Generative AI for Employee Productivity

Inarguably, generative AI has the potential to dramatically elevate and augment worker productivity while it supports collapsing morale among workers trapped in tedious manual processes.

For example, the AI service desk was a ripe, early opportunity for generative AI to grow capabilities while controlling headcount. AI allows organizations to turn over routine functions to chatbots and virtual assistants. Elevated by advanced Natural Language Processing (NLP) and machine learning (ML) they are rapidly automating self-service, helping remote and on-site workers alike escape repetitive and paper-based tasks while raising productivity metrics. An overarching goal of a company should be to maximize productivity without sacrificing product or service quality or customer satisfaction while using company resources efficiently.

According to an MIT Management, report, as AI capabilities draw nearer to those of humans, we confront heretofore unknown surprises and challenges. Like leveraging conversational AI for employee productivity, generative AI has the potential to unleash superior workplace productivity, empowering workers to achieve more tasks in a shorter time frame, and streamline collaboration—automating repetitive tasks while opening a heretofore dead-end job to higher-value opportunities and promotions.

Reliance on outdated manual methods impacts more than mere statistics, though the numbers are grim enough. The impact on employees themselves—eager to prove their merit, grow their skills, and earn promotion is inescapable. Enthusiastic workers become cogs—stuck in repetitive tasks that the early Industrial Revolution would easily recognize. The statistics illustrate their pain: 90% of companies fail to optimize digital technology, leaving a gaping crater in morale and a busywork-driven slump in attitude. Resentment and turnover are no surprise.

Identifying Time-Consuming Tasks

It’s ironic that companies still believe commitment to inefficient manual work makes for smart budgeting when the reality is that lack of automation is a costly brake on productivity. Soaring error rates caused by inaccurate data entry on repetitive tasks require tedious rework.

AI can analyze vast amounts of data to identify trends, patterns, and potential risks, providing project managers with valuable insights and enabling smart, proactive decision-making.

The Cost of Inefficiency

In today’s rapidly changing business landscape, companies that don’t incorporate AI into their processes face a significant disadvantage. AI is fast becoming an indispensable tool for organizations seeking to optimize their operations and maintain a competitive edge. It offers the potential to revolutionize the way companies operate, providing valuable insights and enabling them to make better, more informed decisions.

Decreased efficiency and productivity

A study by McKinsey quoted by MIT states that AI can increase labor productivity by up to 40%. Only calculate the cost of losing that advantage.

Missed opportunities for growth and innovation

AI can provide companies with valuable insights to help them make better decisions and grow their business. Companies that don’t embrace AI miss out on these opportunities and will likely fall behind. Seventy-three percent of US companies already adopted AI in 2023 in at least some areas of their business. One year after ChatGPT hit the market, more than half of the companies surveyed (54%) have implemented GenAI in some areas of their business.

Reduced operational cost

This is another benefit that companies lose by delaying AI adoption. By automating repetitive and manual tasks, AI can help to reduce operational costs by improving efficiency, reducing errors, and streamlining processes. This can result in cost savings by reducing labor costs, reducing the need for overtime pay, and increasing productivity. An Accenture report found that AI can reduce operational costs by up to 60%.

Difficulty attracting and retaining top talent

This benefit is brought out in a study by Deloitte. Companies If your company isn’t investing in AI, you could miss your ability to compete with other companies in your industry.

Employees and customers alike grow dissatisfied with sluggish turnarounds and subpar experiences. Frontline teams are inundated with routine queries, forcing them to prioritize low-level issues over more strategic support. Introducing AI into the workplace modernizes the employee experience by simplifying processes and automating workflows—creating an efficient environment that boosts employee satisfaction and turns workers into brand champions. Learn how to leverage AI for EX and start reaping the benefits.

