Digital Workforce Services Plc.

[mrs-elsie-barnes]

Let The Rain Fall | Bucky Barnes x Autistic!Reader | Short Series - Part 1 of 4 - 2.6k

Bucky finds your file and is shocked to learn you're not in the field, despite your excellent test scores. Although Steve advises him to let it go, Bucky sets to work on convincing you instead.

Warnings: nothing yet really. Some reference to Bucky's time as the Winter Solider but it's very brief.

Masterlist | Let the Rain Fall Masterlist | Bucky Barnes

Bucky dropped another manila folder onto the desk and leant back in his office chair with a sigh. Across the table Steve looked up from his equally towering pile of agent folders and eyed his friend.  

“Tired?” He asked, closing the latest file and placing it carefully with the others he’d already assessed.  

“This is exhausting, there must be a hundred agents here.” Bucky kept his face covered by his hands, digging the heels of his palms into his eyes.  

“Sixty.”  

“What?”  

“There’s sixty, but some of them already have positions.”  

“Of all the automated shit in this century, this, this, is what we have to do by hand?” 

Steve didn’t look up again, “it’s to keep staff information safe after...well...after everything.  

Bucky tensed; he knew what Steve meant by everything. Flashes of that day still came back to him sometimes in his dreams and his nightmares. Moments of clarity in an otherwise hazy memory, explosions, jets falling from the sky and water, fear and freedom. 

“Well, we already found the best candidate, right at the top, her scores and rankings are incredible.” 

Bucky handed Steve the folder, the covering page turned back so he could see the smiling face of the agent in her profile.  

Steve did look up then, “not her, sorry.” He ducked back down, folding the cover back over and attempting to take the folder from Bucky.  

“Why not? She’s a crack shot, scored well in all the reasoning tests and has excellent recommendations from her tutors. She even has a sealed folder from Xavier’s School, but she must have done well to get the college course she wanted.”  

“I know, but she requested desk duty and we’re respecting that. So, not her. She might do some digital recon, if you ask nicely. But she oversees the mission records now.”  

“Steve, she has a sealed envelope, what if it’s a power? How can you leave her on desk duty.” Bucky insisted.  

It was Steve’s turn to sigh, pushing his hand through his blonde hair until it stuck up in tufts. “Remember when you wanted desk duty? Remember how you have a sealed envelope in your folder? I respected you; I respect her. She’s a great Agent, but she’s not going into the field. Drop it.”  

The two men eyed each other for a second before Bucky stood, grabbing his leather jacket from the back of his chair. “I’m going for a walk. I need a break.”  

Bucky stalked through the Avenger’s compound, allowing the door to the private offices to close with a bang. He’d intended to head towards the gym to work off his frustrations at the incredibly tedious task of picking new agents and the even more frustrating realisation that the only agent he’d shortlisted had voluntarily taken themselves out of the field.  

Before he knew it, he was scanning his pass card and weaving his way through the open plan office that sat opposite the Avenger’s private space. He knew a few people here, mostly from bumping into them on the way into work or at the coffee shop on the ground floor.  

Stopping by one of the assistant’s desks he asked for directions to Mission Records, only to be pointed to a set of small, two people sized, meeting rooms that sat at the edge of the otherwise open space.  

Cautiously he picked through the maze of desks and paused outside of the door. Inside he could hear the faint sound of humming and the swish of papers, after a few seconds there was a dull thud as if something had dropped onto a desk and bounced off quickly. Underlying this was the sound of rain, despite the fact it was a dry day. 

Taking a shaky breath to steady his nerves, Bucky knocked on the door.  

“Who is it?” The voice inside was high and lilting, definitely nervous.  

“Uhm - it’s Sergeant Barnes...” Bucky tried to sound authoritative but, honestly, he hadn’t been in charge of anyone or anything since he was Steve’s second in 1945 and now that he was trying, panic was rising inside of him like a tide.  

From behind the door, he heard another dull thump and the paper noise stopped, but the rain continued. 

“You can come in.”  

Bucky turned the handle slowly, ducking his head and wishing he’d at least taken a lap of the office to think of what he was going to say to you, and then he was inside.  

