Sign in Subscribe
Reflections

Achieving Digital Maturity: A Study in Evolution

Neil Williams

• 2 min read
Achieving Digital Maturity: A Study in Evolution
Photo by Headway / Unsplash

The 2017 Achieving Digital Maturity study by MIT Sloan Management Review and Deloitte revealed the importance of aligning business strategy, talent, culture, and technology. Companies with a long-term vision, agile digital strategies, and cross-functional collaboration were the most successful in achieving digital maturity.

While said report made an important contribution to understanding digital maturity, significant shifts have occurred in the years since, reshaping how companies should approach digital transformation. From the rise of AI-driven tools like large language models (LLMs) to the massive digital acceleration brought on by COVID-19, businesses must now adapt faster and scale innovations more broadly. In this post, we explore what remains relevant from the report, highlight where it falls behind, and offer updated insights to help professionals working in applied AI thrive in today's digital environment.

Big Changes Since 2017

Since 2017, several global disruptions have reshaped the digital landscape:

  • COVID-19: Accelerated digital transformation and remote work across all industries.
  • LLMs (Large Language Models): The rise of AI-driven technologies, such as GPT models, has enabled more efficient data analysis, automated customer interactions, and advanced business forecasting.
  • Cybersecurity threats: As businesses adopt more digital tools, security concerns have surged, especially around AI-generated content like deepfakes.

Timeless Truths from the 2017 Report

Many insights from the 2017 report still hold true:

  • Cross-functional collaboration: The need for interdisciplinary teams to navigate digital transitions remains essential.
  • Long-term digital vision: Companies with a long horizon for digital strategy, anticipating shifts in market needs, have continued to thrive.
  • Digital talent: Attracting and retaining talent with digital skills is as crucial today as it was in 2017.

What Looks Out of Date

  • Digital experimentation: The 2017 emphasis on digital experiments as small-scale initiatives has become outdated. Today, agile enterprises must quickly scale digital innovations across the entire organization.
  • Limited AI use: The report barely touches on AI beyond analytics. The widespread adoption of AI and LLMs in business intelligence, process automation, and customer engagement is now fundamental.

Conclusion

Headline Comment
Emphasize Cross-functional Teams Still relevant and essential for success in today’s digital landscape.
Long-term Digital Strategy Critical for digital maturity, especially in volatile global markets.
Focus on Small Digital Experiments Scaling AI and digital technologies rapidly is more important than small experiments today.
Talent Attraction and Retention Digital skills remain key, but businesses now also need AI expertise.
Leadership in Digital Initiatives A visionary leadership remains vital, though focus on AI and emerging tech needs to increase.

For more recent insights, see reports from Deloitte on AI Trends and McKinsey’s Digital Transformation.