Increased Focus on High-Value Tasks

The inarguable tech talent shortage is challenging many organizations. Organizations are trying to attract talent with future-forward skills, but this doesn’t address common pain points. The temptation to take shortcuts for software development and address high-level tech talent to legacy systems carries a cost of impacting growth. It can increase the difficulty of companies to deliver on long-term stakeholder commitments—looking backward rather than future-forwards. Organizations find themselves playing catch-up and explain away unmet commitments for tech-enabled growth.

To increase commitment to high-value tasks, leaders must overcome any shortage of the tech talent that can handle these challenges. That means focusing on the full potential of talent and skills a worker is capable of, even beyond their formal job description.

Reducing Burnout and Fatigue

Overburdened by mind-numbing manual tasks, employees enjoy limited time and energy for creative problem-solving, strategic thinking, and productive work that propels the business forward. Burnout is a common, painful occupational occurrence.

Reducing burnout is key to employee wellness, and AI offers immense potential to take on this stubborn condition. Burnout can be reduced by AI and commitment to personalized workloads. How? AI algorithms analyze individual work patterns and preferences, giving organizations the wherewithal to optimize workloads and save a worker’s self-esteem and possibly career.

Automation of repetitive tasks might be the most proven remedy for burnout, boosting employee confidence and interest in challenging tasks that can bring positive attention. Also, AI-powered chatbots and virtual assistants can actually intervene to offer immediate mental health support, passing on resources and reassurance to employees to battle stress and anxiety.

Improved work-life balance is a proven, welcome approach too. AI can assist in scheduling and workload management, leaving time for personal life

Enhancing Creativity and Innovation

Innovation is the lifeblood of organizations. As organizations harness the power of AI to gain a competitive edge, recognition is growing in the role that AI can play in unlocking creativity and innovation. Rather than stifling human creativity, AI has the potential to augment and amplify it. AI can analyze vast amounts of data, generate novel ideas, and offer inspiration, AI fosters creative exploration and experimentation, helping teams to unleash original insights and possibilities for breakthroughs.

Automating routine tasks optimizes workflows and processes, freeing up time and resources for creative endeavors. Offloading repetitive, tedious tasks to AI-powered systems lets employees devote more time and energy to brainstorming, prototyping, and collaborative ideation.

Nearly four in 10 users of generative AI in the workplace say they are saving up to 10 hours a week, says a new report. Contentful’s survey among 820 people across the world found that 37% of daily gen AI users are saving between five to 10 hours a week. Nearly four in 10 users of generative AI in the workplace say they are saving up to 10 hours a week, as a new report revealed the widespread use of the technology at work. Another 38% reported saving between one to almost five hours of time at work thanks to gen AI tools, according to the report.

Real-World Examples and Statistics

Auto manufacturing giant Toyota offers a prime example of high-end productivity in real life. The company had very humble beginnings but has grown to become one of the largest and most productive car manufacturers in the world. Its Toyota Production System (TPS) is one of the main reasons for that. TPS implements some of these principles:

• An environment of constant learning and improvement
• Standardizing systems for consistent quality
• Elimination (not just reduction) of waste

By enacting TPS practices in its manufacturing every day, Toyota ensures the company is continually improving and operating at a high standard while resources are not being lost.

Accelerating J.P. Morgan Data Product Development in the Age of AI

Artificial intelligence is fast becoming a generation-defining technological breakthrough. Hear how technology leaders from the JP Morgan Payments Data team are putting AI to use, from encouraging agile technical experimentation to driving real business outcomes.

Delta Airlines

Delta Airlines is recognized for its dedication to enhancing customer experience and service. The airline has successfully integrated generative AI into the customer experience. They have incorporated an AI chatbot called “Ask Delta,” and witnessed a 20% drop in call center volume.

Pharmaceuticals

Pharmaceutical companies like Amgen and Insilico Medicine, as well as academic researchers, are utilizing generative AI to design proteins for medicines. Predicting the folding of proteins has been a significant challenge for geneticists and pharmaceutical developers for decades..

Time Savings Across Different Roles

Microsoft has done deep research into AI adoption to discover what’s working for people, emerging challenges, and how organizations can evolve from early experimentation to broad business transformation. This information from Copilot customers reveals which job functions gain early AI advantages.