The small office space was considerably cooler than the main office, with the faint smell of fresh linen fabric softener. It wouldn’t normally be the kind of detail he’d notice, except that he liked it too and knew it wasn’t sold at the small grocery shop on the other side of the compound. You had to go all the way into town for anything other than Tony’s preferred fruit cocktail scent. He was lost in his thoughts when he looked up, and there you were.  

Your folder had boasted of your prowess with a gun, your efficiency with a knife, tenacity during physical training and, although there was a picture of you in your official agent’s uniform, he had not been prepared for meeting you in real life.  

He was, in fact, surprised to recognise you considering the wave of people that seemed to roam around the compound. He’d seen you eating alone on the grass outside, and reading in the atrium when it was raining after hours. It was odd to see you in your own office, you looked so different to the official image of you on file. 

“Good afternoon, Sergeant Barnes.” You said, politely but with that edge of nervousness still bubbling beneath the surface. 

He took you in. Your soft, pale blue cotton shirt over what was clearly a pair of sweatpants, despite the fact they were a dark blue. Although your trousers fit you, the shirt was too large, it didn’t quite fit correctly and the sleeves were so long that your hands were covered up to your knuckles by the cuffs. As his gaze travelled over you, you shifted, pulling your hands inside of the sleeves completely and then tucking your hands under your thighs.  

You looked small, in such a large chair, wider than his own with a comfortable, quilted back and seat, your legs crossed neatly under your desk as if you were sitting on the floor.  

The desk itself was home to an array of trinkets and toys, all lined up along the top edge and around the double screen of your computer. Bucky marvelled at your ability to keep up with such a thing, he found his own laptop screen quite enough brightness. But then your room was darker than his office with Steve and the blaring overhead light.  

You shifted again, looking at him pointedly.  

“Would you like to sit down?” You indicated a round armchair that took up most of the rest of the space and he sat down heavily, aware of his large black boots and wide frame in such a small space.  

“Thanks,” he hesitated. 

Awkwardly, you quickly gave your name, as if he hadn’t read your folder a hundred times.  

You allowed one of your hands to be freed from its confines under your leg, but only to chew the pad of your thumb while you gazed somewhere over Bucky’s left shoulder.  

Bucky’s stomach turned over and he angled his shoulder back self consciously. You snapped your eyes to his and then looked down at your thumb, “sorry,” before snatching one of the toys from your desk and beginning to push the little plastic bubbles in and out.  

“I wanted to talk to you about your scores at the academy.”  

“Oh?” You kept your eyes on the toy.  

“They’re very good.”  

“Yes, I’m very proud of them myself.”  

“And you graduated college?”  

You looked up again, “look I know it took me a little longer than everyone else but I -”  

Bucky held his hands up in surrender, “it wasn’t a comment on how or when, just that you had.”  

“Oh,” you nodded, “okay.”  

Pop, pop, pop.  

“Sorry, did you need something from me? It’s just best if you’re really clear and then I can answer.” You placed the little plastic toy back in its place on the desk.  

“I wanted you to join the Avengers Agents, we have three open spots and I’d like you to take one of them.”  

“No, thank you.” You smiled at him, it was a friendly but firm smile that reached your eyes enough to let him know you were at least flattered, but that this really was a no and for some reason it made him absolutely furious.  

“If you’re worried about the other agents then -” 

“No, it’s not that. I don’t want to.”  

“There’s lots of training and -”  

“No, thank you.”  

“It’s a great -”  

“I said, no.” You snapped and then plastered that smile back on the lower part of your face. “Thank you.”  

You turned to your computer and began typing and Bucky stood feeling smaller than he had in a long time.  

“Can I ask why?”  

Your typing stopped but you didn’t look at him.  

“I already documented that I’d ask you, so if you don’t want to, I  just need a reason.” He waved at the twin stacks of paper in your ‘in’ and ‘out’ trays. “You know what the paperwork is like here.”

“I don't like the uniform, it’s itchy and uncomfortable. Is that good enough?” You cocked your eyebrow at him and then turned, pointedly, back to your work.  

Bucky left with a nod, closing the door quietly behind him and pausing long enough to hear the shift of paper again.  

Your conversation with Sergeant Barnes had left you rattled, so as soon as he’d walked away you closed your computer down with a sigh and left the office for the day. You’d come in extra early tomorrow to make up for it, it’d be quieter in the morning anyway and you could hopefully get ahead by 10am and then enjoy a quiet coffee and some time curled up reading before the next round of debriefs were submitted.  

