#einstein copilot

[salesforceetc]

By 2025, it’s estimated that 100 zettabytes (ZB) of data will be stored in the cloud. For context, 1 ZB equals 1,000,000,000,000 gigabytes (GB). As organizations aim to harness this vast data for informed decision-making, seamless integration between systems becomes crucial.

Yet, the biggest challenge to timely project delivery, successful technology adoption, and exceptional customer experiences is the ease of integrating systems.

Ready to dive deeper? Check out the blog - Transforming Data Management with Salesforce Data Cloud: Connectors and Data Streams

[freddynossa]

Asistentes Virtuales y Chatbots: Revolución en la Interacción Digital

Los asistentes virtuales y chatbots se han convertido en herramientas fundamentales en nuestra interacción diaria con la tecnología. Estas soluciones basadas en inteligencia artificial están transformando cómo nos comunicamos con dispositivos, servicios y plataformas, ofreciendo respuestas inmediatas y personalizadas a nuestras necesidades. ¿Para qué sirven los asistentes virtuales y…

[fexleservices]

Game Changer Alert! Salesforce Launches Einstein Copilot - AI Assistant for Enhanced CRM

Boost efficiency & personalize customer interactions! FEXLE, your trusted Salesforce consulting company, can help you leverage Einstein Copilot's power.

Learn More here!

[aci-infotech]

In the dynamic world of user experience, Salesforce Einstein Copilot stands out as a groundbreaking innovation. Einstein Copilot isn't just another feature; it's a potential game-changer. It equips businesses to meet the high service standards expected by today's consumers, giving them a competitive edge in the market. It's about keeping up with the times and providing service that's not just smart but also intuitive.

[eaglesnick]

“A life directed chiefly toward the fulfillment of personal desires will sooner or later always lead to bitter disappointment.” - Albert Einstein

Have you ever asked yourself why so many of Reform UK’s top officials are either millionaires or billionaires? Do the ordinary supporters of Reform UK really believe the super-rich elite that runs Reform is going to be looking out for their interests?

To answer this question we could do worse than look to America and the relationship between Elon Musk and Donald Trump. Musk, a highly successful businessman and the world’s richest individual, (worth £136bn) has been making political headlines of late and previously was anything but a friend of Trump.

“In 2016, Musk was not the biggest fan of his future bestie, stating publicly that Trump was not fit to run for the nation’s highest office.” (Independent: 31/12/24)

So what has changed? What explains this change in the relationship between arguably the two most powerful men in the world? The answer is simple – Trump needed campaign money which Musk was willing to provide, and Musk needed a President that would be in his debt so he could go on making billions.

The AI chatboat Copilot informs us that:

"Elon Musk's business success and fortune are significantly tied to China. Tesla's Shanghai gigafactory, which opened in 2019, is a major contributor to this success. The factory produces almost one million Tesla cars annually, accounting for more than half of Tesla's global car production. China is also a crucial market for Tesla, being the second largest after the United States.”

Here is the problem for Musk.  Trump has always regarded China as the enemy and publicly announced his determination to impose even greater trade tariffs on imported goods from China than he had  during his previous presidency. This would have a seriously detrimental effect on Musk’s businesses ventures and his own personal wealth.

What better way to try and mitigate this possible personal economic disaster than having Trump in his debt? And it  is working.

Only a few weeks ago Musk came out in opposition to the bipartisan spending bill that would have:

“…prohibited or required notification of overseas transactions involving China in sectors like semiconductors, quantum technology and artificial intelligence. It also would have included an expanded review of Chinese real estate purchases near national security-sensitive sites and a requirement to study national security risks posed by Chinese-made consumer modems and routers.”  (Newsweek: 27/12/24)

The final version of the bill saw ALL previous China related provisions removed!

The point I am making is that millionaire and billionaire businessmen and women are rarely interested in politics other than how it affects them personally, especially concerning their own fortunes.

We are frequently told that Russia and China are the enemies of the West and that they endanger our democratic way of life. Yet time and time again, the very rich foster business ties to these countries and are happy to go on trading with them whatever the threat to the rest of us.

