Dec 11, 2020

Accenture: Strategic scaling AI shows 3X the return

Janet Brice
4 min
AI (artificial intelligence)
Three-step guide to strategically scaling Artificial Intelligence (AI) in your business can leverage three times the ROI, report Accenture...

Leaders who strategically scale Artificial Intelligence (AI) investment enjoy three times the return compared to companies who pursue a silo approach, report Accenture.

During a time when 84% of C-suite executives believe they must leverage AI to achieve their growth objectives, the message from Accenture is “focus on the ‘I’ in Return on Investment (ROI)” in the new report, AI: Built to Scale. 

“An overwhelming majority believe achieving a positive return on AI investments requires scaling across the organisation. Yet 76% acknowledge they struggle when it comes to scaling it across the business,” comment Accenture. 

According to statistics, three out of four C-suite executives believe if they don’t scale AI in the next five years, they risk going out of business entirely.

“With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics? Asks Accenture.

Insight from global research

Global research by Accenture involved 1,500 C-suite executives from companies with a minimum revenue of US$1 billion in 12 countries across 16 industries, looked at:

  • Extent to which AI enables the business strategy
  • Top characteristics required to scale AI
  • Financial results when done successfully

Three distinct groups of companies of AI emerged from the research:

Strategic scaling

  • CEO focus with advanced analytics and data team solving big rock problems
  • Multi-disciplinary teams of 200+ specialists championed by Chief AI, Data or Analytics Officer
  • Able to tune out data noise
  • Accenture predict 15-20% of companies are here

Proof of concept factory

  • Analytics buried deep and not a CEO focus
  • Siloed operating model typically IT-led
  • Significant under investment, yielding low returns
  • Accenture predict 80-85% of companies are here

Industrialised for Growth

  • Digital platform mindset and enterprise culture of AI democratising real-time insights to drive business decisions
  • Less than 5% of companies have evolved to this point

“Considering the companies in our study collectively spent US$306 billion on AI applications in the past three years, the ROI gap amongst them is significant. How significant? US$110 million between companies in the Proof of Concept stage and Strategic Scalers,” comments Accenture.

The report explores if there is a relationship between successfully scaling AI across the enterprise and key market valuation metrics? And what is the “premium” for being a leader?

“We discovered a positive correlation between successfully scaling AI and three key measures of financial valuation with an average lift of 32% on enterprise value/revenue ratio, price/earnings ratio, and price/sales ratio.”

Accenture report a three-step guide to strategically scaling AI in your business:

  1. Drive intentional AI

According to statistics, Strategic Scalers are 65% more likely to report a timeline of one to two years to move from pilot to scale.

“To successfully scale, companies need structure and governance in place - Strategic Scalers have both - 71% say they have a clearly defined strategy and operating model, while only half of the companies in Proof of Concept report the same,” says the report.

“Strategic Scalers are also far more likely to have defined processes and owners with clear accountability and established leadership support with dedicated AI champions.”

  1. Tune out data noise

Strategic Scalers tune out data noise as they recognise business-critical data, identifying financial, marketing, consumer, and master data as priority domains plus they are more adept at managing data. 

Accenture research shows they have a more accurate data set (61% versus 38% of respondents in Proof of Concept). And 67% of Strategic Scalers integrate both internal and external data sets as a standard practice compared to 56% of their Proof of Concept counterparts.

“They use the right AI tools - things like cloud-based data lakes, data engineering/data science workbenches, and data and analytics search to manage the data (60% compared with 47%) for their applications. 

  1. Treat AI as a team sport

The effort of scaling calls for embedding multi-disciplinary teams. “It’s a lesson Strategic Scalers have learned well. In fact, 92% of them leverage multidisciplinary teams,” say Accenture.

“It enables faster culture and behaviour changes. In contrast, those still in Proof of Concept are more likely to rely on a lone champion within the technology organisation to drive AI efforts.”

Three additional variables that speed companies along their journey to the ultimate destination are also identified in the report and include:

  • Focus on the ‘I’ in Return on Investment (ROI)
  • Adopt a digital platform mindset to scale
  • Build trust through responsible AI

“Scaling the exponential power of AI across the enterprise is a journey. Those that learn the lessons on each path will reach a place where the business is seamlessly fused with intelligence that boosts productivity and effectiveness,” concludes Accenture.

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Jun 12, 2021

How changing your company's software code can prevent bias

Lisa Roberts, Senior Director ...
3 min
Removing biased terminology from software can help organisations create a more inclusive culture, argues Lisa Roberts, Senior Director of HR at Deltek

Two-third of tech professionals believe organizations aren’t doing enough to address racial inequality. After all, many companies will just hire a DEI consultant, have a few training sessions and call it a day. 

Wanting to take a unique yet impactful approach to DEI, Deltek, the leading global provider of software and solutions for project-based businesses, took a look at  and removed all exclusive terminology in their software code. By removing terms such as ‘master’ and ‘blacklist’ from company coding, Deltek is working to ensure that diversity and inclusion are woven into every aspect of their organization. 

Business Chief North America talks to Lisa Roberts, Senior Director of HR and Leader of Diversity & Inclusion at Deltek to find out more.

Why should businesses today care about removing company bias within their software code?  

We know that words can have a profound impact on people and leave a lasting impression. Many of the words that have been used in a technology environment were created many years ago, and today those words can be harmful to our customers and employees. Businesses should use words that will leave a positive impact and help create a more inclusive culture in their organization

What impact can exclusive terms have on employees? 

Exclusive terms can have a significant impact on employees. It starts with the words we use in our job postings to describe the responsibilities in the position and of course, we also see this in our software code and other areas of the business. Exclusive terminology can be hurtful, and even make employees feel unwelcome. That can impact a person’s desire to join the team, stay at a company, or ultimately decide to leave. All of these critical actions impact the bottom line to the organization.    

Please explain how Deltek has removed bias terminology from its software code

Deltek’s engineering team has removed biased terminology from our products, as well as from our documentation. The terms we focused on first that were easy to identify include blacklist, whitelist, and master/slave relationships in data architecture. We have also made some progress in removing gendered language, such as changing he and she to they in some documentation, as well as heteronormative language. We see this most commonly in pick lists that ask to identify someone as your husband or wife. The work is not done, but we are proud of how far we’ve come with this exercise!

What steps is Deltek taking to ensure biased terminology doesn’t end up in its code in the future?

What we are doing at Deltek, and what other organizations can do, is to put accountability on employees to recognize when this is happening – if you see something, say something! We also listen to feedback our customers give us and have heard their feedback on this topic. Those are both very reactive things of course, but we are also proactive. We have created guidance that identifies words that are more inclusive and also just good practice for communicating in a way that includes and respects others.

What advice would you give to other HR leaders who are looking to enhance DEI efforts within company technology? 

My simple advice is to start with what makes sense to your organization and culture. Doing nothing is worse than doing something. And one of the best places to start is by acknowledging this is not just an HR initiative. Every employee owns the success of D&I efforts, and employees want to help the organization be better. For example, removing bias terminology was an action initiated by our Engineering and Product Strategy teams at Deltek, not HR. You can solicit the voices of employees by asking for feedback in engagement surveys, focus groups, and town halls. We hear great recommendations from employees and take those opportunities to improve. 


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