The office was empty at 7.30am, the lights still off and the scent of the cleaners all-purpose spray still lingering in the air. You’d only managed to settle in and grab a coffee before there was a sharp knock on the door and a familiar shadow looming through the frosted glass.  

“Come in.”  

Sergeant Barnes opened the door tentatively and peeked around the frosted glass, “morning.” 

He smiled awkwardly, hovering in the doorway with a large black garment bag before you beckoned him in and pointed towards the spare chair.  

“Morning,” you smiled back automatically, but before you could drop it a genuine flash of happiness passed over the Sergeant’s face and your smile moved from forced to genuine too. There weren’t many people who were actually happy to see you around the office, and yet here was Barnes, again, smiling at you.  

“I’m really sorry about yesterday.” He said, seriously, “I didn’t mean to push you, I was just worked up.”  

Whatever you’d been expecting when he’d knocked, it wasn’t this.  

“Oh, well.” You moved in your seat, pulling your hands inside your sleeves again, a navy-blue fleece lined sweater today, since the weather was unseasonably cold, the collar was turned over under your chin where you’d been fiddling with it. “I was short with you too, I can be a bit – sensitive, about – things. So, I’m sorry too.”  

“Then we’re even,” he smiled and settled into his chair more, looking around at your office.  

Suddenly you felt self-conscious, this was your space and it was hard won. You’d filled it with every soft thing that you needed to make it through your days in the office, cute mugs, fidget toys, blankets and even a teddy. While Sergeant Barnes was looking at your bookshelf you tried to move the little bear from his prominent position next to your monitor and into the open draw by your side, but he caught you and grinned instead.  

“Cute bear.”  

You snatched it up and squeezed its soft body between both your hands. “Thank you.”  

There was an awkward silence as the Sergeant seemed to think of what to say next and then he grabbed the garment bag again, as if he’d forgotten it as soon as he’d sat down.  

“Oh, yes, I was talking to Steve about what you said yesterday -” he looked up at your blank face, “Steve Rogers, you know ahh-” he rubbed his cheek as if he could remove the red smudge of embarrassment. 

“I guessed.”  

“Right, of course, I spoke to Steve, and he said that if that was what was holding you back then it was an easy fix and -” he pulled the zipper down on the bag revealing a black-on-black ensemble inside. Fitted combat trousers with pockets and an empty utility belt as well as a black, long-sleeved, shirt and flack vest. “It’s all made of a cotton blend with reinforced, lightweight, Kevlar. If you like it we can look at adding Vibranium for strength. It has a fleece lining, I noticed you had two fleece lined items in here and took a risk, so it should be soft on your skin. What do you think?”  

Bucky beamed at you from across your desk and your stomach twisted into knots, a yawning chasm of silence opening between you the longer you didn’t answer. You knew what you were supposed to say, you knew you were supposed to be excited and say yes and run off to be an Agent.   

“It smells like my fabric softener.” You blurted. 

“Yes, I figured you used the one from the store in town, I hope that wasn’t presumptuous?” 

For a moment you reached out to touch the sleeve, it was soft and it smelt lovely. But -

“Thank you, Sergeant Barnes, I can see you’ve gone to a lot of effort -”  

“But it’s still a no?”  

“It’s still a no.”  

“Okay.” He said, kindly, zipping the garment bag back up. You expected him to leave, taking it with him, but instead he hung it on an empty hook by your door. “I just wanted you to know that I’m sorry, and that you’re welcome to join us anytime. There’s a big budget, especially for talented agents, I’d hate for something like a uniform to hold you back.” 

“Thank you.”  

“There’s a simple recon next week, Steve and I are leading some of the other newly qualified agents and he said you sometimes do recon, there’s a seat open for you if you want, but there’ll be no hard feelings if you don’t come.” 

“Okay.” 

You weren’t sure if it was the awkwardness or his earnest smile, but you had the urge to hug him. You hadn’t hugged anyone since you’d moved to the compound and you missed the comforting feeling of it, he even smelt lovely and for the briefest moment you imagined him holding you close to him. He had a black cotton shirt on with a dark green and blue flannel over the top. It looked soft, and now your arms felt empty and heavy at your sides, with no one to hold but yourself.It felt strange, too, to be wanted. You’d mostly assumed your colleagues were glad to be rid of you. Instead of embracing him, you stood and offered your hand, allowing him to squeeze your palm before he left, and then spent the next three hours wondering about his request.  

