Author: Stefan Ivić, Partner, Consulting, Head of Data and AI for Deloitte Central Europe
The advancement of Artificial intelligence (AI) has created opportunities to disrupt traditional ways of operating, coordinating and optimizing activities across organizations.
What do we really mean by AI?
AI is a suite of technologies and methodologies that use advanced algorithms to ‘learn’ from large amounts of data, imitating three behaviours that are traditionally associated only with human intelligence: interacting with other humans, completing routine tasks, and generating insights and predictions about future events.
Building a more robust, data-enabled AI business model is now imperative
The unprecedented availability of data, along with cloud and AI capabilities to process and learn from it, is creating many opportunities for consumer companies to reimagine agility in their business processes. The adoption of these new data- and AI-enabled processes will separate the winners from the losers, equipping them with the ability to better sense and respond to changes in consumer behaviour, optimize performance and customer relevancy in turbulent times, and handle supply/demand shocks or other unforeseen business disruptions with greater resiliency.
To become an AI-first organization, first you must define what role AI will play in your business and the value you expect it to generate. This will give your organization a vision of the capabilities, skillsets, and applications to rally around and guide the journey as you navigate through some of the common use cases below:
Understanding, predicting, and shaping customer demand
AI can enable an accurate and timely understanding of your customers’ needs and direct them to products. At the same time, respond to micro and macro changes in consumer demands trends.
Reducing the likelihood of an unexpected shock to inventories. Understanding the complete customer journey with a greater transparency into how the customer interacts with your enterprise across different channels. Segmenting your customer base to differentiate and customize your offerings based on their specific needs.
By combining data from multiple channels with the power of machine learning, consumer businesses can better understand the complex relationship between customers and products across all touchpoints. Mining internal and external data to gain insights into the preferences of similar individuals, allows organizations to create offers, incentives, and a shopping experience that appeal to a specific set of people.
Improving operational efficiency
Machine earning and AI can help improve operational efficiency incrementally.
Using new sources of data, new techniques, and automation, consumer businesses could radically improve their operational productivity at the hyper-localized level.
Adopting data science and machine learning techniques will allow companies to automatically determine and optimize performance, increase productivity, and minimize constraints. AI can also help remove steps from manual processes, freeing people to perform more strategic tasks and develop further system improvements.
Building supply chain resiliency
Companies can improve the resiliency of their supply chain by harnessing data that can be used to predict the risks and provide mitigating strategies to protect against uncontrollable external factors.
As supply chains stretch farther around the world, they get even more difficult to manage because of things like unpredictable weather patterns and external sources of risks.
The data available through social media streams could provide valuable live information about the status of locations, assets, sentiments, and weather in various regions; it could be used to make better decisions. Data availability and appropriate insights are critical for any system to manage supply chain risks effectively. With simulation and digital twin technology, companies can facilitate a faster analysis process, a faster quantification process, and often better suggestions for mitigation based on a wide-ranging analysis of past scenarios.
AI Empowering TaxTech
AI and ML can be employed to dramatically improve the capabilities of traditional RPA tools through “intelligent automation” of compliance within the tax department of the organization.
Through an iterative learning process, AI enabled applications are fed large amounts of data, which they automatically process for the desired function - either validation or correction. As more transactional services are digitized, more data is created, which may also be fed back into the AI and ML models, thus constantly improving tax processes with increasing accuracy and consistency.
As more accounting and finance tools become digital, AI can play a larger role within an organization seeking to leverage intelligent automation to manage the analysis of dense and complex tax regulations and applicable laws, receipts and invoices, as well as other written text. While scanning physical bills to capture the relevant information would be an arduous and slow method of documentation, by combining Optical Character Recognition (OCR) and AI algorithms, digitized invoices and bills can be run as scripts in the development processes, making AI a far more realistic and capable solution for downstream tax compliance work.
The scale of becoming an AI-enabled organization can seem daunting, but a focused, value-based, and incremental approach to adoption will help to deliver immediate value and mobilize the entire organization to move toward a new future.