Artificial Intelligence (AI) is fast becoming a transformational technology, opening up new avenues for business innovation. There has been significant investment in developing meaningful AI technology, and adoption rates are taking an upturn, according to research by McKinsey.

For businesses that are looking to improve productivity and remain competitive, AI could offer a significant benefit—and the opportunities for greater productivity that AI can offer are many and varied.

The current state of business productivity

In recent years, many sectors have seen productivity growth become stagnant. There are a number of possible causes for this, each impacting different sectors in different ways. Arguably the global COVID-19 pandemic played the most significant role leading to the stagnation.

The pandemic and subsequent lockdowns uncovered vulnerable touchpoints within global supply chains. Additionally, the unexpected shift to remote working for many organizations, and the increased responsibilities for employee well-being, meant that some businesses had to redefine workflows almost overnight. A report by the Office for National Statistics (ONS) in the UK highlighted that productivity in the second quarter of 2023 was only 1.3% higher than its pre-pandemic level.

Some research seems to suggest that there is a link between low productivity growth and changes in the way that the workplace is managed. This is especially true of large enterprises such as JP Morgan, which is attempting to reduce remote working and bring employees back to the office in a bid for more efficient operations. Bloomberg recently reported that the financial institution will be asking all employees to return to the office five days a week, having already imposed the same rule on management in 2023.

Historically, emerging technology and its adoption have driven productivity gains in the enterprise. However, the rapidly-changing AI landscape is making it difficult for businesses to track potential benefits. Additional barriers to AI adoption include a reliance on outdated legacy systems, a digital skills gap, and complex, fragmented workflows in many organizations

However, artificial intelligence productivity tools—with their ability to automate tasks, analyze vast datasets, and generate insights—offer a pathway to reverse these trends and unlock new levels of efficiency. By adopting AI tools for productivity, businesses can work to overcome these challenges and achieve an increase in efficiency and productivity.

Opportunities for growth: How does AI increase productivity?

Among all the boosts to productivity that AI can offer, all roads lead back to its versatility and scalability. There is a lot of flexibility in how businesses can deploy AI for productivity, and the tech stack can be tailored to specific needs.

There are three principal AI technologies that can—individually or combined—be used to address certain challenges faced by many enterprises. These are generative AI (GenAI), AI agents, and predictive AI:

GenAI: Large language models (LLMs) and other tools can be used to create content, improve the way a business engages with its customers, and provide tools for simpler ideation. Some of the best AI productivity tools available today, showing how GenAI can drive productivity, include those used by marketing teams to create personalized email campaigns, and financial analysts to get real-time information for their clients.
AI agents: These are systems made up of LLMs, tools, and software that operate autonomously or with little human supervision, and take on tasks that would otherwise require hands-on human operators. Examples of this include virtual assistants that can help schedule events, and customer service assistants that provide expert information and help around the clock.
Predictive AI: Stemming from traditional machine learning, predictive AI with GenAI can take large volumes of historical data and analyze it in real-time, adapting to changes in the external environment. It can help to optimize supply chains, streamline equipment maintenance, and begin to anticipate the needs of customers.

These three technologies can empower a business to enhance decision-making, reduce operation or supply costs, and streamline workflows. Strategic deployment of AI tools unlocks new opportunities for innovation and growth. To stay competitive, businesses must explore how AI can boost productivity and identify the most effective tools to drive measurable impact.

How to use AI for productivity

AI is changing the way that many companies work across various industries. From finance to manufacturing, organizations are leveraging the power of AI tools to overcome complex challenges and take advantage of new opportunities.

Looking at the impact of AI on six key industries—financial services, healthcare and life sciences, the public sector, energy and utilities, manufacturing, and retail and e-commerce—shows how AI has already been used to enhance productivity within these niches.

AI productivity in financial services

AI is fast becoming a transformational technology for improving productivity in financial services. We’ve already seen AI used to enhance the customer journey, providing knowledgeable chat interfaces to help troubleshoot issues. On the other side of the financial fence, AI has also helped to simplify the analysis of large volumes of data for risk assessment purposes.