“New data suggests that 37 businesses with ties to the UK are currently under investigation for potential breaches of the UK's sanctions against Russia.”  (*Shedder+Wedderburn: 11/10/24)

It is time for the supporters of Reform UK to wake up to the very strong possibility that the millionaires and billionaires that bankroll and run the party are not there to make life better for ordinary working people but to protect and expand their already massive personal fortunes.

Like Musk, they are not investing millions in political activity for the good of their fellows. It is all about selfish greed. Michael Wolff, in his book ‘SIEGE: Trump Under Fire’, talks of the inevitable conflict of interests between the American working class and people like Musk and the super-rich global elite.

“It was a good day’s pay for a good day’s work versus global capital accumulation… Riding the China train to a new global order was quite a profitable activity for capital markets, but it was devastating for the job prospects of American workingmen and women.”

British workingmen and women would do well to pay heed to this warning when contemplating voting for Reform UK and its super-rich backers.

[rajaganapathi114]

What is meant by Tableau Agent?

By lowering the entrance barrier and assisting the analyst from data preparation to visualisation, Tableau Agent (previously Einstein Copilot for Tableau) enhances data analytics with the power of AI. Improve the Tableau environment by learning how agents operate in a sophisticated AI Tableau Course in Bangalore. No matter how much experience you have as a data analyst or how new you are to data exploration, Tableau Agent becomes your reliable partner, enabling you to gain knowledge and confidently make decisions.

Tableau Agent Integration:

By integrating into the Tableau environment, Tableau Agent improves your workflow for data analysis without causing any unplanned modifications. It serves as your intelligent assistant, helping you with the Tableau creation process while guaranteeing accuracy, offering best practices, and fostering trust based on the Einstein Trust Layer. Together with Tableau Agent, you can confidently examine your data, detect trends and patterns, and effectively and impactfully explain your results.

Many capabilities provided by Tableau Agent improve the data analytics experience and enable anyone to fully utilise their data.

Quicker answers with suggested queries

It can be frightening to start from scratch when you are learning analytics for employment, education, or just for leisure. Even so, where do you start? To reduce the burden and enable you to move swiftly from connecting to data to discovering insights, Tableau Agent can be used to provide questions you might ask of a particular datasource.

Tableau Agent creates a summary context of your connected datasource by quickly indexing it. The dataset is used to produce a few questions based on this summary. "Are there any patterns over time for sales across product categories?" is one scenario Tableau Agent recommends using a dataset similar to Tableau's Superstore practice dataset. You may build a line chart with only one click. Users used to Tableau's drag-and-drop interface can make changes to anything shown before saving and going on to the next question because this is all taking place throughout the authoring process. combining data analysis with hands-on learning.

In a reputable software training institution, anyone can rapidly improve their analytics skills by using the suggested questions.

Data exploration in conversation

Frequently, the response to your initial query prompts more enquiries that enhance your comprehension of the information. With Tableau Agent, you can enhance and iterate your data exploration. You don't have to lose the context of your earlier query to look for more information. Learning how Tableau arranges measurements and dimensions will help you become accustomed to where to drag and drop to obtain the precise visualisation you desire. You can save your work and start a new sheet to address a new question at any time.

Tableau Agent is capable of handling misspellings, filtering, and even changing the viz type. Tableau Agent does indeed employ semantic search for synonyms and fuzzy logic to identify misspelt words. Thus, if you begin with product category, "filter on technology and show sales by product" might be your next query. After setting the filter, Tableau Agent changes the dimension. Seeing this in practice can help you train more employees in your company to use data exploration for their own benefit.

Tableau Agent may enhance your analytics experience, regardless of whether you are working with an existing dashboard or beginning from scratch.

Creation of guided calculations:

Writing calculations in a foreign language might be challenging. With the use of natural language cues, Tableau Agent assists you in creating computations. Therefore, Tableau Agent is there to help you with calculations and explanations in Tableau Prep and while you are generating visualisations, whether you are developing a calculation for a new business KPI or you want to keep track of your favourite sports team.