Microsoft created a survey on ongoing Copilot Usage in the Workplace. Results were revealing: Workers in cybersecurity, product development, and sales/business development reported the most time savings daily, while those in procurements, legal, and supply chain reported the least. Across the board, 11 minutes of time savings daily is all it takes for most people to feel like AI is useful.

Impact on Overall Productivity

The Brookings Institution wrote up an explainer to examine the prospects of AI for the workforce, businesses, and the overall economy. They asked: Could this technology power future growth through improved productivity?

Recent advances have motivated massive investments and a huge span of new applications. Generative AI emerged through key breakthroughs such as “transformer architecture,” coupled with huge computing power made possible by advanced AI chips. Case studies show that generative AI profoundly increased the productivity of call center customer support agents, software developers, and mid-level professionals.

The challenges of generative AI can be overcome through a well-planned implementation approach, collaboration, and regulatory guidance. The future of generative AI has the potential to transform our personal and professional lives. According to a recent survey of over 500 senior IT leaders, most of them (67%) plan to prioritize generative AI within the next 18 months for their company, with one-third (33%) saying it will be their top priority.

Addressing Implementation Barriers

A McKinsey survey discovered that foundational barriers are still holding back AI implementation—though most organizations are beginning to adopt AI in their businesses. Apparently, the critical factor in using AI effectively is an organization’s success in transforming core business through digitization. Of the nine capabilities asked about, robotic process automation, computer vision, and machine learning are most commonly deployed.

Ensuring Data Privacy and Security

Data Security and Privacy are among crucial challenges that companies encounter while implementing generative AI. Generative AI models rely heavily on huge data sets to create accurate and meaningful outcomes. However, handling such huge and sensitive data can pose privacy and security concerns.

To handle data security and privacy concerns, businesses must ensure strict adherence to ethical guidelines and compliance with data protection laws to protect sensitive data from potential breaches.

Ethical and Bias Considerations are also deeply concerning. Generative AI models learn from input data, and if training data fed into the system is biased, the output delivered by the AI models will also be biased.

Managing Change and Employee Resistance

Artificial Intelligence (AI) has been impacting everything from customer service to healthcare. However, one area where AI is significantly driving change is change management, which guides individuals, teams, and organizations through transitions to successful outcomes, by means of adoption strategies and tools. Sometimes traditional change management doesn’t work when dealing with the complexities and uncertainties of change.

AI can create visuals in the form of charts and graphs or enhance communication during the change management process. AI technologies like natural language processing (NLP) and machine learning can refine internal and external communication. Users can create prompts in chatbots that answer with tutorials, answers created by humans, and more to address issues immediately, mitigate concerns, and keep everybody in the loop about the change process.

What is an enterprise copilot? Aisera defines it as “your ultimate virtual assistant with proactive notifications and resolutions.”

Aisera’s AI Copilot extends the capabilities of AiseraGPT to serve as the ultimate proactive concierge bot, ensuring users stay atop their objectives. To help meet goals, Aisera provides customizable prompts, skills, and workflows. Aisera’s enterprise AI Copilot smartens every interaction, increases accuracy, and personalizes your selections. To keep workers ahead of the curve, Aisera’s AI Copilot provides timely alerts and recommendations as it automates resolutions—ensuring that you’re consistently informed and well-prepared for whatever challenges come your way.

Enterprise AI copilots fit the “digital assistant” definition, but unlike digital assistants of the past, today’s copilots take a smarter approach to the digital workplace. Past digital assistants followed logic-based chatbot features, but now, AI-powered enterprise copilots deliver a more powerful solution by incorporating sophisticated conversational features such as Retrieval Augmented Generation (RAG) and Natural Language Processing (NLP) that are capable of handling more complex queries. Benefits include the following:

Features and Capabilities

Copilots offer improved knowledge discovery, the ability to streamline tasks, reduced context switching, guided attention and much more. Aisera enables a high-value single interface with its copilot and a unified interface for all user requests.

