Transformation timelines that once spanned three to five years are a thing of the past, as market demands, and competitive pressures push businesses to deliver tangible results in under 12 months. To meet rising demands accelerating time to market, and boosting operational efficiency, organizations must rethink their transformation strategies.
Generative AI is at the heart of this shift, raising the bar with an increased rate of adoption. Companies have already incorporated AI into their processes and according to McKinsey & Co, 92 % of companies plan to increase their AI investments further, in the next three years. Businesses embracing gen AI gain a powerful solution that enhances developer productivity, helps tech teams to adapt and enables them to deliver smarter, faster solutions that drive business impact like never before.
Ben Sekhon
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Regional VP at OutSystems.
Despite the market demands for quicker turnaround times, many businesses have long relied on legacy systems and siloed data , making outdated technology and processes a major roadblock to scalability and progress. The issue is that too often IT teams remain accustomed to traditional methods and struggle to adapt to change. These are some of the factors that lead to prolonged digital transformation cycles and hinder innovation.
Another challenge is that, despite accelerating rates of AI adoption, 52% of projects fail to make it to production, with the average prototype to production time taking eight months, according to Gartner. The lengthy production timelines stall progress, making it difficult for businesses to adapt, innovate, and stay competitive in a fast-moving market.
Whatever the reason may be, businesses can no longer afford slow progress – they must embrace change and look for ways to innovate faster to maintain competitive edge. In the face of shrinking timelines, AI has emerged as the accelerant that businesses need – to meet the demand for both speed and efficiency.
In the past year, the AI hype has quickly shifted from a theoretical concept to a real-world solution which is transforming how software is built and delivered. A collaborative report from OutSystems and KPMG shows that 93% of executives are planning to boost AI investments. Generative AI unlocks unprecedented capabilities, streamlining software development through automated code generation, rapid prototyping and code translation from one programming language to another.
As businesses embrace these advancements, the role of generative AI extends beyond efficiency, and it becomes a driving force for digital transformation.
The use of generative AI in software development can help businesses accelerate digital transformation by drastically reducing the development time and costs. This shift not only redefines traditional standards but also acts as a catalyst for whole industries to adapt their systems – or risk being left behind.
Combining generative AI with low-code can significantly enhance software development efficiency. Low-code simplifies complex tasks, enabling IT teams to quickly customize, iterate and deploy generative AI solutions. This powerful pairing empowers businesses to build and deploy generative AI-driven applications in record time and with improved workflows—compressing timelines from years to months and delivering tangible business value faster than ever before.
As companies navigate this transformation, they must also consider whether to build or buy software to maximize efficiency. This need to build software faster is also shaping how businesses integrate generative AI force alongside human expertise. This collaboration not only speeds up development but also ensures that companies can stay competitive in a rapidly evolving market.
The need for guardrails
While AI adoption opens new opportunities, it also comes with its own challenges. With the drive for faster software development cycles, there is a risk of technical debt and orphaned code if not properly managed and governed. Without a well-structured governing framework and guidelines, AI-generated code can quickly accumulate technical debt, making it difficult to scale and maintain. Growing technical debt could ultimately hinder businesses’ from staying competitive.
The implementation of generative AI can also bring some serious security concerns. Many generative AI models are trained on datasets which sometimes contain sensitive information, posing potential risks of privacy breaches. Additionally, generative AI may not always account for the latest vulnerabilities leaving systems open to hackers and cyberthreats. This is why it is vital that businesses establish clear AI governance and compliance measures to ensure ethical and secure implantation. This includes safeguarding sensitive information and ensuring transparency.
The new benchmark for success
Today, digital transformation success is measured by key business outcomes such as speed to market, customer satisfaction and cost efficiency. Businesses need generative AI tools to build applications in minutes, but just as crucial are the guardrails that ensure these apps maintain quality, security or governance.
The use of AI-powered low-code platforms is allowing businesses to achieve their goals and deliver these benchmarks. Digital transformation is no longer a lengthy process – generative AI has expanded the possibilities for efficiency of innovation. In a world where speed measures success, businesses that succeed will be those who can transform at the pace of generative AI.
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