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Artificial Intelligence

Scaling AI: Giving data its due

November 9, 2021

In business, there are three distinct stages of scaling AI… First there’s “proof of concept,” then comes “strategic scaling,” and finally, if all goes to plan, you’ll be “industrialized for growth.” Accenture surveyed 1,500 CxOs, cross-industries around the globe with the goal of uncovering the success factors that contributed to scaling AI. We found that companies industrialized for growth made some big cultural shifts on their AI journey: One of the biggest? Making sure data, analytics and AI are democratized across the workforce and tightly aligned to the growth priorities of the business.

Aligning data strategy to business strategy

To become an organization that uses AI effectively, you need to start with the data. By leveraging valuable data and AI assets more broadly across their organization, 80 percent of their well-planned scaling initiatives are successful. Here are three things a company must do to reach sustainable and industrialized growth.

  1. Drive “intentional” AI. This means setting realistic expectations. With a clearly defined strategy and operating model, a timeline, structure and governance in place, AI and business goals are aligned and progress is possible.
  2. Tune out data noise. Over 90 percent of the data on earth has been created in the last ten years. Be careful what internal and external data you choose and determine what is business-critical, because as the saying goes “garbage in, garbage out.”
  3. Treat AI as a team sport. 92 percent of companies that have successfully reached industrialized growth have leveraged cross-platform, multi-disciplinary teams. There are AI advocates everywhere and the transformation happens across the entire organization; AI adoption is not the purview of a lone champion.

Reinventing how data and AI initiatives are executed against business strategy can result in a return on your investment at speed. This effect builds the case to move from AI pilots to enterprise-wide business transformation.

If you focus on the 5-10% of your data that drives 90% or more of your business value, you’re ensuring that the data informs your analytics.

By investing in your data foundation — data quality, data management, data governance models for the cloud, differentiating between data generation and consumption, and clear operating models — you will facilitate cleaner data, which in turn fosters smarter AI.

If you focus on the 5-10 percent of your data that drives 90 percent or more of your business value, you’re ensuring that the data informs your analytics. Your data analytics ultimately feed your AI models, allowing you to extract business-priority insights at speed and scale, driving better outcomes.

Brewing industrialized growth by scaling AI

The pressure to find new growth in the beer industry is hopping and competition from other drinks is fierce. One global brewer scaled up to industrialized growth by using machine learning to resolve their data veracity issues and create more accurate forecasting models. Their analytics were scaled over one hundred global datasets, from sales and forecast data, to social media, to weather and beyond. Company decision makers had access to commercial intelligence and actionable insights in record time. In the first year, the ROI was four times the initial investment.

Becoming data-led and growth focused

Why have so few companies on the AI journey reached industrialized growth? There appears to be a disconnect.

In our Accenture report AI Momentum, Maturity & Models For Success, we asked “To what extent do you expect to see analytics having a role in your organization’s artificial intelligence?” There was a huge variation in responses: 61 percent of all executives surveyed think analytics has either a moderate, minor or no role at all in their organization’s AI journey. Only 14 percent of companies who have not progressed on their AI journey think data and analytics are vital to successful AI adoption. And 79 percent of companies that have had real success with AI technologies say analytics have a major role in AI.

75%

of executives believe they risk going out of business in 5 years if they don’t scale AI

79%

of companies who have realized success say analytics have a major role in AI

84%

of executives believe they won’t achieve their growth objectives unless they scale AI

92%

of companies that reached industrialized growth leveraged cross-platform, multi-disciplinary teams

To realize the connection between data and AI, strong best practices are required:

Strategy & goals

Your data and analytics strategy need to be mapped against business goals.

360 view

Data discovery and augmentation should use internal and external data to give a 360-degree view and generate quality predictive analytics.

Governance

Data management requires planning, governance, monetization and compliance.

Culture

Businesses must adopt a data-driven culture and the democratization of AI and data.

If these milestones are executed well, data will become a competitive asset and the ultimate differentiator. By scaling AI with cloud, organizations can reposition their offerings, extend capabilities and improve data and AI maturity to create new sources of value and ultimately, sustainable growth.

​Michael Lyman

Lead – Strategy & Consulting, North America

Michael has more than 30 years of experience working with C-suite executives on major disruptive trends and transformational issues

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