You can build skills in Aisera’s AI Copilot Studio to save time for users while increasing productivity. This high-utility studio merges the power of prompts with workflows to create context-aware skills. Enterprises can employ such specialized skills as creating an email template and generating an expense report to save your users time and effort.

Aisera’s AI Copilot is built upon the TRAPS Framework to accelerate time-to-value of generative AI while minimizing potential risks. Aisera delivers an enterprise AI Copilot that is trusted, responsible, auditable, private, and secure. The benefits include increasing user productivity, boosting accuracy for fine-tuning prompts, saving user time via the Aisera Copilot studio, and gaining the convenience and agility of a single interface.

Real-World Use Cases

Aisera’s AI Copilot use cases are too numerous to list here, but you can simply call it a transformative way to enhance productivity and solve problems. Copilots provide instant guidance for tasks that would otherwise absorb vast amounts of time and resources. These use cases include:

•  Automating instant responses to frequently asked questions, assisting account reps in addressing mundane queries in real-time, and even solving complex issues based on historical data.
•  Efficiency and cost savings in retail and e-commerce, insurance, healthcare, telecom & utilities, hospitality & travel, banking & finance—virtually no limit.
• Helping software developers and programmers code more quickly and accurately, lowering error rates and improving performance.
•  Writing assistants that offer real-time support and suggestions for perfecting grammar, punctuation, style, and clarity.
• Personal finance insight assists people in managing their budgets by offering insights, expense tracking, investment recommendations, and customized financial advice.
• An AI health coach copilot assists people in optimizing their fitness, training, and healthy eating, working with clients to achieve specific plans and results.

Measuring Success

Enterprise-grade copilots are proliferating, as major global companies, including Salesforce, Microsoft 365, and ServiceNow unite operations across diverse systems and become part of their offerings. These copilots facilitate collaboration, manage and perform tasks, and boost productivity for employees and customers, enhancing morale and satisfaction.

The potential for AI-driven productivity lies in the future. Economists use the term general purpose technology (GPT) for an innovation that has widespread application, keeps improving in performance, and generates innovations in industries using the GPT. Past GPTs include electric motors and personal computers. That means AI will be a GOT and generate astounding productivity improvement ahead.

1- Emerging Trends

As influential as generative AI has quickly become, early adoption rates suggest a far more all-encompassing future that affects various sectors, from education to virtual reality. Learn more about today’s emerging trends that will likely play a role in how generative AI is used down the road.

Marketing and Sales Automation

Generative AI is frequently used for marketing, sales, and similar creative content generation tasks at this time.

Support for Product and Software Development

Whether an experienced developer or a novice with no coding knowledge, several generative AI tools now assist with different programming tasks.

Increasing Educational Use and Impact

Students are already using tools like ChatGPT to answer homework questions or write essays. Teachers and parents are concerned this could negatively impact students’ education, but it could also benefit them in learning how to implement AI solutions as learning tools.

Embedded AI Applications

Big tech companies like Microsoft now offer AI assistants that guide user search experiences on the web or support content generation and task completion in Office suite solutions like Microsoft 365. Google has followed suit with Gemini.

Contextualized, Global Generative AI

The majority of generative AI models have time-based and linguistic limitations, but some vendors expand their tools to handle more languages and dialects.

2- Future Innovations

Artificial intelligence can automate many aspects of business operations, ranging from a few simple tasks (think status updates) to complex, multilayered inventory processes. Expect to see this embedded generative AI approach as an almost universal part of online experience management in the coming years.

Growth in Multimodality

Accepting inputs and generating outputs in multiple formats is becoming a top priority for consumers—and AI vendors are taking notice.

Wider Adoption of AI as a Service

As the adoption of generative AI technology increases, more businesses will feel the pain of falling behind competitors.

Significant Workforce Disruption and Reformation

Most experts and tech leaders are torn on whether AI will be a net positive or net negative for employees themselves.