Tableau Agent searches the Track Name column without specifically requesting it when I ask it to "create an indicator for songs that are a remix" based on my playlist data. Tableau Agent is aware that the term "remix" is frequently used in the Track Name field due to the initial indexing. Before accepting the computation, you can learn how it will behave by reading the informative description that is included with it. Tableau Agent is capable of understanding LODs, text and table computations, aggregations, and even regex writing!

Conclusion

observe how Tableau Agent leverages reliable AI to help you realise the full potential of both your data and yourself. Explore Tableau Agent, which is only available in Tableau+, by watching the demo.

[charles233]

AI Copilots in Business: The New Strategic Advantage

In a rapidly evolving business landscape, competitive advantage is no longer just about capital, market share, or even talent—it’s about how intelligently and efficiently you operate. That’s where AI copilots come in. These intelligent digital assistants are quickly becoming indispensable to modern enterprises, offering a new kind of strategic edge: one that leverages automation, data insight, and real-time collaboration.

From automating repetitive workflows to enabling faster, smarter decision-making, AI copilots are transforming how organizations think, plan, and act. As we stand on the brink of a new era in business, leaders must understand how to harness the full potential of AI copilots to drive growth, enhance productivity, and outpace competitors.

What Is an AI Copilot?

An AI copilot is an intelligent assistant powered by advanced machine learning models that help users perform tasks more efficiently. Unlike traditional automation tools that follow rigid instructions, AI copilots are context-aware, interactive, and capable of understanding natural language. They integrate with business tools—emails, spreadsheets, CRM systems, coding environments, and more—making them part of the daily workflow rather than just an add-on.

Examples include:

Microsoft Copilot in Office 365

Salesforce Einstein Copilot

Notion AI

GitHub Copilot

ChatGPT for Enterprise

These copilots can draft content, analyze data, generate insights, recommend next actions, and even simulate business outcomes—unlocking a new dimension of operational intelligence.

Why AI Copilots Are a Strategic Advantage

1. Faster Decision-Making

In business, speed often translates to competitive advantage. AI copilots reduce the time it takes to:

Analyze performance reports

Identify trends

Forecast outcomes

Compare business scenarios

Instead of waiting for analysts or departments to compile data, decision-makers can now get instant, data-driven answers, allowing them to act faster and more confidently.

2. Operational Efficiency

Repetitive tasks—writing reports, formatting slides, compiling meeting notes, or responding to standard customer queries—consume valuable employee time. AI copilots handle these tasks instantly, freeing up teams to focus on strategy, innovation, and client engagement.

This operational lift leads to:

Reduced manual errors

Lower overhead costs

Greater focus on high-impact work

3. Scalable Expertise

Not every team has a dedicated data analyst, legal expert, or marketing strategist—but with the right AI copilot, any employee can access that kind of assistance. For instance:

A junior employee can draft a contract using legal language

A sales rep can analyze customer trends like a data scientist

A marketer can write optimized content with SEO guidance

This democratization of expertise enables smaller teams to perform like larger ones and accelerates skill development across the board.

Real-World Business Applications

Executive Leadership

Executives rely on AI copilots to:

Generate summaries of board documents

Prepare strategic briefs

Simulate the impact of policy or pricing changes

Stay updated on market shifts and competitor activities

The result? More informed, agile leadership.

Marketing & Sales

AI copilots help marketers:

Generate and A/B test ad copy

Personalize email campaigns

Create social media content calendars

Analyze campaign performance in real-time

Sales teams use copilots to:

Write prospecting emails

Summarize CRM notes

Predict customer churn

Recommend upsell/cross-sell opportunities

This leads to higher conversion rates and faster cycles.

HR & Talent Management

AI copilots support HR teams by:

Screening resumes

Drafting job descriptions

Analyzing engagement surveys

Personalizing onboarding processes

That allows HR to shift focus from admin to culture, growth, and retention.

Product Development

For product managers and engineers, copilots:

Summarize user feedback

Draft user stories and specs

Generate or review code

Track sprint progress

This shortens time-to-market and ensures that products are more aligned with customer needs.

