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Highlights
- Enterprises’ digital transformations should reach deeper into their operations to enable business model change and realize realize superior outcomes such as healthier cashflows, responsive supply chains, and frictionless experiences, and faster go-to-market.
- By embracing a machine-first approach to building cognitive operations, enterprises can realize greater value from their digital transformation initiatives.
- Machine-first cognitive operations help enterprises gain the capabilities to anticipate disruptions, improve uptime, and transform customer experience.
In this article
Transforming operations
Focus on building intelligent operations to pivot your digital transformation journey
Enterprises’ digital transformation often focus on operations that directly touch customers. They build mobile apps for customers to purchase their products and services or answer inquiries and complaints through social media. No doubt these efforts are important. However, relatively few digital transformations zero in on operations such as supply chain, finance, and internal knowledge networks. All of these can go a long way in improving customer experience and enhancing quality while boosting profitability, even though customers may not have direct exposure to these operations.
Take the case of a mobile app that makes it easier for customers to choose and order a product. If the product doesn’t arrive on time because of broken supply chain systems and processes, the app usage will plunge.
The bottom line? Digital transformation must reach much deeper into a company’s operations to enable them to:
- Predict demand patterns by product type and location, ensuring that warehousing and distribution can accommodate orders. Equally important, each customer order should trigger an appropriate internal production plan, including orders for raw materials.
- Ensure staffing plans for production and distribution are adequate to handle the orders. In turn, this will require systems that track employees’ needs, availability, and other factors.
- Identify peak and trough periods including seasonal variations to make sure supply can meet demand.
The good news? Data and the technology to make this possible are available in abundance. What happens behind the scenes in a company can be automated and enhanced with powerful digital technologies such as AI, cloud computing, and analytics. We call this as a machine-first approach to digital transformation.
What happens behind the scenes in a company can be automated and enhanced with powerful digital technologies such as AI, cloud computing, and analytics. We call this as a machine-first approach to digital transformation.
Machine-first approach
Creating intelligent operations with a machine-first approach
Let’s look at the three main elements in this approach:
- Using automation technologies to take over routine manual tasks and processes. Take, for instance, turning hand-written forms or applications into digital text, enabling the automation of huge data-entry tasks. Other automated processes include recruitment with the help of bots and AI-driven platforms, accounts payable and order booking across the telecom, retail, and travel industries.
- Extracting insights from data generated by those automated processes—insights that direct the next steps enterprises can take. For example, intelligent document processing and event-driven automation prove promising for retailers as it can reduce time taken for payment processing and ensure timely stock of supplies.
- Identifying process disruptions that are about to occur and taking action to prevent or correct them. In retail, for instance, it involves proactively identifying and solving connectivity issues before sales open—ensuring there’s no disruption in customer experience that can lead to customer churn.
Two factors make it possible today to take this machine-first approach to digital transformation.
- Data-driven analytics and AI help identify opportunities for process improvements. For instance, this could mean identifying ‘hidden spends’ in millions of records in procure-to-pay processes.
- Transform business processes by adopting agile and lean approaches and embed design thinking to sustain innovation.
Data at the core
Leveraging digital technologies to automate business systems and processes and tap into data
Taking a machine-first approach to building cognitive operations requires executives to move on three fronts:
- Moving from being process-centric to data-centric: With data proliferating from a variety of sources, it must be translated into information that makes work more effective and efficient. For example, a product development department should get real-time information on who is using the company’s products and services, and where and when they are using them.
- Automating both operations and delivery of insights: Intelligent automation involves extracting insights while eliminating effort and sharing the insights with the right team. Automated business operations eliminate manual intervention in filling orders, responding to customers, and ordering supplies. Systems supporting cognitive operations must provide alerts that predict problems before they happen.
- Providing real-time visibility for people to act: With automated systems delivering real-time alerts, cognitive operations give managers and workers insights to perform tasks that machines cannot. This requires a business operations center to provide intuitive dashboards with data visualization.
Today, enterprise leaders can take advantage of machine learning, AI, and advanced analytics capabilities to identify operational inefficiencies and determine how to fix them. In many cases, this does not require a huge overhaul of business operations. Often, companies only need to fine-tune their business processes and with automation they can realize benefits in a short time.
However, leaders need to constantly eliminate redundant and outmoded systems and processes, while investing in more effective systems and processes.
Advantages galore
Five quick wins businesses can get from building machine-first cognitive operations.
Firms we have worked with have experienced the machine-first advantage throughout their operations:
- Improved cash flow: Utilizing an automated cash forecasting system helped a British steel manufacturer analyze data from finance and other departments to reduce disputes and increase collections by 28% in a market that is seeing decreased demand.
- Procurement savings: Using AI software to scan purchase requests across multiple plants and vendors helped a global enterprise better understand the behavior that drives purchasing price variances across plants and reduce annual expenses by US$ 3.2 million.
- Self-healing IT infrastructure: Deploying automated health checks for the point of sale (PoS) systems and IT infrastructure for a US fashion retailer automatically resolved 95% of incidents detected. This was especially important during the peak shopping season when incidents increase significantly due to high volumes of transactions processed. Proactive incident prevention improved mean time to repair and provided 100% uptime for critical business applications.
- Improving customer onboarding: Enhancing integration of applications, and orchestration of the order-to-activate-service process with a case manager responsible for each order helped a leading telecom provider cut down order activation time to half while the net promoter score rose to 20 from -44.
- 100% auditing of travel and expense reports: Implementing new intelligent processes automated employee travel and expense management system for a major airline, enabling the 20-member back-office team to audit 100% of expense reports—up from 20% earlier.
These examples show the power of a machine-first approach to designing important business-outcome focused operations and embedding them with automated intelligence. Companies that have adopted a machine-first approach are unlocking new value from cognitive operations and leveraging it as a competitive differentiator.
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