Rapid advances in the capabilities of generative AI software have caused many engineers to wonder how this technology can help them look more brilliant, improve their productivity and avoid mistakes.

Many software developers have incorporated generative AI concepts into their apps, and engineers are embracing their new AI copilots for a variety of everyday tasks.

Project management

Engineers manage many projects. This is a difficult task, especially for digital transformation projects with their complex integrations. Engineers are often looking for tools to reduce risk and improve performance.

For example, ClickUp, Forecast, Hive, Notion, Project Planner, Project Insight, and Wrike use artificial intelligence to:

• Optimize resource allocations and workload management.

• Calculate project schedules based on task precedence and priorities.

• Prepare project status reports.

• Monitor project progress and recalculate forecasts.

• Calculate task cost estimates and costs at completion.

• Manage project costs and variance.

Business process automation

Large and small organizations use multiple apps to automate various business processes. Expanding and enhancing automation is central to digital transformation initiatives. Generative AI capabilities created an opportunity for many app vendors to upgrade the functionality of their product offerings. Example business processes for which app vendors offer AI-enhanced solutions include:

Rapid advances in the capabilities of generative AI software have caused many engineers to wonder how this technology can help them look more brilliant, improve their productivity and avoid mistakes.

Many software developers have incorporated generative AI concepts into their apps, and engineers are embracing their new AI copilots for a variety of everyday tasks.

• Accounts payable automation

• Content creation

• Credit scoring

• Customer service interactions

• Diagnostic image analysis

• Resume evaluation

• Robotic Process Automation (RPA)

• Routing optimization

• Server log analytics for cyber security defense

• Workflow automation

This list of business processes can help engineers identify an automation opportunity that can improve work quality or reduce cycle times at their organization.

Vendors that offer AI apps for one or more of the business processes listed include Anyword, Automation Anywhere, Datadog, MarketingBlocks, Microsoft, Quadient, Splunk, SS&C Blue Prism, Texta.ai and UiPath.

Research

Engineers search for products, technologies and academic research to solve technical problems, advance designs and explore digital transformation. The software market offers several AI apps to help engineers conduct research and manage the results.

For example, Consensus, ChatPDF, Elicit, QuillBot, Research Rabbit and Semantic Scholar use artificial intelligence to speed up research tasks, increase relevant output, and offer insightful data. These apps provide some combination of the following capabilities:

• Search for relevant articles based on the prompt text.

• Summarize articles in the result set.

• Identify relevant results even if the article does not contain keywords from the prompt phrase.

• Track their research progress.

• Help to structure prompt phrases.

• Receive feedback on the likely importance of their work.

• Collaborate with other engineers.

• Extract text from PDFs.

• Create bibliographies.

Software development

Most engineers spend some time developing informal software for tasks including data analysis for digital transformation, design and troubleshooting equipment performance lapses. Any tool that can increase productivity and reduce mistakes would be helpful.

For example, AI coding assistants like Amazon CodeWhisperer, CodeT5, GitHub Copilot OpenAI Codex, Polycoder and Tabnine can write significant amounts of software with limited input.

All software includes many standard statements, often called boilerplate, for tasks like input/output, error checking and managing user interaction. It’s a hassle to write and maintain. Fortunately, AI coding assistants are particularly good at this work that engineers and others dislike.

Software development requires an intimate knowledge of the syntax of every command in the language. It’s easy to forget the precise order of the arguments for every command. AI coding assistants are meticulous and consistent in understanding this critical detail.

AI coding assistants cannot replace human programmers' creative thinking and problem-solving skills. However, they can help developers by creating code snippets based on specific needs. AI coding assistants can misunderstand prompts just like humans can misunderstand each other. Engineers must still review and test the AI-written software.

Content writing

Many engineers spend more time writing reports and other forms of content than they’d like. Generative AI apps can reduce this time without sacrificing quality.

Google Duet AI, Grammarly Business, Linguix Business, Ludwig, Microsoft 365 and ProWritingAid are examples of AI apps that go beyond what software like ChatGPT and its peers can do. Some of these AI apps combine AI communication assistance with your internal knowledge base and style expectations to reduce report-writing effort while improving the quality of the final product. These tools can also be used for website content, presentations, social media posts and other written communication.

Language translation

While engineers do not translate documents, they do sometimes want to read articles originally written in other languages.

AI translators like Alexa Translations, Bing Microsoft Translation, DeepL, Google Translate and Mirai Translate support many languages and offer excellent results.

When the need for accuracy is high and the topic is technical or complex, many translation services combine machine translations with human review and editing. That reality reminds us that AI translators aren’t perfect.

Customer interaction

Adding more intelligence to automated customer interaction depends on digital transformation. Many customer interaction app vendors incorporate generative AI concepts into their call center apps. The goal of these AI apps is to:

• Improve the quality of responses customers receive.

• Reduce the elapsed time required to answer customer questions.

• Minimize the number of customer interactions with more expensive call center staff.

For example, Conversica, Genesys Cloud CX, Hyro, IBM Watson Assistant, Kore.AI, Leena AI and Open AI offer enterprise conversational AI platforms that enable engineers to configure intelligent AI-enhanced text and voice apps. These AI apps can give customers fast, consistent and usually accurate answers across multiple messaging platforms and devices. Using machine learning (ML) and large language models (LLM), AI apps learn from customer conversations to improve their ability to resolve issues the first time while:

• Improving customer satisfaction.

• Reducing wait times.

• Avoiding tedious website and FAQ searches.

• Eliminating interactions with simplistic chatbots.

• Controlling call center operating costs.

More sophisticated AI apps can perform the following tasks that are beyond the ability of chatbots:

• Understand more complex questions.

• Provide more nuanced or conditional answers.

• Recognize when to use information from a knowledge base.

• Ask for clarification from the customer.

• Hand-off customers to call center staff for more assistance if necessary.

Limitations of generative AI

Engineers should remember that generative AI apps have limitations like most tools. These limitations include:

• Hallucinations. Sometimes, generative AI misinterprets the training data, misunderstands the context, misleads due to misinformation, or makes up inaccurate information.

• Language errors. Despite its excellent help, generative AI occasionally provides or proposes grammatically incorrect or awkward text.

• Limited to English. Many generative AI apps are effectively limited to English because that’s the largest market and the training data’s dominant language. These apps produce low-confidence results in other languages due to limited training data.

• Limited creativity. Despite creating new data based on existing patterns, generative AI is limited in creativity and originality. That’s the domain of engineers.

• Bias. Sometimes, generative AI introduces bias in its results due to bias in the training data and model limitations.

• Limited application. Where only limited training data is available, or the data is highly complex, generative AI applications will not be practical.

The rapid development of many function-specific apps that include AI capabilities can help engineers reduce drudgery, avoid mistakes and improve their performance.

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