{"id":13765,"date":"2024-02-12T09:24:23","date_gmt":"2024-02-12T09:24:23","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=13765"},"modified":"2024-12-09T18:14:15","modified_gmt":"2024-12-09T18:14:15","slug":"helping-ease-the-path-to-ai-adoption-in-the-enterprise","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/ai\/helping-ease-the-path-to-ai-adoption-in-the-enterprise\/","title":{"rendered":"Helping ease the path to AI adoption in the enterprise"},"content":{"rendered":"

What do you do when decision-makers think that AI gets more wrong than it does right?<\/p>\n

I\u2019m currently working with a customer in the education sector, replacing an internally hosted system with a cloud-based platform.<\/p>\n

Creating a new platform is a big job, with one in-house engineer, two Equal Experts engineers and two third-party engineers all working on the new platform. As you\u2019d expect, there\u2019s a fair amount of code.<\/p>\n

The customer uses a specific, free IDE called Visual Studio Code, with React as a front-end development framework. When we create new pages, the platform uses Gatsby and pulls in content from a headless CMS, Kontent.ai. Then, we can type code in VS Code to generate the page.<\/p>\n

We\u2019ve been using VS Code and Copilot<\/a> to speed up the creation of new code within the IDE. Copilot uses AI to make auto-complete suggestions as the developer is writing code. It takes knowledge from billions of lines of code written by thousands of developers to make an informed prediction about what you\u2019re writing. As you type, Copilot offers a suggestion for the next lines of code and if it\u2019s correct – you just press the tab, and it will implement that code.<\/p>\n

The huge advantage is that it makes the creation of large quantities of code much faster. In general, I can create in a couple of hours what used to take a full day. What Copilot gives you is working code that doesn\u2019t have any typos, as you\u2019d commonly see in human-generated code.<\/p>\n

However, we met with some resistance from the customer to the idea of using Copilot, particularly around accuracy. The client\u2019s team had heard more about what AI gets wrong than what it gets correct, and we faced a lot of skepticism.<\/p>\n

Our approach with this engagement was to do the following.<\/p>\n

Sharing research on Copilot\u2019s impact on efficiency<\/h2>\n

We shared research<\/a> with the customer that has been carried out by GitHub, looking at how Copilot affects the efficiency and quality of code development.<\/p>\n

In Github\u2019s study, one group of developers created JavaScript code traditionally, while the other group used AI. The results were compared for accuracy and correctness, and revealed that the group writing code manually took on average 161 minutes to complete the task, compared to 71 minutes when using Copilot. In other words, it took 130% longer to complete the task when not using Copilot.<\/p>\n

Sharing personal experiences of Copilot<\/h2>\n

Faced with skepticism about AI, it\u2019s important to help clients understand that while tools like Copilot might sometimes suggest something that\u2019s wrong, 95% of the time, it\u2019s going to suggest exactly what you would have typed.<\/p>\n

Also, you\u2019re not required to go along with Copilot if it makes a suggestion that isn\u2019t correct. You can ignore it, or provide more context to get a better suggestion.<\/p>\n

Faced with skepticism about AI, it\u2019s important to help clients understand that while tools like Copilot might sometimes suggest something that\u2019s wrong, 95% of the time, it\u2019s going to suggest exactly what you would have typed.<\/p>\n

Github says that Copilot makes developers 55% more effective but I think that\u2019s a conservative estimate. I\u2019m an architect and I don\u2019t write code day in, day out, but I\u2019ve found it to be invaluable. I can get through a day\u2019s work in probably a couple of hours. It\u2019s a highly effective code completion tool and it\u2019s context aware, and knows what you\u2019ve written before, like the structure of the project and the code base.<\/p>\n

Starting early in the procurement process<\/h2>\n

One of the big challenges we had to overcome with Copilot was our customer\u2019s procurement process. Although Copilot only costs $20 per month, and there are only six developers, it took us 120 days to go through the formal procurement process for us to be able to use the tool.<\/p>\n

We found the procurement process almost as big a challenge as convincing the client to use the tool in the first place.<\/p>\n

Be an evangelist<\/h2>\n

In many cases people are still learning about how AI can be helpful in the development process, so sharing examples of how it relates to the project is important.<\/p>\n

I\u2019ve found that Copilot is really good when you\u2019re writing code that has a branch in it – that sounds against best practice but it picks up those branches and so if you start writing code for something on a Monday, it picks up that you might want the same for Tuesday and Wednesday and so on.<\/p>\n

Recently there\u2019s been a feature released where you can chat with the code and that\u2019s helpful if you are working with code that you\u2019re not familiar with; you can ask it to explain what it does, and you can even ask it to read the code and tell you if you could potentially do it better.<\/p>\n

Finally, I\u2019ve found that Copilot really excels when you\u2019re picking up a new language. Most of the time you\u2019re working in a language you understand, but Copilot can explain itself which is really helpful when you’re learning new languages.<\/p>\n","protected":false},"excerpt":{"rendered":"

What do you do when decision-makers think that AI gets more wrong than it does right? I\u2019m currently working with a customer in the education sector, replacing an internally hosted system with a cloud-based platform. Creating a new platform is a big job, with one in-house engineer, two Equal Experts engineers and two third-party engineers […]<\/p>\n","protected":false},"author":244,"featured_media":20374,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[412,179,5,3],"tags":[99,741,415],"location":[397],"class_list":["post-13765","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-data","category-our-thinking","category-tech-focus","tag-ai","tag-ai-adoption","tag-copilot"],"acf":[],"yoast_head":"\nEasing AI Adoption in Enterprise: Key Strategies<\/title>\n<meta name=\"description\" content=\"What do you do when decision-makers think that AI gets more wrong than it does right?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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