The Impact of Generative AI on Software Development
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What is Generative AI?
Over the last year in 2023, the greatest tech boom of our time has been the advent of artificial intelligence or AI. Most recently, a new technology is creating a new buzz: generative AI. Generative AI is a type of artificial intelligence that refers to models, training, and algorithms to output brand new content procedurally. Some examples of what generative AI can produce are:
●????? Text, Code
●????? Video, Audio
●????? Images, 3D renders, etc.
Over the last year many tools have been created to apply generative AI. They are ChatGPT (text generator), DALL-E (image generator), GitHub Copilot (code generator), and many others. With the hype, KPMG launched a survey in March 2023 to understand how enterprises and companies are adopting generative AI technologies. The key results of the survey indicate that there are great opportunities and benefits for generative AI to developers and users as well as some risks and challenges.
Top 4 Opportunities and Benefits in Generative AI
Based on key findings from the KPMG generative AI survey, the top 4 biggest opportunities and benefits of generative AI are personalization, productivity, quality, and competitive edge.
1. A more personalized experience
From the survey, 77% of respondents expect generative AI to have the biggest impact on their businesses out of all emergent technologies. Several applications of generative AI, such as analytics and report generation are great tools that can be used to personalize developer experience and business processes.
2. Increased productivity
From the survey, 73% believe generative AI will increase workforce productivity. AI has been revolutionizing the software development industry by reducing time and increasing output and productivity. One application of increase productivity from generative AI is automation. Tasks like generating tests, and documentation allow developers to focus on more complex tasks.
3. Improved quality
According to the survey, 74% will implement their first generative AI solution within the next two years. This is due to the potential generative AI has in business and software development. Like the previous productivity benefit, generative AI allows for quality assurance and measures to mitigate risks and errors.
4. Boosted competitive edge:
64% of respondents in the KPMG survey believe generative AI will help their business gain a competitive advantage over competitors. It can help reduce the barriers to entry for new businesses and developers with developing complex codebases. It also allows smaller companies to catch up and achieve goals that they thought were previously impossible or unfeasible.
Top 4 Risks and Challenges in Generative AI
There are two survey results that highlight the challenges and risks that generative AI would cause:
●????? 92% or respondents think generative AI implementation introduces moderate to high risk concerns and 47% are still at the initial stages of evaluating risk and risk-mitigating strategies for generative AI.
The study further elaborates that the top barriers to implementation are a lack of skilled talent, the cost or lack of investment and a lack of clear business case. The top risk focus areas are cybersecurity and data privacy. From these findings, the top 4 biggest risks and concerns in generative AI are job displacement, lack of skill and creativity, growing concerns about transparency, ethics, and security risks.
a) Job displacement
The emergence of generative AI has the potential to revolutionize the IT industry. However, this has a negative impact. With a new focus on automation, productivity, and output, this new shift has brought on concerns about job displacement and career uncertainty. This is particularly highlighted in the multitude of tech industry layoffs in 2023 coinciding with the introduction of generative AI.
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b) Lack of skill and creativity
On top of job displacement pushing tech talent out into the job market, generative AI has had a negative impact on creativity and skill. The benefits of generative AI in day-to-day tasks and objectives can make developers reliant on the technologies. This could potentially decrease development innovation.
c) Transparency and ethical concerns
The opaque nature of generative AI decision-making can present the challenge of ensuring transparency and accountability. Additionally, issues such as data privacy and collection in conjunction with generative AI must be dealt with delicately. Growing concerns of hacking, identity theft and the stealing of intellectual property are among the major concerns of generative AI.
d) Security risks
Generative AI is not perfect. There is still risk in security problems that stem from its use. Since it relies on machine learning algorithms, it may be vulnerable to several kinds of malicious attacks and data manipulation if not properly secured. Companies should be cautious in protecting themselves against potential harm with their own measures and trusted systems.
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Head of Digital Transformation at SumatoSoft | We implement comprehensive projects and deliver high-end web, mobile, and IoT solutions.
5 个月It's fascinating to see how this technology is shaping the future of various industries, despite the challenges it presents.