Article

The Curious Case of Transformations — Where’s The Value?

By Shyam Doddavula, Saibal Samaddar, Tanushree Halder, Jitesh Gera

14 Feb, 2022
15 min read

Insights

Most business transformation efforts fail to extract maximum potential value, as targets are set without comprehensive benchmarking of each metric, coupled with decentralized handling of the program.
AI-based value management solutions help effectively identify, design, and realize value from transformations.
Their capabilities include benchmarking through organizational maturity assessments, automatic initiative identification through AI-driven knowledge graphs, real-time benefit tracking, and centralized value management with clear accountabilities and real-time status updates.
In addition to technology, successful transformations also require change in cultures and mindsets to improve openness and agility, and ensure executive sponsorship.

According to Infosys’ research, 84% of transformation programs fail to realize their true potential. Even organizations that claim to have undergone successful transformations realize only 67% of their maximum potential financial benefit, while the unsuccessful ones realize just 37%.¹

Why do transformations underperform?

Lack of executive sponsorship, poor change management, and improper measurement of metrics remain the key culprits behind underperforming transformations. These factors stem from two specific issues — a lack of proper benchmarks that help appropriately define the success criteria for each initiative and the unavailability of a unified platform to manage the entire transformation program.

The lack of appropriate, peer performance-based benchmarks leads to disagreement over what’s possible and what’s not. It creates ambiguity around goals and initiatives, resulting in uncoordinated efforts from business and transformation units. In turn, targets end up being looked at as vague and unrealistic numbers.

Lack of an appropriate benchmarking framework and a centralized value management platform are the two main reasons for underperforming transformations

The unavailability of a centralized transformation management platform leads to disparate information sources in documents held separately by different stakeholders. The visibility of the real-time scenario is extremely low, and periodic progress checks demand significant manual efforts. Overlaps across initiatives get uncovered at later stages due to this disconnection.

Consequently, there is considerable value leakage. Important metrics aren’t tracked enough, timelines get stretched, and accountability weakens. It’s a spiraling effect that ultimately leads to an underperforming transformation.

The three stages of value creation

Essentially, a firm simply needs to identify, design, and realize the value from a transformation (Figure 1).

Figure 1: The three stages of value creation in a business transformation

In the identification stage, a thorough assessment of all organizational functions, including sales and marketing, customer service, procurement, manufacturing, product development, finance, information technology (IT), and human resources (HR), is performed. A firm must understand where it stands against its peers on processes, technology, skills, and throughput. This assessment removes subjectivity from the improvement level required on key parameters within each function, and it allows firms to set maximum targets for each initiative.

Next, in the design stage, initiatives are implemented. Here, risks are identified and plans are implemented. If an initiative doesn’t progress as planned, course corrections are made. Change management kicks in with leadership communicating the rationale for change while seeking feedback from staff to make the process more inclusive. Communication and feedback are crucial in a change scenario. According to a 2020 survey, 32% of organizations see slow adoption of change initiatives when leaders don’t understand what drives value for their employees.²

Lastly, the realization of value happens through a hard-core focus on measuring metrics that matter. Periodic reviews are conducted and course corrections are made to achieve targets. Then, the financial impact is calculated and the final value is determined.

While every organization essentially works through these three stages, what differentiates a successful transformation from a failed one is the way it’s done. These stages must speak to each other through an agile feedback loop and enable proactive course corrections along the way. Moreover, decisions on initiatives should be backed by reliable and verifiable knowledge, internally and externally.

AI-based value management tools help truly integrate the identify, design, and realize stages

And this means intelligent transformation. AI-based value management solutions provide a centralized transformation platform embedded with peer benchmarking capabilities. These solutions automatically identify and prioritize the most relevant metrics across business functions. They also suggest initiatives that can help improve these metrics and measure the benefits they’ll generate. And they possess forecasting capabilities that help chart the path each metric would take based on the chosen initiatives and target improvements and suggest course corrections in case roadblocks appear.

Identify value with AI-based maturity assessments

Most organizations still prioritize transformational initiatives based on one-dimensional internal data and gut feelings, as they don’t have access to information on how their peers are performing in relevant metrics. This leads to vague targets, misplaced priorities, half-baked plans, and unclear accountability. As a result, about 45% of the potential transformational value gets lost during the identification stage (target setting and planning).³

Nearly half the potential value is lost in the identification stage itself, as firms fail to set realistic ambitions without proper benchmarking capabilities”

Maturity assessments evaluate an organization across five key pillars — business, customer, operation, brand, and synergy (Figure 2). These pillars cover all operational, financial, strategic, and process-oriented metrics. The organization’s performance regarding these metrics is then compared with that of the top-quartile, average, and bottom-quartile players across relevant industries and/or regions. This helps evaluate the organization at the enterprise, business unit, and individual levels.

Figure 2: The five pillars of an organization’s maturity assessment exercise

This way, companies can set their targets and goals based on objective data. When organizations set targets based on their full potential, they achieve 2.3 times the improvement they would normally.⁴

In the traditional process, these assessments are manual and the scope of the information that can be gathered is limited. Research is time-consuming, and experiences reside in people’s brains, not in a database. However, an AI-based tool can help capture information from consultants’ past experiences and the web, and also find linkages between various metrics for synergic improvement. These linkages are derived through a comprehensive knowledge graph at the core of all intelligence. This knowledge graph determines the connection between metrics across business functions to predict the potential effects an initiative focusing on one metric could have on others. Such tools can utilize the information gained from maturity assessments and their embedded benchmarking capabilities to suggest improvements in related metrics.