Part 2 ->

[mostlysignssomeportents]

Cleantech has an enshittification problem

On July 14, I'm giving the closing keynote for the fifteenth HACKERS ON PLANET EARTH, in QUEENS, NY. Happy Bastille Day! On July 20, I'm appearing in CHICAGO at Exile in Bookville.

EVs won't save the planet. Ultimately, the material bill for billions of individual vehicles and the unavoidable geometry of more cars-more traffic-more roads-greater distances-more cars dictate that the future of our cities and planet requires public transit – lots of it.

But no matter how much public transit we install, there's always going to be some personal vehicles on the road, and not just bikes, ebikes and scooters. Between deliveries, accessibility, and stubbornly low-density regions, there's going to be a lot of cars, vans and trucks on the road for the foreseeable future, and these should be electric.

Beyond that irreducible minimum of personal vehicles, there's the fact that individuals can't install their own public transit system; in places that lack the political will or means to create working transit, EVs are a way for people to significantly reduce their personal emissions.

In policy circles, EV adoption is treated as a logistical and financial issue, so governments have focused on making EVs affordable and increasing the density of charging stations. As an EV owner, I can affirm that affordability and logistics were important concerns when we were shopping for a car.

But there's a third EV problem that is almost entirely off policy radar: enshittification.

An EV is a rolling computer in a fancy case with a squishy person inside of it. While this can sound scary, there are lots of cool implications for this. For example, your EV could download your local power company's tariff schedule and preferentially charge itself when the rates are lowest; they could also coordinate with the utility to reduce charging when loads are peaking. You can start them with your phone. Your repair technician can run extensive remote diagnostics on them and help you solve many problems from the road. New features can be delivered over the air.

That's just for starters, but there's so much more in the future. After all, the signal virtue of a digital computer is its flexibility. The only computer we know how to make is the Turing complete, universal, Von Neumann machine, which can run every valid program. If a feature is computationally tractable – from automated parallel parking to advanced collision prevention – it can run on a car.

The problem is that this digital flexibility presents a moral hazard to EV manufacturers. EVs are designed to make any kind of unauthorized, owner-selected modification into an IP rights violation ("IP" in this case is "any law that lets me control the conduct of my customers or competitors"):

https://locusmag.com/2020/09/cory-doctorow-ip/

EVs are also designed so that the manufacturer can unilaterally exert control over them or alter their operation. EVs – even more than conventional vehicles – are designed to be remotely killswitched in order to help manufacturers and dealers pressure people into paying their car notes on time:

https://pluralistic.net/2023/07/24/rent-to-pwn/#kitt-is-a-demon

Manufacturers can reach into your car and change how much of your battery you can access:

https://pluralistic.net/2023/07/28/edison-not-tesla/#demon-haunted-world

They can lock your car and have it send its location to a repo man, then greet him by blinking its lights, honking its horn, and pulling out of its parking space:

https://tiremeetsroad.com/2021/03/18/tesla-allegedly-remotely-unlocks-model-3-owners-car-uses-smart-summon-to-help-repo-agent/

And of course, they can detect when you've asked independent mechanic to service your car and then punish you by degrading its functionality:

https://www.repairerdrivennews.com/2024/06/26/two-of-eight-claims-in-tesla-anti-trust-lawsuit-will-move-forward/

This is "twiddling" – unilaterally and irreversibly altering the functionality of a product or service, secure in the knowledge that IP law will prevent anyone from twiddling back by restoring the gadget to a preferred configuration:

https://pluralistic.net/2023/02/19/twiddler/

The thing is, for an EV, twiddling is the best case scenario. As bad as it is for the company that made your EV to change how it works whenever they feel like picking your pocket, that's infinitely preferable to the manufacturer going bankrupt and bricking your car.

That's what just happened to owners of Fisker EVs, cars that cost $40-70k. Cars are long-term purchases. An EV should last 12-20 years, or even longer if you pay to swap the battery pack. Fisker was founded in 2016 and shipped its first Ocean SUV in 2023. The company is now bankrupt:

https://insideevs.com/news/723669/fisker-inc-bankruptcy-chapter-11-official/

Fisker called its vehicles "software-based cars" and they weren't kidding. Without continuous software updates and server access, those Fisker Ocean SUVs are turning into bricks. What's more, the company designed the car from the ground up to make any kind of independent service and support into a felony, by wrapping the whole thing in overlapping layers of IP. That means that no one can step in with a module that jailbreaks the Fisker and drops in an alternative firmware that will keep the fleet rolling.

