Technical debt is generally seen as difficult to quantify. A new metric from McKinsey is making it easier for organizations to actually measure and compare their technical debt against others. As a result, organizations can have a better overall understanding of their problem, and how to figure out possible targets.

What is technical debt? 

Coined in 1992 by software developer Ward Cunningham, technical debt is, as McKinsey explains, “the off-balance-sheet accumulation of all the technology work a company needs to do in the future.” In other, more basic words, it’s the “result of prioritizing speedy delivery over perfect code,” as the team from ProductPlan explain.

This speed might help your organization meet deadlines (and make the resulting debt “worth it”), but if managed poorly — i.e. not paid back — it can create a string of problems. Despite this, technical debt isn’t inherently bad. In fact, like financial debt, there can be good and bad technical debts. As the team from Asana outlines in this great explainer article:

“In some instances, tech debt is the result of a calculated move to both meet software deadlines and ship high quality code within sprints. In other instances, technical debt is the result of an unavoidable mistake made when releasing a software update.” 

Quantifying technical debt

A McKinsey survey from July 2020 found that approximately 30% of CIO respondents “believe that more than 20% of their technical budget ostensibly dedicated to new products is diverted to resolving issues related to tech debt.” They also found that CIOs estimated tech debt is approximately 20 to 40% of the value of their entire technology estate before depreciation. Even more, almost half of respondents that completed modernization programs were not able to successfully reduce technology debt.

The results lead to the creation of McKinsey’s Tech Debt Score (TDS). This metric helps organizations quantify their technical debt — and measure themselves against peers. 

“This score helps companies rapidly understand the scale of their problem,” they explain, “identify what a feasible target state could be, and determine the corresponding economic benefit from an improved TDS.”

When McKinsey performed an analysis of tech debt at 220 companies across five geographies and seven sectors, they found a “significant correlation” between the TDS and business performance. Those in the 80th TDS percentile had revenue growth 20% higher than those in the bottom 20th percentile — and 10% higher than the average.

Meanwhile, a poor TDS performance ultimately leads to more tech debt — mostly because these organizations aren’t sure where to start or how to prioritize paying back the debt. They’ll pour money into applications that aren’t a big percentage of the technical debt, or modernize these in ways that won’t reduce the debt.

Ultimately, figuring out how to alleviate tech debt will be a different process for every organization. That said, the team from McKinsey have noticed some ‘best practices’ that are quite common across the board:

• Start by measuring the cost and size of the debt
• Price the debt into all IT services
• Make sure the remediation program is tailored to the specific company profile
• Systematically optimize tech debt via transparency

​We’ve come to rely on AI for so much in our everyday lives. Wayfinding apps, speech-to-text capabilities, facial recognition for unlocking phones — it’s all possible through AI. And while some organizations are getting the most out of the technology’s potential, a new Accenture report found that 63% — a clear majority — are still ‘testing the waters.’

According to Accenture’s new The art of AI maturity report, just 12% of organizations have advanced their AI maturity enough “to achieve superior growth and business transformation.” The report found that these ‘AI Achievers’ can chalk up, on average, 30% of their total revenue to AI.

That said, according to Accenture machine learning models, more AI Achievers will join the ranks with a suggested increase from 12% to 24% by 2024.

What is AI maturity? According to Accenture, AI maturity “measures the degree to which organizations have mastered AI-related capabilities in the right combination to achieve high performance for customers, shareholders and employees.”  

What do these AI Achievers do differently? Accenture identified five specific competencies:

• Leadership champions AI as a strategic priority for the entire organization.
• Heavy investment in talent.
• They industrialize AI tools and teams to create a strong AI core.
• Responsible AI design, from the get-go.
• Long- and short-term AI investments are prioritized.

Accenture also included other categories of organizations, as they relate to AI capabilities:

• AI Builders: “show strong foundational capabilities and average differentiation capabilities.”
• AI Innovators: “show strong differentiation capabilities and average foundational capabilities.”
• AI Experimenters: average capabilities in both categories; as mentioned above, they are still testing the waters.

Achievers, Builders, and Innovators together represent 37% of organizations surveyed. 

What industries have the most AI maturity? Tech is, little surprise, fairly far ahead of the game. The automotive sector has high hopes for AI-powered autonomous vehicles. Aerospace and defense firms will likely see demand for AI-enabled remote systems. And the life sciences will make use of AI in the development of pharmaceuticals. 

At the bottom? Financial services and healthcare have a fairly low AI maturity, largely due to factors like legal/regulatory issues, and lack of AI-trained talent.

AI isn’t new, but suffice to say, there is still a huge amount of room for AI growth, with AI Achievers setting the standards. 

“As AI technologies become more prevalent, the future of all businesses is going to look very different,” explains Accenture. 

“Some will lead the change, and some will be subjected to it. Those who transform will be the ones whose teams master the art of AI maturity, using cloud as the enabler, data as the driver and AI as the differentiator.”

Explore the full AI Maturity report, including use cases and industry-specific details here.

According to a new Salesforce global survey of 600 CIOs and IT decision makers, 91% of respondents report that demand for automation from business teams has increased over the last two years. 

As Salesforce reports, companies are faced with a variety of macroeconomic uncertainties (eg. hiring, costs), leading to a renewed focus on efficient growth, cost savings, and increased productivity by CEOs and business leaders. Automation, in turn, drives immediate value across all areas of business

Where is the highest demand? Four departments lead the way:

• Research and development (39%)
• Administrative/operations (38%)
• Customer service (33%)
• Marketing (26%)

The survey also dove into existing technology stacks, finding that:

• 96% respondents said that “modifying and rebuilding automations is a challenge as systems and business requirements change.” 
• 4 in 5 respondents said “restructuring existing application and data landscapes to support automation would likely compound their organization’s technical debt.”

“Organizations across every industry want to automate processes and customer experiences as quickly as possible. However, if they try to go fast with the wrong tools and techniques, they’ll actually impede true innovation,” said Matt McLarty, Global Field CTO & VP of the Digital Transformation Office, MuleSoft. 

Getting automation right

The key with automation is making sure all benefits are accessed, leading to quicker, more efficient growth.

According to the study, 44% of respondents are now using integration and API management capabilities to fully support their business process automation efforts.

McLarty also outlined why we need a new mentality with respect to automation in business: “Organizations should never just think that once a process is automated the job is done,” he said.

“The organizations that thrive in the digital economy are the ones that adapt to changes the fastest,” continued McLarty. “For established companies, that means focusing on three things: growth with efficiency and sustainability, agility with safety at scale, and continuous innovation.”

Learn more about the study and further automation trends from Salesforce/Mulesoft.