Digital Operating Models: Unlocking Value

In the fourth edition of the newsletter, I shared my views about the need for intentional digital transformation. In this, I introduced the '9 Levers of Value' framework to underpin a holistic digital transformation. Further, in the fifth edition of the newsletter, I outlined ways in which digital transformation can help extend and/ or expand the business model.

Presenting the sixth edition, which considers the ways in which digital transformation can help enhance the operating model of an organization. This includes identifying good use cases for different types of digital interventions, and also importantly, areas where digital investments may not be suitable.

The operating model of an organization includes core processes such as manufacturing/operations, supply chain/ logistics, technology as well as corporate/ support services such as finance, human resources and IT. Additionally, it includes the people performing these processes along with the organization structure, controls/ governance framework, and culture.

Digital transformation has a profound impact on the operating model of organizations, forcing them to consider which activities should (continue to) be performed by employees vs. machines/ algorithms. The resulting change management and the need to manage employee motivation as well as culture makes this even more challenging.

To consider areas where digital transformation can be effectively deployed, a useful framework is to consider effort/ complexity vis-a-vis the impact/ outcomes. This presents a few opportunity areas:

1. Digital Automation

Activities which have low levels of complexity and impact are great candidates for automation initiatives. Examples include volume-driven processes that have a Standard Operating Procedure (SOP) or are performed by an outsourcing partner. Several such activities are required for regulatory or compliance reasons, which is why they are classified as 'low impact'.

These activities are good candidates for a variety of tools including ERP, point solutions, Intelligent Automation, and Low Code/ No code solutions. The relatively lower cost per transaction of such tools enables a good Return of Investment (ROI).

The emergence of AI/ GenAI tools are enabling automation of processes that require some flexibility rather than 100% rule-based processing, as well as those that require ingestion and/ or comprehension of documents or other media. However, while feasibility has now expanded, a careful cost/ benefit analysis is required to assess whether AI/ GenAI investments are truly needed.

An interesting example of what's possible using AI is automating the manufacturing quality control process using a visual inspection AI tool.

Another example we deployed for a large Technology company was to use GenAI to create Balance Sheet Reconciliation narratives explaining monthly variances in accounts. This eliminated the need for human executives who currently perform this activity, as an input for review by finance controllers.

In addition to efficiency benefits, such tools also offer benefits in terms of better consistency/ reliability, by eliminating cases of human error and fatigue.

2. Digital Augmentation

While AI that can replace human workforce is a few years away, the latest AI/ GenAI tools can significantly augment/ enable people in performing processes. A good application would be in areas which are important but not significantly complex. The benefits here include efficiency, but also an improved experience for both the employees performing the activity as well as the recipient of the service (internal/ external).

A good example to showcase such an approach was for a large global organization in the area of Marketing Development Funds payout approval. The existing process had human teams manually verify - Proof of Spend (typically invoices evidencing cost incurred); and Proof of Execution (photos/ documents evidencing completion of an eligible activity such as advertising or an agreed promotional activity with customers). The reviewing team includes cursory checks to identify and reject duplicate documents and any erroneous documents (amount/ document mismatch). However, deploying an AI-based tool here dramatically improved the ability to screen for duplicates/ mismatches, and also add capabilities to detect tampering/ photoshopping and geotagging (where possible) to improve the process governance and reduce fraudulent payouts. In these cases, the human remained 'in-the-loop' for rejected claims to reduce the potential for false positives. The role of the team changed from reviewing all documents to reviewing claims flagged for rejection, improving the turnaround time as well as the employee experience.

3. Digital Advise/ Insights

The biggest opportunity for AI/ GenAI is in activities which support decision making. These are quite important but are complex (and have therefore been hard problems to solve using digital transformation). Consequently, most decisions are taking based on experience/ 'gut-feel'. However, AI and GenAI are equipped with very strong capabilities to consume large amounts of unstructured data, synthesize the information and provide a reasoning-based recommendation. As increasing amounts of data get captured and analyzed, the role of AI and GenAI in identifying patterns and insights is set to grow.

We have seen multiple instances of intelligent forecasting solutions being deployed in organizations. These solutions are able to consider a large number of inputs such as macroeconomic variables and develop a Machine Learning model to forecast projected volumes/ revenues/ profits. There are also successful case studies of companies adopting a 'data-driven operating model', especially for SaaS/ platform companies with a large presence in the Small and Medium Business (SMB) segment.

An interesting example was a supply chain resiliency tool deployed at a large global company. This tool used Machine Learning to create a graph model of the entire supply chain, and overlaid a public dataset of disruptions to identify potential points of failure and suggest backup/ recovery strategies.

Another application is the (increasing) usage of conversational tools and interfaces. These are being increasingly embedded into a wide range of applications including ERP, CRM, Business Intelligence tools and office suites. These allow on-demand analyses and suggested actions, enabling technology to truly be a 'co-pilot' alongside humans who need to finally make the decisions.

These decisions can yield significant outcomes in the form of revenue growth and/ or cost reduction. Unsurprisingly, these opportunities represent very attractive use cases for AI and GenAI from a Return-on-Investment perspective.

4. Process Adjustment needed (Avoidable digital investment)

Finally, activities that are complex but not important/ impactful need to be reviewed carefully. Such activities are sometimes needlessly complex due to legacy reasons and can be simplified significantly. Several firms make the mistake of not considering impact/ Return on Investment criteria for digital investments and expend precious time and bandwidth on insignificant problems.

Summary

Digital transformation is a powerful tool to improve efficiency, productivity, employee/ stakeholder experience, and can enable informed decision making. However, it is critical to assess use cases, and direct investment to the most attractive opportunity areas.

An old quote from Eliyahu Goldratt is still as relevant today!

"Automation is good, so long as you know exactly where to put the machine" - Eliyahu Goldratt

#ArtificialIntelligence #GenerativeAI #Technology #Digital #TechTonicThursdays