However, it is in the field of fraud detection that AI has had arguably the largest impact in the finance sector. By leveraging AI-driven predictive analytics, finance companies have been able to proactively identify fraudulent activities. Banks and other financial institutions have started to use machine learning models to analyze financial transactions in real-time, highlighting potentially fraudulent transactions and blocking them before they cause any harm.

Likewise, investment firms have used AI to deliver personalized investment advice, making their services more accessible to a wider customer base.

So, how does AI improve efficiency in finance? The use of AI productivity tools ensures operational excellence, improves the efficiency of workflows, and mitigates risks. For example, accounting firm Johnson Lambert was able to reduce audit hours by 50% with an AI assistant.

AI productivity in healthcare and life sciences

AI has had an impact on healthcare and life sciences in a variety of ways. The fact that AI models can synthesize huge volumes of data has led to an exponential increase in the speed of drug discovery. For example, AI has been used to predict protein structures in a way that would likely have been impossible before.

How does AI improve efficiency in healthcare outside of drug discovery? Other uses of AI have included apps that can act as diagnostic tools. Patient management systems have also become AI-enabled, leading to more efficient operation of healthcare facilities.

Even staff scheduling—an ever-present problem for healthcare providers—has been simplified by deploying AI as a tool for analyzing data related to staff shortages and peak time needs.

AI productivity in the public sector

The public sector has made some effective use of AI tools to increase productivity, too. Firstly at an administrative level, by rolling out AI assistants to deal with citizen inquiries, or to deliver critical information in a timely manner, for example.

Perhaps the most impactful use of AI in the public sector from a human viewpoint, though, is the role it can play during disaster response scenarios. Predictive analytics, for example, have been used to forecast resource needs, enabling governments to plan and allocate resources more effectively.

Elsewhere, natural language processing (NLP) tools have also been used by the public sector for tasks such as text classification, sentiment analysis, document processing, and content translation as well, helping to improve communications and accessibility for the public, and efficiency for staff. AI’s versatility is evident in its applications as AI productivity apps in public services, which contribute to a measurable increase in productivity.

AI productivity in energy and utilities

Energy and utility providers have found a number of ways that AI can be used to improve productivity as well as the quality of the services they offer.

Predictive analytics can be used to analyze historical maintenance data, highlighting possible equipment problems and helping to predict breakdowns. This reduces service outages and lowers the cost of maintenance. It has been forecast that predictive AI leveraged in this way could result in a 20% saving on operational costs.

Electricity companies have used AI to analyze usage trends and predict when periods of increased demand will occur, enabling them to manage the grid to meet these needs. Field technicians have been given access to AI models trained on technical manuals and other expert knowledge to increase their efficiency by making the knowledge base more mobile.

These are among the best AI tools for business productivity in resource-intensive industries. By adopting AI tools for productivity, organizations can ensure greater operational efficiency and sustainability.

AI productivity in manufacturing

How does AI increase productivity in manufacturing? A 2023 survey by the National Association of Manufacturers (NAM) found that 72% of manufacturers reported reduced costs and improved operational efficiency after deploying AI technology.

AI has been used to make quality control more efficient and reliable. Using computer vision technology, AI can detect defects in products with greater accuracy. When this is combined with AI-controlled robotic manufacturing lines, the benefits are compounded.

Supply chain optimization and logistics is another area where manufacturing companies have successfully employed AI to reap significant rewards—principally by using AI analytics to optimize resource supply and production schedules to meet demand more efficiently. This demonstrates the transformative impact of artificial intelligence productivity tools in industrial operations.

AI productivity in retail and ecommerce

Many of the AI tools used by retailers revolve around the customer journey and improving the customer experience. This includes delivering personalized product recommendations based on customer preferences, and providing AI-powered chatbots that can answer questions about products or provide support.

Additionally, the impact of GenAI on productivity in fields such as email marketing has made the process of engaging and nurturing prospects more efficient.

Retailers can use AI algorithms to analyze purchase behaviors, helping to ensure the right products are available at the right time, and helping to solve stock shortages and supply issues. NLP, semantic analysis, and text analytics can be used to uncover problems with customer satisfaction, too.

These examples of AI productivity tools demonstrate their impact on customer satisfaction and business outcomes, reinforcing how to use AI to be more productive in customer-centric industries.