Overcoming Challenges and Building Trust

While AI copilots offer clear advantages, companies must address a few key considerations:

Data Privacy and Security

AI copilots must be integrated with enterprise-grade security protocols to ensure sensitive data isn’t compromised. This includes:

Data encryption

Role-based access control

Model training restrictions (i.e., not using company data to improve the public model)

Bias and Fairness

AI copilots, like all AI, can inherit bias from their training data. Business leaders must ensure ethical oversight and regular audits to mitigate unintended consequences, especially in hiring, finance, and legal processes.

Training and Change Management

AI copilots are most effective when teams know how to use them. This requires:

Training on prompt engineering and best practices

Change management programs to support adoption

Clear guidelines on when to rely on AI and when to involve humans

With the right onboarding, AI copilots become a natural extension of the team.

How to Get Started

If you’re considering integrating AI copilots into your business strategy, here’s a simple roadmap:

Identify high-friction workflows: Start where there's lots of repetition—report writing, customer support, document analysis, etc.

Choose the right tool: Evaluate copilots based on your tech stack (Microsoft, Google, Salesforce, etc.), budget, and security needs.

Pilot with a small team: Test its impact, gather feedback, and refine your approach.

Scale with structure: Roll out across departments with training, policies, and performance metrics.

Final Thoughts

AI copilots are not a futuristic concept—they are a present-day strategic asset. The companies that embrace this shift are not just improving productivity; they’re redefining how work is done, decisions are made, and growth is achieved.

As competitive landscapes continue to evolve, one thing is clear: the strategic advantage will go to those who learn to work with AI—not against it.

[salesforcesblog]

📈 How Agentforce Can Revolutionize Sales Performance in Your Business

Are you looking to scale sales without scaling headcount? Want to increase rep productivity, shorten sales cycles, and improve lead conversion—all without overhauling your tech stack?

Meet Agentforce, Salesforce’s autonomous AI platform that empowers your sales team to operate at peak performance with less manual effort and more strategic impact.

At Astreca, we help businesses unlock the power of AI in sales—and Agentforce is one of the most transformative tools we’ve seen.

Here’s how it can drive real business outcomes:

🔍 What Agentforce Delivers for Your Business

✅ 24/7 Sales Enablement & Lead Nurturing Agentforce acts as an always-on SDR—automating personalized outreach, following up with leads, and handling inquiries instantly via Slack, SMS, and more.

✅ Data-Driven Sales Coaching at Scale Forget inconsistent coaching. Agentforce trains on your company’s data to deliver tailored feedback to each rep—improving skills, confidence, and close rates without adding management layers.

✅ Custom Agents That Work the Way You Do Build AI agents that match your unique workflows. Whether it's qualifying leads, suggesting next best actions, or syncing tasks across platforms—Agentforce adapts to your processes.

✅ Smarter, Faster Decision Making Integrated with Salesforce, Einstein, and (optionally) Data Cloud, Agentforce analyzes behavior, identifies sales signals, and provides your team with real-time recommendations that accelerate deals.

✅ Secure, Controlled AI Unlike generic AI tools, Agentforce operates within clearly defined guardrails. You control the tone, actions, and boundaries—keeping data secure and decisions aligned with your business goals.

📊 Measurable Business Benefits

Boost Sales Productivity without increasing team size

Reduce Time-to-Close with intelligent deal support

Improve Conversion Rates through personalized, automated follow-ups

Lower Operational Costs by automating manual tasks and workflows

Enhance Customer Experience through timely, consistent communication

🆚 Agentforce vs. Copilot (Microsoft)

FeatureAgentforce (Salesforce)Copilot (Microsoft)FocusSales automation & AI agentsDocument & task automationPlatform IntegrationSalesforce, Slack, custom workflowsOffice 365 (Word, Excel, Outlook, Teams)PersonalizationHigh (trained on your sales data)Medium (document-level assistance)Ideal Use CaseSales teams, pipeline management, lead conversionProductivity, communication tasks

Together, these tools can complement each other—but Agentforce is the only one built specifically to drive sales.

🔗 Learn More

📘 Dive deeper into the use cases and real-world benefits: How Agentforce Is Changing Sales for Reps

🔧 Ready to transform your sales team with Agentforce? Astreca Consulting specializes in helping growing businesses implement AI-powered sales strategies with Salesforce. We tailor each solution to your team, your tools, and your goals.

