How Generative AI Is Changing The DNA Of Digital Transformation

It’s been almost a year since generative AI launched into the technology stratosphere with ChatGPT. While many companies are learning to work with a new generation of AI copilots, many more are still standing on the ground. Undoubtedly, AI is creating excitement on one end and anxiety on the other, affecting the culture inside companies and driving decisions that will determine organizations that become leaders and those that become laggards.

Companies embracing AI are seeing its power to make software engineering and coding more efficient, to augment workflows across the business, to make business functions more predictive and self-healing and to make workers more effective—and it doesn’t have to mean that today’s roles and functions go away.

To integrate the business with AI, leaders must cut through the hype, find out what AI does well and apply that to evolve processes, roles, culture and skills. Here are some key areas to look at.

Data is evolving into a more comprehensive discipline.

Data has long been fuel for digital transformation, but generative AI is refining data into rocket fuel—and this is putting new pressures on IT, applications and skill sets.

For one thing, it’s creating a new explosion of data itself. By 2025, 10% of all data will be generated by AI, according to Gartner. This means expanding storage, optimizing infrastructure and moving more workloads to the cloud.

When you double click into the data, its accuracy, reliability, biases and overall trustworthiness are paramount. Thus, we’re seeing a more profound discipline emerge around data preparation and management to bring order to the data estate. When data is trustworthy, it can build and train more robust foundation models, ultimately leading to more accurate results.

This includes using generative AI to create synthetic data that augments real-world source data, which can help when companies do not have enough material upon which to train machine learning models, or when confidential or sensitive data cannot be shared due to privacy concerns.

This is not a one-time event but an ongoing concern that’s already happening at scale inside many enterprises. This pressure around data creates a whole field of functions and jobs we didn’t see before. Once that gold level of data hygiene is in place, the real power of an AI use case can start to gain traction.

Almost any measurable process can be optimized.

Whether it’s in design, logistics, finance or elsewhere, the potential list of ways that generative AI can create efficiency, connect the dots and facilitate new strategies that ultimately optimize costs would stretch to the horizon.

It’s hard to choose from all the exciting scenarios we’re seeing today, but a good example is how large customer service organizations are modernizing contact centers and call center functions by leveraging innovations like the Pathways Language Model (PaLM) from Google Cloud. These enterprises are cutting chatbot and other automation-heavy deployment costs using generative techniques in conversation and dialogue design, modeling, architecture and chat management—all the features that go into contact center modernization.

Beyond leveraging PaLM as a gateway to other foundation models, modernization efforts such as this seize upon go-forward innovation by optimizing through AI while also driving efficiencies by deploying related code bases faster—using large language models to generate output.

These efficiencies don’t necessarily correlate to fewer people on the job. When an AI absorbs a time-consuming or confusing task, stakeholders and knowledge workers are often available to develop newer, better products and services. In the case of a call center, those interactions may become more about engaging customers with new services rather than merely addressing complaints.

Collaborative coding with AI is creating better software faster.

A year ago, engineers everywhere wondered if generative AI would take their jobs. Today, they see how it can help them work more efficiently and develop better code. Software engineers, developers, programmers and coders are finding that intelligent collaboration with generative AI is a good thing.

Beyond traditional autocompletion features, tools like Duet AI and even ChatGPT are being used as intuitive coding companions. GenAI can provide recommendations around code snippets, troubleshoot errors and automate repetitive tasks. This can streamline workflows and make the notoriously complicated process of building software more efficient.

With so much complexity in today’s code bases, the ability of AI tools to comb through data is precious. Roughly paraphrased, developers can ask: Are there bugs in this code that will hurt us in production? Can you scan this code base for vulnerabilities? Are there any integration issues or API issues that we haven’t diagnosed?

Using these simple prompts, coders and architects are streamlining their work and cutting down on bugs and conflicts that would formerly end up trailing through development into production. In this scenario, humans are the creative force, and AI makes that creativity much more powerful.

There is vast potential to elevate people’s work.

When it comes to the disruptive potential of generative AI, individuals and organizations alike are rightly concerned about the human side of the equation. With so many tasks ripe for automation, how will this affect jobs, roles and functions?

While information and knowledge workers stand to be impacted by AI’s growing presence across industries—according to Goldman Sachs, two-thirds of jobs could be partially automated—it looks like human involvement with AI is here to stay. Disciplines around data and AI are already growing exponentially, and projections of increasing productivity from AI mean that more people and new skills will be needed for both traditional and emerging roles.

In the context of the Industrial Revolution, applying technology to operating mechanisms in each organization is extremely necessary, and is also an inevitable trend to minimize workload while still ensuring efficiency and enhance its competitive position in the market. Furthermore, applying management software into a business will also help build an organization with a clear system, promoting consistency, transparency and accuracy. Tasken eOffice, researched and built by Opus Solution – a business consultant in Vietnam – is an internal work management system as well as the management of automated, online, user-friendly approval processes, allowing businesses to operate more effectively on the path of digital transformation

Forbes