At the end of the maturity assessment, metrics requiring improvement are identified. These metrics are then prioritized based on the gap between a company’s actual performance versus the benchmarked value, in addition to their business impact. A simple 2x2 plot of metrics by their criticality and business impact can help move an organization toward the initiatives that drive maximum value.

For instance, assessing a firm’s HR function could result in benchmarking such metrics as time to hire (in days) and HR full-time equivalents per 100 other employees. These metrics indicate the experience a company delivers to its employees and prospective candidates. A deeper evaluation of these metrics would reveal the actual tasks that can be optimized. In the case of time to hire, for example, the company might need to reduce the time taken to screen profiles, source candidates, schedule interviews, and calculate compensation. Solutions such as automation of the interview scheduling process (through calendar management tools) and integration of compensation calculations with the employee database (as against individual calculations in spreadsheets and separate parity checks) could help reduce the overall hiring time.

Next, targets under each metric are defined along with timelines to achieve them. Project sponsors, business champions, technical champions, and owners are also defined for each initiative to establish clear accountability. In addition, each initiative is aligned to a value lever, which essentially specifies whether it would help reduce cost, increase revenue, improve efficiency, or lead to some other positive change. This helps an organization set up the foundations for extracting the maximum value out of its initiatives.

Design value with meticulous implementation and continuous progress reviews

At this stage, 35% of the total value leakage can happen, primarily due to the follow-on effects of the identification stage.⁵ The execution suffers when organizations don’t have a complete picture of why they’ve set out to achieve whatever they’ve planned. Poor planning results in more than the usual surprises during implementation, and these unaccounted issues derail the transformation efforts quite rapidly.

Implementation must be carried out with utmost attention to detail. The smallest aspects, such as the people to be involved, their responsibilities, the flow of information, and the processes to be targeted, must be defined and communicated carefully. At this stage, companies need to set up a centralized project management office that ensures clear communication between stakeholders and manages shifts in accountability until execution is complete. They also need to institute change management activities that prove the rationale for each initiative to help minimize resistance from staff and external partners.

In the earlier example of an HR function’s transformation, the organization would ideally evaluate off-the-shelf automation solutions for candidate screening and faster deployment of interview scheduling. This process could involve employee feedback on shortlisted vendors to ensure effective change management. Old systems can then be swiftly replaced by new ones — granted that staff is sufficiently trained. At this stage, leadership can help expedite any roadblocks, such as budget approvals.

Meticulous initiative implementation with timely course-corrections is key to minimizing value leakage. AI-based value management tools help flag off-course initiatives in real-time to ensure that

During and after the deployment of these solutions, the reduction in time to hire would then be measured to assess any gaps against the target. Continuous progress review, through periodic checks on whether people are adjusting to the change well enough and whether the employee/candidate experience is improving, ensures successful and timely completion of the initiative. The need for course corrections through other changes or additional initiatives is also evaluated regularly.

Technology can help here as well. Continuous updates on real-time actions and outcomes in a centralized platform can immediately flag off-course initiatives to their accountable stakeholders. Also, course-corrective actions can be automatically identified and suggested by AI-based tools. Such tools act as enablers for tighter control over execution and help minimize value leakage.

Realize value with real-time benefit tracking

Post implementation, companies tend to lose momentum. This is where the remaining 20% of value leakage typically happens.⁶

Benefits realization involves measuring and delivering the impact of transformation on the company’s financials along with the closure of operational targets. It also marks the adoption of new practices across the entire scope of the business impacted by the transformation. The financial benefits captured can be deflated (or inflated), depending on whether both direct and indirect impacts of transformation are correctly estimated. Benefits realization also follows from having correctly identified linkages between metrics across business functions at the first stage, indicating the spillover effect of value leakage from one stage to another.

Continuing with the example of an HR function’s transformation, the realization stage would involve measuring the benefits realized at the end of the timeline. Supposing the automation of tasks and other initiatives helped reduce time to hire by 50%, the change couldn’t be considered as an optimum improvement unless the metric stacks up closely against that of the top-quartile performers. Moreover, the company would need to chart out plans for further improvements at this stage and then restart the entire process of identifying, designing, and realizing value from new initiatives and the existing ones with further potential.

AI-based tools enable real-time measurement of value derived across the business, allowing room for course-corrections even in the final stages of a transformational initiative

As in previous stages, AI tools facilitate real-time tracking of realized benefits. As actions progress, these tools, utilizing their knowledge graphs, are able to calculate mutually exclusive value delivered by each initiative’s direct and indirect impact across the organization. This way, an organization can course-correct at this stage as well and minimize value leakage.

The intelligent route to value

Companies worldwide are predicted to invest $6.8 trillion in digital transformation during 2020-23, and in all likelihood, a significant chunk of it would be invested in AI-based developments.⁷ Further, AI is expected to contribute $13 trillion to the global economy from 2020 to 2030.⁸ Evidently, AI will play a major role in future business transformations and value creation.

AI uncovers improvement areas that aren’t readily identifiable by humans. AI platforms can immensely contribute to successful transformations in the value management space. However, any technology by itself shall never be enough. Extracting the maximum out of transformation programs, and doing so sustainably over a long period, requires a shift in stakeholders’ mindsets. Openness, agility, and executive sponsorship are the core elements here.

Businesses should build a complete AI-powered organization rather than take it up piecemeal. Culture, not technology, is the biggest challenge to AI initiatives. Leaders must address this challenge by investing in AI learning, communicating the urgency and need for its adoption, and flipping the decision-making approach from top down to bottom up.⁹