Let’s build smarter sales—together.

#Salesforce #Agentforce #SalesAI #RevenueGrowth #BusinessIntelligence #SalesTransformation #Astreca #SmartSelling

[jcmarchi]

AI-driven admin analytics: Tackling complexity, compliance, and customization

New Post has been published on https://thedigitalinsider.com/ai-driven-admin-analytics-tackling-complexity-compliance-and-customization/

AI-driven admin analytics: Tackling complexity, compliance, and customization

As productivity software evolves, the role of enterprise IT admins has become increasingly challenging. 

Not only are they responsible for enabling employees to use these tools effectively, but they are also tasked with justifying costs, ensuring data security, and maintaining operational efficiency. 

In my previous role as a Reporting and Analytics Product Manager, I collaborated with enterprise IT admins to understand their struggles and design solutions. This article explores the traditional pain points of admin reporting and highlights how AI-powered tools are revolutionizing this domain.

Key pain points in admin reporting

Through my research and engagement with enterprise IT admins, several recurring challenges surfaced:

Manual, time-intensive processes: Admins often spent significant time collecting, aggregating, and validating data from fragmented sources. These manual tasks not only left little room for strategic planning but also led to frequent errors.

Data complexity and compliance: The explosion of data, coupled with stringent regulatory requirements (e.g., GDPR, HIPAA), made ensuring data integrity and security a daunting task for many admins.

Unpredictable user requests: Last-minute requests or emergent issues from end-users often disrupted admin workflows, adding stress and complexity to their already demanding roles.

Limited insights for decision-making: Traditional reporting frameworks offered static, retrospective metrics with minimal foresight or actionable insights for proactive decision-making.

Optimizing LLM performance and output quality

The session focuses on enhancing outcomes for customers and businesses by optimizing the performance and output quality of generative AI.

Building a workflow to solve reporting challenges

To address these pain points, I developed a workflow that automates data collection and improves overall reporting efficiency. Below is a comparison of traditional reporting workflows and an improved, AI-driven approach:

Traditional workflow:

Data collection: Manually gathering data from different sources (e.g., logs, servers, cloud platforms).

Data aggregation: Combining data into a report manually, often using Excel or custom scripts.

Validation: Ensuring the accuracy and consistency of aggregated data.

Report generation: Compiling and formatting the final report for stakeholders.

Improved workflow (AI-driven):

Automation: Introducing AI tools to automate data collection, aggregation, and validation, which significantly reduces manual efforts and errors.

Real-Time Insights: Integrating real-time data sources to provide up-to-date, actionable insights.

Customization: Providing interactive dashboards for on-demand reporting, enabling admins to track key metrics and make data-driven decisions efficiently.

Evolution with AI capabilities: Market research insights

Several leading companies have successfully implemented AI to transform their admin reporting processes. Below are examples that highlight the future of admin reporting:

Microsoft 365 Copilot

Microsoft’s AI-powered Copilot integrates with its suite of apps to provide real-time data insights, trend forecasting, and interactive visualizations. 

This proactive approach helps IT admins make data-driven decisions while automating manual processes. By forecasting trends and generating real-time reports, Copilot allows admins to manage resources and workloads more effectively.

Salesforce Einstein Analytics

Salesforce Einstein leverages advanced AI for predictive modeling, customer segmentation, and enhanced analytics. 

Admins can forecast future trends based on historical data and create personalized reports that directly impact strategic decision-making. This enables actionable insights that were previously difficult to uncover manually.

Box AI agents

Box’s AI agents autonomously collect, analyze, and report data. These agents detect anomalies and generate detailed reports, freeing admins to focus on higher-priority tasks. By automating complex reporting processes, Box’s AI agents enhance both speed and accuracy in decision-making.

How generative AI is revolutionizing drug discovery and development

Discover how generative AI is transforming drug discovery, medical imaging, and patient outcomes to accelerate advancements with AstraZeneca

Future capabilities and opportunities

Looking ahead, several emerging capabilities can further unlock the potential of admin reporting:

Seamless data integration: AI-powered tools enable organizations to unify data from disparate systems (e.g., cloud storage, internal databases, third-party applications), providing a holistic view of critical metrics and eliminating the need for manual consolidation.