Increasing Regulatory, Ethical, and Societal Pressures

In March 2024, the EU AI Act was officially approved by the EU Parliament.

Bigger Emphasis on Security, Privacy, and Governance

More tools like Watson’s OpenScale may pop up independently or as part of generative AI solutions packages.

Greater Focus on Quality and Hallucination Management

As governments, regulatory bodies, businesses, and users uncover dangerous, stolen, inaccurate, or otherwise poor results, they’ll continue to put pressure on AI companies to improve data sourcing and training processes, output quality, and hallucination management.

Widespread Embedded AI for Better Customer Experiences

Many companies are already embedding generative AI into their enterprise and customer-facing tools to improve internal workflows and external user experiences—happening with established models like GPT-3.5 and GPT-4.

3- Long-Term Benefits

Creative Collaboration: Facilitate creativity by enabling artists, writers, and musicians to co-create with AI algorithms.

Virtual and Augmented Reality: Enhance immersive experiences in virtual reality (VR) and augmented reality (AR) environments.

Urban Planning and Design: Assist city planners and architects in creating sustainable, efficient, and aesthetically pleasing urban spaces.

Drug Discovery and Development: Accelerate drug discovery and development by simulating molecular interactions, predicting drug efficacy, and optimizing drug designs,

Legal Research and Document Review: Streamline legal research and document review processes; analyze vast amounts of legal texts, case law, and regulatory documents to generate insights, identify precedents, and assist in preparing arguments and drafting legal documents.

Environmental Conservation: Support environmental conservation efforts by analyzing satellite imagery, ecological data, and climate models for insights into environmental trends, habitat conservation priorities, and sustainable land management practices.

Artificial intelligence tools like chatbots helped boost worker productivity at one tech company by 14%, according to new research from Stanford and MIT that was first reported by Bloomberg. The study is thought to be the first major real-world application of generative AI in the workplace. Researchers measured the productivity of more than 5,000 customer support agents, based primarily in the Philippines, at a Fortune 500 enterprise software firm over the course of a year. Improvement was even greater for “novice and low-skilled workers” who got their work done 35% faster.

Why It Matters

In some cases, ability to use AI surpassed real-life work experience: Customer service agents with two months of AI customer service support performed as well or better than agents with over six months of experience working without AI. Less experienced workers benefit from AI by taking its recommendations to get up to speed and learn skillsets that usually take time to emerge with experience.

The Role of Skilled Workers in Modern Enterprises

AI tools benefit from the best and brightest workers training the AI itself by providing examples of best practices, which was then implemented. Generative AI in UX design with immense advantages waiting in the wings.

The Potential of Generative AI in Enhancing Expertise

Generative AI can transform mundane interactions into seamless and user-friendly experiences. Algorithms handle repetitive tasks workers designers can focus more on strategic vision plus enable mass personalization through understanding users.

For Best Results, Use Cognitive Effort and Experts’ Judgment

Generative AI is uplevelling how skilled teams communicate, manage tasks, and make decisions. By automating repetitive processes and augmenting human capabilities, AI empowers employees to focus on high-value activities that drive innovation and growth. The benefits can be transformative—from improved efficiency and collaboration to better planning and resource allocation.

Balancing Automation and Human Insight

Generative AI tools are heavily reliant on the inputs from their users. Generative AI has made content generation more accessible and efficient for everyone, but it still requires an expert to oversee its outputs. A human-in-the-loop (HITL) can validate outputs and apply quality assurance—especially in domains where specialized knowledge and expertise are paramount such as medical or legal research.

Leveraging AI for Complex Problem-Solving

The future is about how we can work alongside AI to create a more efficient, creative, and adaptable world. AI tools like ChatGPT are powerful allies in the ideation process, enabling teams to generate ideas faster, explore problems from new angles, and uncover innovative solutions. This perspective encourages professionals to view AI as a valuable asset that complements their complex, problem-solving  skills, rather than a replacement. By focusing on unique knowledge that AI cannot replicate, professionals can secure their place in the future job market.

