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Summary

In a world where disruption is the new norm, business longevity is threatened by several factors. While enterprises seek digital transformation, hidden data has thwarted their efforts at a granular level. At a time when companies need more informed and fact-based decision-making, they hit their biggest barrier – data-driven insights. How can they unearth insights buried under everyday user interactions and leverage them to fuel transformation? Read this article to find out.

Companies are dying younger – the average lifespan of a company on the S&P 500 was just over 21 years1. The reason? Failure to adapt to an ever-changing and increasingly complex environment where disruption isn’t a single pivotal event but rather a permanent condition. Profound global shifts have got enterprise leaders grappling with various challenges, including – pandemics, geo-political conflicts, inflation, recessions, and permanent shifts in consumer behaviours.

Enterprises have invested billions of dollars in digital transformation programs to avoid these disruptions and capture more significant opportunities. Despite all these efforts, almost 70% of business transformation projects fail2. Where are they going wrong?

Most of these failures can be attributed to a lack of transparency and a fragmented understanding of their business. At a time when companies need more informed and fact-based decision-making, they hit their biggest barrier – data-driven insights.

To overcome this barrier – over and above the business model and operating model transformation – there is a need to embark on a data transformation led by artificial intelligence (AI). This third leg of transformation would be about shifting enterprises from the foundation of people and processes to one of software, data, and algorithms. What would such a shift entail?

Operational Work Insights – the crux of AI-led transformation

To create an understanding of their business, companies need three types of data:

  1. Firstly, operational transaction data stored in system logs, databases, data warehouses, and data lakes.
  2. Then, value chain data comes from partners, suppliers, distributors, and other parts of the ecosystem.
  3. Lastly, the data that still needs to be created must be generated. This data, hidden within the interactions between humans and software, can yield vital contextual insights available via the right discovery platform.

Most organizations depend on enterprise applications such as Oracle, SAP, and Salesforce to capture business data and leverage process mining to build an understanding of their processes. However, these represent only the tip of the iceberg, as employees spend 60% of their day on productivity applications like Excel, Word, PowerPoint, etc. This substantial portion of interactional data—critical for extracting hidden business value—lies uncharted. Tapping into this data can paint a more comprehensive picture of how work is done and give comprehensive operational work insights. However, process mining is not capable of capturing this hidden data. Task mining, on the other hand, can bridge this gap and get granular insights across diverse applications to unlock business value.

Task mining can help discern opportunities for automation and optimization, enhancing the customer and employee experience, improving risk management, governance, and compliance, and even driving top-line growth by uncovering upsell or cross-sell opportunities.

For instance, an operations leader can use operational work insights to identify the bottlenecks and blind spots in operations and understand how efficiency and other KPIs can be improved. Similarly, a compliance leader will be able to monitor and identify if the employees are adhering to the policies and compliance requirements.

Task mining enables compliance leaders to predict potential compliance failures and avert them in near real-time. An automation leader would benefit from discovering automation opportunities and minimizing the costs associated with manual discovery. Operational work insights provide better identification and scalability of automation.

How task mining accelerates digital transformation

Task mining is particularly beneficial in accelerating digital transformation journeys. The digital transformation lifecycle can be segmented into six stages: Discovery, Design, Develop, Evaluate, Execute, and Monitor. Task mining can add significant value at each stage.

  1. During the discovery stage, it provides insights into work patterns and identifies essential factors such as time to complete tasks, frequency, cost, and deviations or variations.
  2. In the design phase, it helps identify bottlenecks and barriers and feeds into the design blueprint.
  3. During the develop phase, task mining helps analyze automation potential.
  4. In the evaluate phase, it aids in prioritizing optimization and automation opportunities.
  5. At the execute stage, task mining informs change management efforts, promotes collaboration, and helps accelerate automation implementation.
  6. Lastly, during the monitor stage, it helps enterprises monitor process performance and employee productivity against desired outcomes, aiding in the continuous improvement of processes.

However, despite the evident benefits, a few challenges hinder the widespread adoption of task-mining solutions.

Key challenges and common pitfalls to task mining adoption
  • Compliance and data security considerations
    Organizations are not keen on exposing sensitive data to third-party providers.
  • Neglecting internal resistance
    Granular oversight into day-to-day operations can feel like surveillance and create a climate of insecurity and hesitancy to embrace the technology.
  • A siloed approach
    Deploying multiple task mining initiatives across business units and departments without an overarching strategy or cohesive approach doesn’t provide an accurate picture.
  • Lack of technology awareness
    General skepticism regarding the credibility of new technologies and lack of awareness about the capabilities and importance of task mining could impact stakeholder buy-in.
  • Not tracking the right business metrics
    Tracking for the sake of tracking yields no benefit. Companies need to define relevant metrics and KPIs and go after those.
  • Lack of process SME involvement
    The input from process stakeholders is crucial in setting up the right metrics to track using task mining.
  • Underestimating enterprise IT involvement
    Organizations often underestimate the time to secure approvals from enterprise IT for data access. This could cause unexpected delays in the process.
To overcome these challenges, enterprises need a well-thought-through approach to task mining that is people-first, leverages multiple technology levers, is integrated with automation, and is a joint effort between process excellence teams, automation teams, enterprise IT, and business leaders. To make the pot even sweeter, the approach can be self-funding – leveraging automation to generate and use these savings to fund transformation.

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Unlocking unlimited possibilities with Generative AI

As with everything else, Generative AI has a great potential to influence task mining. For instance, it could democratize the insights by helping even business users access them in natural language conversations. Large language models could be employed to summarize and synthesise insights swiftly, comparing the current output of the process discovery with standard operating procedures, for instance. Moreover, as AI evolves into a multi-modal form, it could generate a video of a user performing a task and then regenerate the required insights and documentation. This could increase the potency of task mining technologies, expediting their maturity. This acceleration would extend the depth and breadth of insights and deliver them faster.

The road ahead

The task mining software market is still in the early stages of maturity but is accelerating at 75+% CAGR3 and is expected to reach US$480 million in 2024. Embracing the right combination of digital transformation actions today can add US$1.25 trillion in market capitalization across Fortune 500 companies alone.4 The time is ripe to take advantage of this opportunity, and the first movers will gain significant ground quickly.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

References:
  1. https://www.statista.com/statistics/1259275/average-company-lifespan/#:~:text=In%202020%2C%20the%20average%20lifespan,even%20further%20throughout%20the%202020s
  2. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/about-deloitte/deloitte-uk-digital-transformation-are-people-still-our-greatest-asset.pdf
  3. https://www.everestgrp.com/press-releases/task-mining-software-market.html
  4. https://www.deloitte.com/global/en/our-thinking/insights/topics/digital-transformation/digital-transformation-value-roi.html

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