AI-powered decision support: Context-aware AI can offer personalized recommendations or automate complex workflows based on historical patterns and operational context, reducing manual intervention while enhancing accuracy.

Automated compliance checks: AI tools can continuously monitor compliance with evolving regulations, automatically generating compliance reports to keep organizations secure and up-to-date.

Security and performance monitoring: AI can detect unusual patterns in data, such as unexpected traffic spikes or system anomalies, allowing admins to proactively address potential security threats or failures before they escalate.

Interactive dashboards and NLP: By incorporating natural language processing (NLP), AI tools enable admins to query data using plain language and receive intuitive, visual reports, streamlining analysis and enhancing user experience and usability.

Conclusion

The transformation of admin reporting from manual workflows to AI-driven insights has revolutionized IT operations. By automating routine tasks, delivering real-time insights, and enhancing predictive capabilities, AI empowers IT admins to focus on strategic initiatives while ensuring data accuracy and compliance.

As organizations continue to adopt advanced AI capabilities, the future of admin reporting holds exciting possibilities, from seamless data integration to adaptive, context-aware decision-making tools. 

These innovations will not only enhance efficiency but also enable organizations to thrive in an increasingly complex, data-driven world.

[christianbale121]

What Is AI Copilot Development and How Can It Revolutionize Your Business Operations?

Artificial Intelligence (AI) is no longer a futuristic concept—it's a present-day business asset. Among the most transformative innovations in this space is the rise of AI Copilots. These intelligent, task-oriented assistants are rapidly becoming indispensable in modern workplaces. But what exactly is AI Copilot development, and why should your business care?

In this blog, we’ll explore what AI Copilot development entails and how it can dramatically streamline operations, increase productivity, and drive strategic growth across your organization.

What Is an AI Copilot?

An AI Copilot is a specialized AI assistant designed to work alongside humans to perform specific tasks, offer contextual support, and automate complex workflows. Unlike general chatbots, AI Copilots are tailored for deeper integration into business systems and processes. Think of them as highly intelligent digital coworkers that can analyze data, suggest decisions, and execute actions in real time.

Some popular examples include:

GitHub Copilot for software development

Microsoft 365 Copilot for productivity tools

Salesforce Einstein Copilot for CRM tasks

These solutions are context-aware, learn from usage patterns, and adapt over time—making them much more than simple bots.

What Is AI Copilot Development?

AI Copilot development is the process of designing, building, and deploying AI-powered assistants that are customized to meet the unique needs of your business. It involves integrating AI models (such as GPT-4 or custom LLMs) with enterprise data, APIs, and workflows to create a seamless digital assistant experience.

Key components of Copilot development include:

Requirement analysis: Understanding specific user roles and pain points

Model selection & training: Choosing the right AI model and fine-tuning it with proprietary data

System integration: Connecting the copilot to tools like CRMs, ERPs, emails, analytics dashboards, and more

User interface (UI/UX): Creating intuitive chat-based or voice-based interfaces

Security & governance: Ensuring data privacy, access controls, and compliance

How AI Copilots Can Revolutionize Your Business Operations

Here’s how implementing AI Copilots can create tangible improvements across your organization:

1. Boost Productivity and Reduce Repetition

AI Copilots can handle routine tasks—scheduling meetings, summarizing reports, updating records—freeing your employees to focus on high-value work. The result? Less burnout and more innovation.

2. Accelerate Decision-Making

With real-time access to data and contextual recommendations, AI Copilots help employees make informed decisions faster. For example, a finance copilot could highlight trends and flag anomalies in your financial statements instantly.

3. Enhance Customer Experience

Customer service copilots can analyze prior interactions, pull up relevant data, and assist agents in delivering personalized support. Some can even resolve issues autonomously.

4. Unify Disparate Systems

Copilots can act as the connective tissue between siloed systems, allowing users to retrieve data or trigger workflows across multiple platforms without switching interfaces.

5. Enable Continuous Learning and Adaptation

With AI learning from user interactions and outcomes, copilots get smarter over time. This leads to continuously improving performance and relevance.

