The promise of AI goes beyond automation, driving the creation of novel products and services across various sectors and generating new revenue streams. Early AI adopters are already witnessing tangible results from the technology.
CIOs and their C-suite peers who initially harnessed AI to automate workflows and other tasks are now eyeing the next big opportunity: using machine intelligence to handle complex decision-making. It’s little surprise, then, that IDC estimates AI spending in the Asia-Pacific region will exceed US$90 billion by 2027.
Chatbots for customer service
Chatbots are a prime example of how AI is redefining decision-making. Having evolved beyond their early versions, IDC’s Future of Enterprise Resilience & Spending Survey ranked chatbots as the second most integrated generative AI element among businesses in Asia-Pacific.
The quality of chatbots today not only improves customer experience but also reduces costs for organisations that no longer require employees to wait on hold or navigate complex menus.
AI-powered chatbots can draw from a large database of knowledge, allowing for immediate and accurate answers to customer requests. The technology’s voice and sentiment analysis capabilities also mean it can make adjustments to tone, recognise customer intent, and take actions to resolve it. This level of nuance is driving businesses to shift towards AI-powered decision-making in customer service.
In addition to being a great vector for automated data collection, businesses can tap into AI-powered communications for a range of customer care processes — from service requests to answering enquiries, guiding customers through purchasing, and even conducting promotional activities. Ultimately, this also goes a long way to making customers feel empowered, as they can now independently interact with businesses via AI that leverages a central database.
Optimising sales
The beauty of integrating AI into data processes is the ability to efficiently collect and analyse data from across various platforms. This is particularly useful to sales representatives who need client data at their fingertips in order to secure deals. AI-enabled sales systems can extract information from CRMs, social media, email threads, content interactions, and more to identify leads with the highest likelihood of conversion.
Sales teams can also tap into machine intelligence to help keep track of their various sales conversations, prompt them to follow up, and even help them prepare for meetings by suggesting questions or offers that a particular prospect would be most responsive to. As with any AI-enabled system, outcomes can be fed back into the models to drive improvements and increase accuracy in the system’s predictions.
Implementing dynamic pricing
From airlines to ride-sharing services and online retailers, dynamic pricing has been a key capability in adjusting to changing market conditions. The utilities industry particularly stands out, having long implemented dynamic pricing through sophisticated algorithms. All this to say, AI can equip businesses to adjust pricing structures based on fluctuations in demand and supply.
Digital twins for supply chain optimisation
AI promises efficiency when it comes to optimising supply chains, as it can juggle the many variables involved — from inventory to processes and transactions. This provides enhanced visibility across supply chains through digital twins, which mirror all assets and systems. By doing so, businesses can not only predict potential problems but also identify existing weaknesses and loopholes.
One example is AI’s use in assessing the capabilities of various vendors. This empowers executives to make cost-effective and reliable choices when selecting suppliers. Additionally, AI is useful in streamlining logistics operations via the implementation of smart contracts, in tandem with blockchain technology. Smart contracts can automatically trigger processes and transactions when specified conditions are met, demonstrating that AI is proving to be a key to future-proofing the supply chain.
Predictive maintenance
AI-powered data management and analytics also enable a more proactive approach to system maintenance. AI can efficiently sift through and analyse vast amounts of data to streamline processes and identify potential issues that may elude a human technician. This capability is crucial not only for IT security but also for data management, governance, compliance, and integration needs. AI can detect issues across the system early enough for them to be fixed before failure occurs.
As a predictive maintenance tool, AI can enhance decision-making efficiency. System maintenance can be scheduled and conducted without disrupting business operations, thus reducing downtime and financial losses from unexpected breakdowns. Deloitte estimates that predictive maintenance can reduce maintenance costs by 25% and breakdowns by 70%, while increasing productivity by 25%.
That said, AI is not a silver bullet, nor will it ever be. While the technology is advancing rapidly and becoming more reliable, leaders should bear in mind the need for human input in business decisions. Relying solely on AI can negatively impact the quality of overall decision-making.
The key is to find a balance that aligns with each organisation’s specific needs. Through a well-thought-out strategy, enterprises can position themselves to leverage AI’s full potential for driving customer personalisation and centricity.
