No longer a technology limited to data scientists, AI has been democratized even further by the latest user-friendly chatbots.
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 manpower requirements with the advent of technologies that can automate mundane tasks.
As this technology’s applicability grows rapidly, here are the key AI trends that you should take note of when considering its use in your business.
The five impacted trends
The democratization of AI technology will deliver value across the value chain
ChatGPT and similar advancements from now on will democratize AI: it is no longer a tool only for data scientists. Advancements in and increasing accessibility to “low-code” and “no-code” platforms are expected to completely revolutionize digital transformation journeys by expanding automation use cases across the value chain.
One use case is AI-based pricing that dynamically factors in determinants including cost fluctuations, market trends and internal price history. We can expect to see such AI use cases in a wide array of business functions including strategy, product management, marketing, customer care and procurement, across a multitude of industries.
There will be a significant uplift in automation use cases in operations
With expanding ease of access and growing awareness of AI benefits, we can expect deeper AI and automation applications, particularly in the backend of operations.
The adoption of AI for manufacturing is forecast to grow from US$1.1bn in 2020 to US$16.7bn by 2026. The coming years will see automation solutions offer much-needed upgrades in legacy operations, planning and optimization systems. The technology will facilitate improved network planning and optimization through integration of processes for data preparation, planning and data transmission.
Expect an increasing focus on hyper-automation
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 both simple and complex business processes to solve friction points in operations.
Platforms like ServiceNow readily offer enterprise service management solutions through comprehensive low-code automation solutions to digitize and automate siloed processes and dramatically improve workflow. By breaking down silos and seamlessly connecting processes, systems and people with company-wide digital workflow, hyper-automation platforms help businesses build more resilient, productive and innovative operational processes.
Such platforms focus on composability through a targeted mix of technologies including API management, integration, RPA, and process mining.
With the breakthroughs in generative AI causing greater awareness, hyper-automation is expected to extend to more industries including banking and finance, healthcare and retail, covering workflow solutions in sales, customer service and human resources.
IT processes will be increasingly automated
Workflow automation and enterprise scheduling service providers like Workato and Puppet empower businesses to automate IT-related tasks using triggers and actions. Such tasks, which IT teams used to 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, such systems automate 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, allowing for the reduction of friction between IT and business teams, increasing productivity, reserving resources and ultimately improving efficiency and reducing operating costs.
Risks associated with AI adoption may hold back use cases
Concerns over data security and governance are a major deterrent for businesses to work with service providers offering AI solutions. An abrupt stoppage of service due to cyberattacks such as Distributed Denial of Service (DDoS), for instance, has been a prime concern for enterprises in the past few years.
Meanwhile, in the current global political and macro-economic climate, and with AI use cases typically taking six to 12 months to demonstrate financial results, tech start-ups run the risk of folding if their solutions cannot get good commercial traction, especially with investors looking to put their money in safer, cash-generating businesses.