This is the third EV risk – not just finance, not just charger infrastructure, but the possibility that any whizzy, cool new EV company will go bust and brick your $70k cleantech investment, irreversibly transforming your car into 5,500 lb worth of e-waste.

This confers a huge advantage onto the big automakers like VW, Kia, Ford, etc. Tesla gets a pass, too, because it achieved critical mass before people started to wise up to the risk of twiddling and bricking. If you're making a serious investment in a product you expect to use for 20 years, are you really gonna buy it from a two-year old startup with six months' capital in the bank?

The incumbency advantage here means that the big automakers won't have any reason to sink a lot of money into R&D, because they won't have to worry about hungry startups with cool new ideas eating their lunches. They can maintain the cozy cartel that has seen cars stagnate for decades, with the majority of "innovation" taking the form of shitty, extractive and ill-starred ideas like touchscreen controls and an accelerator pedal that you have to rent by the month:

https://www.theverge.com/2022/11/23/23474969/mercedes-car-subscription-faster-acceleration-feature-price

Put that way, it's clear that this isn't an EV problem, it's a cleantech problem. Cleantech has all the problems of EVs: it requires a large capital expenditure, it will be "smart," and it is expected to last for decades. That's rooftop solar, heat-pumps, smart thermostat sensor arrays, and home storage batteries.

And just as with EVs, policymakers have focused on infrastructure and affordability without paying any attention to the enshittification risks. Your rooftop solar will likely be controlled via a Solaredge box – a terrible technology that stops working if it can't reach the internet for a protracted period (that's right, your home solar stops working if the grid fails!).

I found this out the hard way during the covid lockdowns, when Solaredge terminated its 3G cellular contract and notified me that I would have to replace the modem in my system or it would stop working. This was at the height of the supply-chain crisis and there was a long waiting list for any replacement modems, with wifi cards (that used your home internet rather than a cellular connection) completely sold out for most of a year.

There are good reasons to connect rooftop solar arrays to the internet – it's not just so that Solaredge can enshittify my service. Solar arrays that coordinate with the grid can make it much easier and safer to manage a grid that was designed for centralized power production and is being retrofitted for distributed generation, one roof at a time.

But when the imperatives of extraction and efficiency go to war, extraction always wins. After all, the Solaredge system is already in place and solar installers are largely ignorant of, and indifferent to, the reasons that a homeowner might want to directly control and monitor their system via local controls that don't roundtrip through the cloud.

Somewhere in the hindbrain of any prospective solar purchaser is the experience with bricked and enshittified "smart" gadgets, and the knowledge that anything they buy from a cool startup with lots of great ideas for improving production, monitoring, and/or costs poses the risk of having your 20 year investment bricked after just a few years – and, thanks to the extractive imperative, no one will be able to step in and restore your ex-solar array to good working order.

I make the majority of my living from books, which means that my pay is very "lumpy" – I get large sums when I publish a book and very little in between. For many years, I've used these payments to make big purchases, rather than financing them over long periods where I can't predict my income. We've used my book payments to put in solar, then an induction stove, then a battery. We used one to buy out the lease on our EV. And just a month ago, we used the money from my upcoming Enshittification book to put in a heat pump (with enough left over to pay for a pair of long-overdue cataract surgeries, scheduled for the fall).

When we started shopping for heat pumps, it was clear that this was a very exciting sector. First of all, heat pumps are kind of magic, so efficient and effective it's almost surreal. But beyond the basic tech – which has been around since the late 1940s – there is a vast ferment of cool digital features coming from exciting and innovative startups.

By nature, I'm the kid of person who likes these digital features. I started out as a computer programmer, and while I haven't written production code since the previous millennium, I've been in and around the tech industry for my whole adult life. But when it came time to buy a heat-pump – an investment that I expected to last for 20 years or more – there was no way I was going to buy one of these cool new digitally enhanced pumps, no matter how much the reviewers loved them. Sure, they'd work well, but it's precisely because I'm so knowledgeable about high tech that I could see that they would fail very, very badly.

