How agentic AI will impact software engineering

Agents, assemble! 

AI-powered coding agents are now real and usable. Indeed, coding agents are all around us, touching on every aspect of the software development life cycle, and recently InfoWorld outlined 12 of the best.  

This raised in the minds of our readers the bigger question, which they rushed to ask Smart Answers: how will this impact my career? 

Smart Answers may be a generative AI tool, but it is not in the business of replacing humans. Using our decades of human reporting it predicts that by 2027, agentic AI tools will significantly impact software engineering careers, necessitating an evolution in workflows and skill sets.  

It further forecasts that this shift means that 80% of software engineers will need to reskill for new roles as generative AI assumes more programming functions. 

Find out: How will agentic AI tools impact my software engineering career by 2027?  

CoPilot or analyst? 

Adoption of AI-driven features in productivity software can be spotty. Functionality appears, and users may not know how to take advantage (or even that it is there). So recently Computerworld outlined nine things to try with Copilot in OneNote, stating that Microsoft’s AI assistant can speed up your workflow and perform tasks you never expected in a note-taking app.  

That’s all well and good, but can Copilot analyze data? That was the question readers asked Smart Answers – our very own AI assistant.  

The answer is that OneNote is somewhat useful in this respect, but its capabilities are spotty. Smart Answers says that Microsoft Copilot in Excel offers various capabilities for data analysis, including chart creation, formula generation, and insight extraction, aiming to simplify complex tasks for users. While it has shown promise in certain areas, its effectiveness can vary, with some users experiencing limitations or inconsistencies.  

Getting there but must do better. 

Find out: How effective is Microsoft Copilot for data analysis in Excel currently? 

Data platforms’ AI security challenges 

A big story with readers of CIO.com has been our analysis of the battle between Snowflake and Databricks for the heart of enterprise AI. We reported that the two companies’ data science platforms have become core components for many CIOs who are focused on leveraging organizational data to drive AI deployments, as the market for similar tools rapidly expands and evolves. 

Readers wanted to understand better the implications for security when data platforms are using AI. Smart Answers agrees there is a challenge here, stating that data platforms face several security challenges when handling AI workloads. Generation of insecure code by AI-augmented development tools is one major concern. And AI models trained on sensitive data risk inadvertently exposing that information within the generated code.  

There’s more, but you need to ask Smart Answers for that. 

Find out: What security challenges do data platforms face for AI workloads? 

About Smart Answers 

Smart Answers is an AI-based chatbot tool designed to help you discover content, answer questions, and go deep on the topics that matter to you. Each week we send you the three most popular questions asked by our readers, and the answers Smart Answers provides. 

Developed in partnership with Miso.ai, Smart Answers draws only on editorial content from our network of trusted media brands—CIO, Computerworld, CSO, InfoWorld, and Network World—and was trained on questions that a savvy enterprise IT audience would ask. The result is a fast, efficient way for you to get more value from our content.