Recent AI-based innovations such as ChatGPT have caught a lot of attention not just from individual users and developers, but also from several enterprises. Microsoft is already embedding several OpenAI tools in its workforce productivity suite, while knowledge-based firms in legal, research, content writing, and many other fields are revisiting their future personnel requirements with the advent of technologies that can automate mundane tasks.

Recently, many of our clients have asked us to train their leadership on the use of ChatGPT to visualise the most relevant use cases in their fields and contexts. One of the key questions prevalent in these sessions was if ChatGPT is their silver bullet, and if it can solve their complex processes end to end, or if it is just one piece of the puzzle.

Following this, we have listed down five AI trends (and risks in the end) that will give you a more comprehensive picture of this seminal technology as its use cases grow.

#1 – The democratisation of AI and automation

AI is no longer a tool only for data scientists.

Advancements in and increasing access to “low-code” and “no-code” platforms with intuitive interfaces, pre-built algorithms, and high-performance cloud computing platforms have completely revolutionised companies’ digital transformation journeys by expanding automation use cases across the value chain.

Several ChatGPT and OpenAI plug-ins will continue to appear in tools for sales teams, customer care agents, and field force, among others. Service providers like Zoho Creator and Appian already provide simple plug-and-play tools that help businesses without expertise in AI or data science to automate analytics or business management processes. 

#2 – Uplift in automation use cases in manufacturing and supply chain

Manufacturing and supply chain industries have not typically been at the forefront of AI implementations due to factors including high capital expenditure for use cases and fear of disruption of production or supply in the onboarding phase. With expanding ease of access and growing awareness of benefits in business suites, we can expect deeper AI and automation applications, particularly in the back end of operations.

OPTANO, a provider of AI-powered operations solutions, for instance, helps manufacturing and procurement companies with operations optimisation in end-to-end supply chain projects and large-scale operations transformations. The company recently streamlined BMW Group’s network planning and optimisation system by integrating processes for data preparation, planning, and data transmission.

The coming years will see automation solutions offer much-needed upgrades in legacy operations, planning, and optimisation systems.

#3 – Increasing focus on hyperautomation in the offering

Hyperautomation is the disciplined approach of rapidly identifying, optimising, and automating as many legacy business or IT processes as possible.

In the last two to three years, the Asia-Pacific region has seen enterprises marrying several point solutions including AI, robotic process automation (RPA), and machine learning to automate simple and complex business processes to solve friction points in operations.

Platforms like ServiceNow offer enterprise service management through low-code automation solutions to digitise and automate siloed processes and improve workflow. By breaking down silos and connecting processes, systems, and people with company-wide digital workflows, hyperautomation platforms aim to help businesses build more resilient, productive, and innovative operational processes.

Salesforce, for example, focuses on composability through a targeted mix of technologies including API management, integration, RPA, and process mining to gradually achieve hyperautomation. This focus is what pushed the company to combine its subsidiary MuleSoft’s API and integration platform with Servicetrace’s RPA platform in 2021.

#4 – IT processes will be increasingly automated 

Workflow automation and enterprise scheduling service providers like Workato and Puppet help businesses automate IT-related tasks using triggers and actions. Such tasks, which IT teams have traditionally carried out manually, may include onboarding new hires with the applications they need to use, assessing the performance of applications, and managing incidents.

For example, Workato automates incident management by connecting information across multiple systems and escalating tickets via collaborative tools for assignment and triage. This allows for proactive detection of problems and automatic initiation of incident processes.

Through such IT automation, administrative bloat can be managed. Doing so will help reduce the friction between IT and business teams, increase productivity, conserve resources, improve efficiency, and reduce operating costs.

#5 – Convergence of company and external environment data

This emerged as a trend with enterprises around 2015-17 with sentiment analysis. Companies could use simple tools to scan the social media footprint of their customers and combine their internal data to test how happy their customers are, and what will make them stick. There were also more niche use cases such as using third-party databases to find the digital footprint of enterprises (through LinkedIn advertisements or Google searches, for instance) and predict what they may need next on their technological needs.

As this trend matures, enterprises are using external data more regularly in planning and forecasting through weather data and tourist inflow analysis, in supply chain management through freight prices and disruptions in foreign locations, designing the footprint of their retail stores through time spent in stores, and even in complex settings like pricing software solutions.

Risks may hold back the scale-up of AI use cases

Concerns over data ownership, privacy, and security have been a major deterrent for businesses to work with service providers offering AI solutions. Enterprises want a thorough vetting of the companies through their infosec processes, which can be a necessary but arduous task.

The risk, nonetheless, that weighs most on start-ups is the competitive and uncertain business environment, as AI start-ups may run the risk of folding if their solutions cannot get good commercial traction. While there is a lot of excitement surrounding this seminal technology, enterprises may keep their purse strings tight as many of the AI use cases can take a few months to a year to demonstrate financial results. These enterprises also need to fundamentally change their business models upon automation, which will typically take more time than the initial integration of the technology.

Finally, in a cash-constrained environment, a lack of venture capital or venture debt, coupled with poor cash generation from the enterprise customer segment can constrain (or even kill) some of the start-ups before they have scaled up. Thankfully, however, we can stay confident that ChatGPT will not see that fate.