You may think EVs are bullshit, and they are – though there will always be room for some personal vehicles, and it's better for people in transit deserts to drive EVs than gas-guzzlers. You may think rooftop solar is a dead-end and be all-in on utility scale solar (I think we need both, especially given the grid-disrupting extreme climate events on our horizon). But there's still a wide range of cleantech – induction tops, heat pumps, smart thermostats – that are capital intensive, have a long duty cycle, and have good reasons to be digitized and networked.

Take home storage batteries: your utility can push its rate card to your battery every time they change their prices, and your battery can use that information to decide when to let your house tap into the grid, and when to switch over to powering your home with the solar you've stored up during the day. This is a very old and proven pattern in tech: the old Fidonet BBS network used a version of this, with each BBS timing its calls to other nodes to coincide with the cheapest long-distance rates, so that messages for distant systems could be passed on:

https://en.wikipedia.org/wiki/FidoNet

Cleantech is a very dynamic sector, even if its triumphs are largely unheralded. There's a quiet revolution underway in generation, storage and transmission of renewable power, and a complimentary revolution in power-consumption in vehicles and homes:

https://pluralistic.net/2024/06/12/s-curve/#anything-that-cant-go-on-forever-eventually-stops

But cleantech is too important to leave to the incumbents, who are addicted to enshittification and planned obsolescence. These giant, financialized firms lack the discipline and culture to make products that have the features – and cost savings – to make them appealing to the very wide range of buyers who must transition as soon as possible, for the sake of the very planet.

It's not enough for our policymakers to focus on financing and infrastructure barriers to cleantech adoption. We also need a policy-level response to enshittification.

Ideally, every cleantech device would be designed so that it was impossible to enshittify – which would also make it impossible to brick:

Based on free software (best), or with source code escrowed with a trustee who must release the code if the company enters administration (distant second-best);

All patents in a royalty-free patent-pool (best); or in a trust that will release them into a royalty-free pool if the company enters administration (distant second-best);

No parts-pairing or other DRM permitted (best); or with parts-pairing utilities available to all parties on a reasonable and non-discriminatory basis (distant second-best);

All diagnostic and error codes in the public domain, with all codes in the clear within the device (best); or with decoding utilities available on demand to all comers on a reasonable and non-discriminatory basis (distant second-best).

There's an obvious business objection to this: it will reduce investment in innovative cleantech because investors will perceive these restrictions as limits on the expected profits of their portfolio companies. It's true: these measures are designed to prevent rent-extraction and other enshittificatory practices by cleantech companies, and to the extent that investors are counting on enshittification rents, this might prevent them from investing.

But that has to be balanced against the way that a general prohibition on enshittificatory practices will inspire consumer confidence in innovative and novel cleantech products, because buyers will know that their investments will be protected over the whole expected lifespan of the product, even if the startup goes bust (nearly every startup goes bust). These measures mean that a company with a cool product will have a much larger customer-base to sell to. Those additional sales more than offset the loss of expected revenue from cheating and screwing your customers by twiddling them to death.

There's also an obvious legal objection to this: creating these policies will require a huge amount of action from Congress and the executive branch, a whole whack of new rules and laws to make them happen, and each will attract court-challenges.

That's also true, though it shouldn't stop us from trying to get legal reforms. As a matter of public policy, it's terrible and fucked up that companies can enshittify the things we buy and leave us with no remedy.

However, we don't have to wait for legal reform to make this work. We can take a shortcut with procurement – the things governments buy with public money. The feds, the states and localities buy a lot of cleantech: for public facilities, for public housing, for public use. Prudent public policy dictates that governments should refuse to buy any tech unless it is designed to be enshittification-resistant.

This is an old and honorable tradition in policymaking. Lincoln insisted that the rifles he bought for the Union Army come with interoperable tooling and ammo, for obvious reasons. No one wants to be the Commander in Chief who shows up on the battlefield and says, "Sorry, boys, war's postponed, our sole supplier decided to stop making ammunition."

By creating a market for enshittification-proof cleantech, governments can ensure that the public always has the option of buying an EV that can't be bricked even if the maker goes bust, a heat-pump whose digital features can be replaced or maintained by a third party of your choosing, a solar controller that coordinates with the grid in ways that serve their owners – not the manufacturers' shareholders.