Use Cases Across Industries

Healthcare: AI Copilots assist clinicians by summarizing patient histories, suggesting treatment options, and automating administrative tasks.

Retail: Merchandising copilots forecast demand, optimize pricing strategies, and automate inventory planning.

Finance: AI assistants help with fraud detection, financial planning, and client advisory services.

Legal: Drafting contracts, summarizing cases, and reviewing documents can be made faster and more accurate with AI copilots.

Getting Started with AI Copilot Development

If you’re considering AI Copilot development for your business, start by:

Identifying critical workflows where automation or assistance would create the most value

Choosing a reliable development partner or platform with expertise in AI and enterprise systems

Starting small, then scaling with more complex tasks and integrations as the solution matures

Final Thoughts

AI Copilots are not just tools—they're strategic assets that can transform how your business operates. From eliminating repetitive work to unlocking new levels of efficiency and insight, investing in AI Copilot development could be the smartest move your organization makes this year.

[ayan-softwares]

Introducing Einstein 1 and Copilot Studio - A Game-Changer in AI-Powered Productivity! Get ready to supercharge your productivity and take your business to new heights with Salesforce's latest innovations. Einstein 1 and Copilot Studio are here to revolutionize the way you work, making your tasks smarter, faster, and more efficient than ever before. Our latest blog post dives deep into these incredible innovations. Read the full blog post here- https://bit.ly/3ZfLJCt

[generativeinai]

Generative AI in Customer Service Explained: The Technology, Tools, and Trends Powering the Future of Customer Support?

Customer service is undergoing a radical transformation, fueled by the rise of Generative AI. Gone are the days when customer queries relied solely on static FAQs or long wait times for human agents. With the emergence of large language models and AI-driven automation, businesses are now delivering faster, smarter, and more personalized support experiences.

But how exactly does generative AI work in customer service? What tools are leading the change? And what trends should you watch for?

Let’s explore the technology, tools, and trends that are powering the future of customer support through generative AI.

1. What Is Generative AI in Customer Service?

Generative AI refers to AI systems that can generate human-like responses, ideas, or content based on trained data. In customer service, it means AI tools that can:

Understand and respond to customer queries in real time

Provide contextual, conversational assistance

Summarize long interactions

Personalize responses based on customer history

Unlike traditional rule-based chatbots, generative AI adapts dynamically, making interactions feel more human and engaging.

2. Core Technologies Powering Generative AI in Support

A. Large Language Models (LLMs)

LLMs like GPT-4, Claude, and Gemini are the foundation of generative AI. Trained on massive datasets, they understand language context, tone, and nuances, enabling natural interactions with customers.

B. Natural Language Processing (NLP)

NLP allows machines to comprehend and interpret human language. It's what enables AI tools to read tickets, interpret intent, extract sentiment, and generate suitable responses.

C. Machine Learning (ML) Algorithms

ML helps customer service AI to learn from past interactions, identify trends in support tickets, and improve performance over time.

D. Knowledge Graphs and RAG (Retrieval-Augmented Generation)

These enhance the factual accuracy of AI outputs by allowing them to pull relevant data from enterprise databases, manuals, or FAQs before generating responses.

3. Popular Generative AI Tools in Customer Service

Here are some of the leading tools helping companies implement generative AI in their support workflows:

1. Zendesk AI

Integrates generative AI to assist agents with reply suggestions, automatic ticket summarization, and knowledge article recommendations.

2. Freshdesk Copilot

Freshworks’ AI copilot helps agents resolve issues by summarizing customer conversations and recommending next steps in real-time.

3. Salesforce Einstein GPT

Einstein GPT offers generative AI-powered replies across CRM workflows, including customer support, with real-time data from Salesforce’s ecosystem.

4. Intercom Fin AI Agent

Designed to fully automate common customer queries using generative AI, Fin delivers highly accurate answers and passes complex tickets to agents when necessary.

5. Ada

An automation platform that uses generative AI to build customer flows without coding, Ada enables instant support that feels personal.