Interface Design, Onboarding, Role Reconfiguration, and a Culture of Accountability

The main challenges of designing AI interfaces are to build trust and transparency with users, and design for natural, engaging interactions, simplicity, and clarity. Explain how the AI system works, which data it uses, and how it makes decisions or recommendations. Also, provide feedback, guidance, and control to your users, so they can understand, adjust, and correct the AI system if needed. When onboarding, respect your users’ privacy and security and follow ethical and legal standards.

Designing User-Friendly AI Interfaces

First, understand your users, their goals and needs and how they will use your AI system. Second, define your AI value proposition and clarify the purpose, functionality, and benefits of your system. Third, design for transparency and trust with your users; explain how your system works, the data it uses, and the process for making decisions or recommendations. Fourth, design for natural and engaging interactions—and add to that simplicity and clarity. Fifth, design a scalable, adaptable system. And designed to handle errors, improve accuracy, perform exceptionally, and provide fallback options. Test and evaluate frequently.

Effective Onboarding Strategies for AI Tools

AI can help identify and remove redundant steps in the onboarding process based on user interactions with the product. AI user onboarding introduces product or company functionality to users and drives product adoption. AI is also better at addressing unique user needs with high-quality resources in less time and at a lower cost at all stages of onboarding, reducing time to value.

Adapting Roles to Maximize AI Benefits

While AI may disrupt certain jobs, it also opens up new opportunities: AI specialists, data scientists, machine learning engineers, and AI ethicists are emerging. These roles require individuals with expertise in AI technologies, data analysis, and ethical considerations surrounding AI deployment. Combining technical expertise with human skills is an ideal goal for the role of AI in the workplace.

Fostering a Culture of Accountability and Trust

When users realize that AI systems are developed with their best interests at heart and in a culture of accountability, they are more inclined to trust the technology and engage with it confidently. Embracing and adhering to the best practices for designing ethical AI user interfaces will prove mutually advantageous to both designers and users.

Case Studies and Real-World Examples

Scan these case studies and learn how:

How Deloitte uses game-changing AI technology during enterprise M&A to drive exponential value.

How IBM enables quickly turning mountains of unstructured data into specific, usable summaries and unlocks the door to smart decision-making for its customers.

Google customer stories on using generative AI to revolutionize business processes for a series of major enterprises, including Wendy’s, Vodafone, GM, and others.

How Aisera championed generative AI initiatives across AMER, EMEA, and APAC to deliver self-service experiences for customers and employees.

Sector-Specific Use Cases

Operations management

On the operations front, AI can help automate back-office tasks like processing invoices, handling documentation and document processing, managing accounts, overseeing the supply chain, and keeping track of inventory.

Customer service

Almost 90% of customers consider an immediate response an essential part of customer service.

Data analysis and predictive analytics

A good example is e-commerce retail businesses that use large amounts of customer data to create user profiles.

Marketing

AI can automate marketing tasks like email and text campaigns, managing social media posts, generating personalized product recommendations, or creating social media copies.

Sales

You can use AI to automate sales tasks such as qualifying leads, scheduling appointments with prospects, and auto-generating follow-up emails at fixed intervals. These help your sales team close more deals and increase revenue.

Success Stories

Aisera helps GAP realize its vision with AI Copilot powered by AiseraGPT and generative AI.

Snowflake chooses Aisera to reduce MTTR by 62%, raise average prediction accuracy by 90%—and save 32k agent hours.

Aisera’s generative AI takes on challenges for Twilio, a leading customer engagement platform with a growing volume of employee inquiries.

the integration of generative AI significantly enhances employee productivity by automating repetitive tasks, optimizing workflows, and improving decision-making. This technology empowers organizations to achieve greater efficiency and innovation.

To learn how to bring the power of AiseraGPT and get started with Generative AI to your business, you can book a custom AI demo today!