We're going to have to change a lot to survive the coming years. Sure, there's a lot of scary ways that things can go wrong, but there's plenty about our world that should change, and plenty of ways those changes could be for the better. It's not enough for policymakers to focus on ensuring that we can afford to buy whatever badly thought-through, extractive tech the biggest companies want to foist on us – we also need a focus on making cleantech fit for purpose, truly smart, reliable and resilient.

Support me this summer on the Clarion Write-A-Thon and help raise money for the Clarion Science Fiction and Fantasy Writers' Workshop!

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/06/26/unplanned-obsolescence/#better-micetraps

Image: 臺灣古寫真上色 (modified) https://commons.wikimedia.org/wiki/File:Raid_on_Kagi_City_1945.jpg

Grendelkhan (modified) https://commons.wikimedia.org/wiki/File:Ground_mounted_solar_panels.gk.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

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Cap-IM Rec Week - Smut Saturday

Day 6 of @cap-ironman's rec week event! Today's recommendations feature a lot of lovely porn. And maybe some plot sometimes, as a treat.

And if you like subby and/or bottom Steve, check out my previous Stony smut rec list too.

Automated Fantasy by IOnlyWriteWhenCarmelyzed (MCU, Explicit, 1,711 words)

Summary: Steve gets bent over by one of Tony's Iron Man suits

Cherry Ride by copperbadge (@copperbadge) (MCU, Explicit, 12,318 words)

Summary: A SHIELD agent named Roger Stevens told Tony that his nickname was "Cap". Tony didn't connect the dots until it was much, much too late.

More below the cut!

Choices by fundamentalBlue (Marvel, Explicit, 1,749 words)

Summary: “We both know why you come here. So don’t go acting surprised or disgusted. You’re the one who wants this. You.”

Choke You With My Charms by synteis (Ults, Explicit, 8,919 words)

Summary: Steve can't seem to help himself from jerking off at Tony's big do. Tony can't seem to get it up after the last round of chemo. But when Tony offers to take Steve in hand and give Steve's cock some old-fashioned punishment, neither of them are quite expecting what happens next.

I won’t leave you falling by @blossomsinthemist (616, Explicit, 14,653 words)

Summary: Tony doms for Steve, which involves some specially enhanced red rope, cock rings, two vibrators, and a lot of orgasm control. It works out. Bottom Steve, trembling and desperate to come, loving dom Tony, plenty of aftercare.

Larger Than Life by @festiveferret (MCU, Explicit, 3,697 words)

Summary: Steve wants that in him. Right now.

Small Weird Love by @haemodye (Marvel, Explicit, 13,140 words)

Summary: When a magical mishap results in Tony swapping his legs for tentacles, he's absolutely mortified. How is he supposed to face Steve? Steve can't possibly want him like this. Right? Right...

So Much to Confide to You by @sineala (616, Explicit, 16,988 words)

Summary: After an attack by the Masters of Evil, Avengers Mansion is in ruins. Tony has come back from California to help the East Coast team pick up the pieces -- literally. And when the team finds items of a certain intimate nature in the wreckage of the mansion's bedrooms, Tony is of course the one who steps forward to claim them. This leads to two problems: Problem Number One: They're not his sex toys. His toys are in California. Therefore, one of Tony's longtime friends is also extremely kinky and he has no idea who. Problem Number Two: One of Tony's longtime friends happens to own an Iron Man butt plug. Oh, God.

The Crying Game by @fohatic (MCU, Explicit, 36,403 words)

Summary: Steve Rogers stared at the dimly glowing digital screen of the little burner phone, rereading the text message as if it might somehow give away something he missed the first dozen times he scrutinized it. His frown only deepened, though, brows drawing together with consternation as the 88 characters only left him with an even more ponderous sense of uncertainty. If you meant what you wrote, I'll be at the Swissotel Sarajevo, 4/18. Presidential Suite. 9pm. Come alone. ... Nearly a year after Steve and Tony's fallout—and only weeks after press rumors that Tony and Pepper's engagement was inexplicably called off—Steve gets a message on the dedicated burner phone. Despite his instinctive reservations, he's compelled to answer the mysterious call. An approximately canon-compliant story.

The Prize by @sabrecmc (MCU, Explicit, 318,625 words)

Summary: Steve ends up as a concubine in the royal harem.