Things to consider when adopting enterprise AI for productivity

Deploying AI in enterprises to improve productivity comes with challenges. Some of these are industry-specific and others more general in nature, but understanding them at the outset is key to mitigating their impact.

Data quality and volume
- Challenge: Training and fine-tuning AI models requires large volumes of accurate data, which may not be available for every use case.
- Solution: Use a pre-trained model and fine-tune it to your distinct needs. Consider using synthetic data for training if appropriate, and invest in sanitizing data.
Lack of in-house skills
- Challenge: The enterprise may lack the in-house expertise to deploy AI effectively.
- Solution: Invest in providing adequate training for employees, or work with external AI specialists.
Ethical considerations
- Challenge: The reputation of the business is at risk if AI use breaches data privacy or generated AI responses exhibit measurable bias.
- Solution: Ensure compliance with data privacy and AI use regulations by establishing an ethical framework for monitoring AI use within the business.
High deployment cost
- Challenge: Rolling out AI solutions can be expensive, in both cost and the level of resources consumed.
- Solution: Analyze and invest in the best AI tools for productivity to maximize returns, based on actual use cases. Tackle the most impactful AI projects first.
Lack of employee buy-in
- Challenge: Employees may see AI deployment as putting their jobs at risk, or drastically changing workflows and processes.
- Solution: Be sure to be transparent, and involve staff in the process of integrating AI into the workplace, carefully highlighting the benefits to them as well as to the wider business.

Building an enterprise with AI-powered productivity

There are a number of best practices when deploying AI to help boost the growth of enterprise productivity. Working within these best practices can help mitigate the challenges that could be faced by enterprise AI deployments.

Establish a clear AI strategy

The key to streamlined implementation of AI within the enterprise is defining a well thought out strategy. It should highlight key objectives that will uncover the areas where artificial intelligence productivity tools can deliver the most value. Ideally, you want to identify a use case that will deliver transformative value for your organization.

Foster collaboration between teams

Key business units—such as operations, IT, and any other department that will be directly impacted by AI deployment—should be encouraged to collaborate. Making the effort to build the right team can lead to a better understanding and acceptance of AI tools for productivity, which may be viewed as a disruptive technology by some stakeholders.

Invest in scalable infrastructure

To ensure that AI productivity solutions can expand as business productivity grows, enterprises should look to deploy the apps into scalable infrastructure—either in-house or through cloud computing. This helps to ensure the business can adapt to evolving needs and grow alongside the technology.

Consider private and secure AI

Consider and address AI security concerns from the outset. Ensure AI systems are secure by keeping data reliable, accurate, and private at all times. Private deployment offers more peace of mind from data security risks.

Monitor and refine AI initiatives

AI systems need to be evaluated regularly to ensure they’re working as intended. To help artificial intelligence increase productivity, the business must work to confirm that AI use continues to comply with legal and ethical requirements.

Choosing the best AI productivity tools for your business

Looking for a cheat sheet to guide the successful deployment of AI to improve productivity? Here are some key considerations for choosing AI productivity tools:

Scalability: Look for solutions that can grow and expand with the needs of the organization.
Ease of integration: Highlight any tools that will integrate simply within end-to-end business workflows.
Vendor reputation: Choose a vendor that has a proven track record within the industry in question.
Customization and fine-tuning: Make sure that any model chosen can be customized or fine-tuned to align with the use case.
Cost-effectiveness: Assess the total cost of ownership, including implementation and maintenance. Evaluate AI productivity tools to maximize impact and ensure an increase in productivity.

Improve the workflows of your business with AI

AI is not a futuristic, untested technology. It is, in fact, a very real alternative to transform the way that companies operate, from customer-facing sales aids to complex manufacturing control systems and advanced planning tools. However, the successful deployment of AI requires careful planning and execution.

Businesses can streamline operations, drive growth, and stay ahead in an increasingly competitive landscape with effective AI solutions. Embracing the best AI tools for productivity and deploying AI productivity apps can enable organizations to thrive in an AI-driven future.

Building enterprise AI agents

A comprehensive blueprint for developing scalable AI agents in regulated industries.

Download the eBook

AI productivity: How AI is transforming the workplace