4. Top Use Cases of Generative AI in Customer Support

✅ 24/7 Automated Support

Generative AI enables round-the-clock support without human intervention, reducing reliance on night shift teams.

✅ Ticket Summarization

AI can summarize lengthy email or chat threads, saving agents time and enabling faster resolution.

✅ Response Drafting

AI can instantly draft professional replies that agents can review and send, speeding up response times.

✅ Knowledge Article Creation

Generative models can help generate and update help articles based on customer queries and ticket data.

✅ Intent Detection and Routing

AI detects the user's intent and routes the query to the right department or agent, reducing miscommunication and wait times.

5. Business Benefits of Generative AI in Customer Service

Increased Efficiency: AI reduces the time spent on repetitive queries and ticket categorization.

Cost Savings: Fewer agents are required to manage high ticket volumes.

Improved CX: Customers get faster, more accurate answers—often without needing to escalate.

Scalability: AI handles volume spikes without service dips.

Continuous Learning: AI models improve over time with every new interaction.

6. Emerging Trends Shaping the Future

1. AI-Human Hybrid Support

Companies are combining generative AI with human oversight. AI handles simple queries while humans address emotional or complex issues.

2. Multilingual Support

LLMs are becoming fluent in multiple languages, enabling instant global customer support without translation delays.

3. Emotionally Intelligent AI

AI is beginning to detect customer tone and sentiment, allowing it to adjust responses accordingly—being empathetic when needed.

4. Voice-Powered AI Agents

Voice bots powered by generative AI are emerging as a new frontier, delivering seamless spoken interactions.

5. Privacy-Compliant AI

With regulations like GDPR, companies are deploying AI models with built-in privacy filters and localized deployments (e.g., Private LLMs).

7. Challenges and Considerations

Despite the advantages, generative AI in customer service comes with some challenges:

Hallucinations (Inaccurate Responses): LLMs can sometimes fabricate answers if not grounded in verified knowledge sources.

Data Security Risks: Sharing sensitive customer data with third-party models can raise compliance issues.

Need for Continuous Training: AI systems must be regularly updated to stay relevant and accurate.

Enterprises must monitor, fine-tune, and regulate AI systems carefully to maintain brand trust and service quality.

8. The Road Ahead: What to Expect

The future of customer service is AI-augmented, not AI-replaced. As generative AI tools mature, they’ll shift from assisting to proactively resolving customer needs—automating complex workflows like returns, disputes, and onboarding. Businesses that embrace this evolution today will lead in both cost-efficiency and customer satisfaction tomorrow.

Conclusion

Generative AI in customer service is redefining what excellent customer service looks like—making it faster, more personalized, and increasingly autonomous. Whether you're a startup or a global brand, adopting these tools early can offer a serious competitive edge.

[nuadox]

Faster EV charging in cold weather: Engineers develop breakthrough battery technology

- By Nuadox Crew -

University of Michigan engineers have developed a breakthrough battery manufacturing process that enables electric vehicles to charge 500% faster in temperatures as low as -10°C (14°F).

This innovation, detailed in Joule, combines a stabilizing electrode coating with microscale channels to prevent performance-degrading lithium plating.

The technique ensures fast charging without sacrificing energy density, a major hurdle for EV adoption in cold climates. The modification allows batteries to retain 97% capacity even after 100 fast-charge cycles in subfreezing temperatures.

The team’s previous research improved room-temperature charging using laser-drilled channels in the anode. However, in cold weather, the electrolyte’s reaction with the electrode formed a layer that slowed charging. By adding a 20-nanometer lithium borate-carbonate coating, researchers significantly improved cold-weather performance.

The study addresses a key consumer concern: winter range loss and slow charging. With EV interest declining in the U.S. due to these issues, this advancement could help boost adoption.

The research is supported by the Michigan Economic Development Corporation, and Arbor Battery Innovations has licensed the technology for commercialization.

Header image credit: Microsoft Copilot (AI-generated).

Read more at University of Michigan

Scientific paper: Tae H. Cho et al, Enabling 6C fast charging of Li-ion batteries at sub-zero temperatures via interface engineering and 3D architectures, Joule (2025). DOI: 10.1016/j.joule.2025.101881

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