The Shift to AI-Native Operations: Generative AI's Growing Importance

Companies are transforming operational workflows from automation-driven to AI-driven. Businesses are rethinking processes to incorporate AI-assisted actions and judgments. The emergent category of AI-native operations has appeared.

Generative AI plays an important role in this area by facilitating the reimagining of processes. The widespread adoption of such approaches will enable organizations to operate more productively and effectively in novel ways.

What Are AI-Native Operations?

By definition, AI-native operations concern an organization's reengineering of business processes "to take full advantage of the power of artificial intelligence." An essential difference from simply automating specified actions is that AI-native operations concern utilizing AI technologies to make operations more productive.

While automation is usually limited to strictly rule-based activities, AI-native operations allow a more fluid set of instructions based on a combination of contextual data and situational cues. Moreover, AI-native operations now also utilize generative AI methods to generate documents, perform analysis, and undertake other functions.

For instance, a customer service request can be summarized by an AI-native operations workflow, and a response drafted – including suggestions as to the next action. If there is a need for further action or consultation with a specialist, the request can be escalated to a human agent. Businesses reengineering their processes often work with an experienced AI development services provider to design and implement AI-native workflows.

Why Generative AI Is Rising in Priority

Generative AI methods, which essentially enable AI systems to produce content and complete tasks in response to natural language prompts, enable more fluid and less rigid operational workflows compared to automation. Generative AI methods are not limited to merely rule-based actions because they can work with data in advanced ways, including processing, analyzing, and presenting it in novel formats (where supported by the tools).

The trends that have increased the relevance of generative AI in particular and AI-native operations in general include:

• The abundance of data: AI techniques can study huge volumes of data to generate insights

• Speed desire: AI techniques free employees from rule-based and repetitive manual work

• Changing customer demands: AI techniques allow for greater agility, personalization, and faster responses

• Desire to reengineer the business process: AI techniques may be incorporated into existing IT systems to automate them and add intelligence to them

Nonetheless, whether AI-nativized workflows will improve business processes always depends on the quality of the input data, its availability, and the relevance of the task at hand. There is a tendency in many organizations to collaborate with providers of Generative AI solutions.

Which Business Functions Are Being Transformed?

There is a number of distinct departments within an organization where the introduction of AI-native operations is currently being considered and tested. Most often, businesses can reap the benefits of generative AI in terms of increased flexibility and speed while reducing the input of humans in repetitive and time-consuming activities.

Operations and Customer Service

AI-powered chatbots can assist customer service representatives, for instance, in finding relevant information, summarizing a conversation history, and determining the most suitable category or response. Such chatbots, if linked to an appropriate knowledge base, can provide much more relevant information than regular customer service bots. In practice, however, human agents are much better at handling difficult situations and interactions.

The Marketing Department

It is increasingly common for businesses to utilize generative AI methods to draft blog posts or product descriptions. Marketing teams often utilize such AI extensively, sometimes even at the stage of conceiving a new marketing campaign, discussing the nuances of targeting different audiences, or analyzing similar marketing concepts. Overall, while such AI greatly facilitates the tasks of marketers, there is still a strong need for human review and oversight.

Development and Testing in the IT Department

Developers and engineers can benefit from utilizing generative AI methods in a number of ways, including explaining complex chunks of code, suggesting some unit tests, system documentation, or identifying some obvious errors. This can free up developers' time, especially in terms of less time-consuming and repetitive routine activities that do not involve high-value tasks. As usual, AI-generated results still have to go through extensive human review before being actually implemented.

The Financial and Operations Departments

Generative AI methods can be useful for preparing financial reports (including analyzing large swaths of financial data), analyzing documents, and summarizing reports in the financial and operations departments. As long as appropriate data analysis tools are utilized in concert with generative AI, it becomes possible for an employee to detect anomalies and delve deeper into detail.

In any case, the analytical and financial calculations, as well as their subsequent application, have to be reviewed by appropriate experts to ensure that conclusions are valid and the right actions are taken.

The Emerging Role of AI Agents

Currently, more and more companies are beginning to utilize AI agents to facilitate AI-native operations. In essence, while a conversational model such as a chatbot works with a user back-and-forth, AI agents can operate with certain permissions and access to tools and data to fulfill a user's request.

An operations agent, for instance, could be entrusted with following a customer's request, finding relevant information, suggesting the next action, and escalating that action to another party for approval. While such AI agents can be very helpful, businesses must enact appropriate policies for their use. Prior to launching AI agents, it is crucial to identify who is responsible for their actions, how they should be governed, and what control mechanisms should be in place. The higher the automation levels, the more stringent the controls need to be. Companies deploying agents at scale frequently rely on a trusted Artificial intelligence development services partner to establish governance and control mechanisms.

Challenges That Businesses Face

Despite the clear advantages of introducing AI-assisted processes at the core of operations, most organizations hesitate due to some of the key challenges that generative AI currently presents. In an unfortunate number of scenarios, businesses can fall victim to simply chasing the newest trends without a coherent understanding of how the technology actually works.

Among the main pitfalls that businesses have to avoid when introducing AI-native operations are:

• Problems with hallucination or erroneous AI output

• The risk of leaks of sensitive information due to the nature of training data or prompts

• Challenges with integrating with legacy apps

• Issues of responsibility for decisions

• Financial costs associated with adopting these methods

The most crucial way to avoid these pitfalls is to ensure that the organization has developed and enacted appropriate policies with regard to the use of generative AI.

How to Build an AI-Native Operating Model

Overall, an organization does not need to reengineer its entire set of operational processes at once. Rather, there is a sensible approach that enables businesses to test out the value of such reengineering and identify areas where AI-native operations are actually beneficial.

Essentially, to implement AI-native operations, a business needs to first find a process that is suited to addressing a tangible problem through reimagined operations. The business has to pick a test case that will enable it to see if the changes are actually useful. Then, the company should set clear performance benchmarks. For instance, a customer service representative who utilizes a chatbot assistant should be able to handle more tickets per day within the same timeframe, or reduce the error rate.

The company then needs to ensure that there are appropriate data sources and that generative AI tools can be integrated into the selected processes and the existing technology stack. Finally, but no less crucially, the company has to train its staff to handle the results produced by generative AI tools, including inaccuracies, errors, and unexpected results. Moreover, continuous analysis and monitoring of the newly implemented processes are needed since both the model and the business environment are subject to change. Some organizations also work with an AI software development services provider to build the applications and interfaces through which employees interact with these tools.