Organisations worldwide are racing to embed generative AI and automation, yet most lack the operational maturity to scale effectively—hampered by data gaps, process rigidity and workforce readiness. Our research reveals that while 74% of investments are meeting expectations, 64% of enterprises still struggle to transform how they operate, and 61% report their data is not AI-ready. However, reinvention-ready companies are pulling ahead dramatically, achieving 2.5× higher revenue growth, 2.4× greater productivity and 3.3× more success scaling AI use cases than their peers. This case study assesses the state of operations maturity across industries and sets out a concrete playbook—centred on data governance, talent reinvention, cross-functional ownership and process mining—to help enterprises close the gap and unlock measurable performance gains.
Case Study Source: Accenture
Problem Statement
Organisations are racing to embed generative AI and automation into their operations, yet most are not operationally ready—citing data gaps, scaling hurdles and workforce preparation issues that slow adoption and limit value capture.
Goal
Assess the state of operations maturity with generative AI and automation, and set out concrete actions to help enterprises modernise processes and scale AI to drive measurable performance improvements.
Challenges
**64%** of organisations struggle to change how they operate.
**61%** say their data assets are not yet ready for generative AI.
**70%** find it difficult to scale projects that rely on proprietary data.
**82%** of early‑stage organisations have not applied a talent reinvention strategy or prepared workers for AI‑led workflows.
**78%** of executives report AI is advancing faster than their training can keep up.
Actions
Establish centralised data governance and a domain‑centric approach to modernise data, connecting processes and tools across functions.
Adopt a talent‑first reinvention strategy, redesigning work, processes and workflows to pinpoint where generative AI creates the most value.
Make business and technology teams co‑owners of reinvention so assets, platforms and products fully leverage generative AI at scale.
Use leading practices such as cloud‑based process mining to benchmark, expose process gaps and target operational inefficiencies.
Key Results
Impact
Shows a clear business case for AI‑led operations, with outperformance of up to 2.5x revenue growth, 2.4x productivity and 3.3x scaling success versus peers.
Provides an actionable playbook—data governance, talent reinvention, joint ownership and process mining—to accelerate maturity and value realisation.
Underscores urgency: the majority remain unprepared, with 64% struggling to change and data barriers at 61% readiness and 70% scaling challenges.
The AI Readiness Gap: Why Most Organisations Aren’t Prepared to Scale
Companies are rushing to adopt generative AI and automation, but the vast majority aren’t operationally ready. Data shortfalls, scaling difficulties, and unprepared workforces are holding them back from realising real value.
The research examined how mature organisations are with AI-driven operations and identified practical steps to help businesses modernise and achieve tangible performance gains.
The Readiness Crisis
The numbers paint a stark picture. Nearly two-thirds (64%) of organisations find it hard to change their operating models. More than half (61%) admit their data isn’t ready for generative AI use.
When it comes to scaling, the challenges intensify. 70% struggle to expand projects that depend on their own data. For those just starting out, 82% haven’t developed strategies to retrain staff or prepare them for AI-driven work.
Perhaps most telling: 78% of senior leaders say AI technology is moving faster than they can train their people to use it. The gap between ambition and capability is widening.
Four Steps to Close the Gap
Successful organisations are taking a structured approach. First, they’re building unified data governance frameworks that connect systems and processes across departments, making information accessible and reliable.
Second, they’re putting people at the centre of transformation. This means rethinking roles, redesigning workflows, and identifying where AI adds genuine value before deploying it.
Third, they’re breaking down silos. Business and IT teams share responsibility for transformation, ensuring platforms and tools are built to support AI at scale.
Finally, leading firms use tools like cloud-based process mining to spot inefficiencies, compare themselves against best practice, and prioritise improvements.
What Success Looks Like
Returns on investment: Three-quarters (74%) of organisations report that their AI and automation spending has met or beaten expectations.
Growing confidence: 63% intend to expand their AI and automation capabilities by 2026.
Accelerating maturity: The share of firms with fully modernised, AI-led processes nearly doubled—from 9% in 2023 to 16% in 2024.
The performance premium: Companies that are truly reinvention-ready are seeing 2.5 times higher revenue growth, 2.4 times better productivity, and 3.3 times greater success scaling AI use cases compared to their peers.
Widespread adoption: Generative AI is already being used across functions—75% in IT, 64% in marketing, 59% in customer service, 58% in finance, and 34% in R&D.
Why This Matters
The business case for AI-led operations is now clear. Firms that get it right are seeing multiples of performance improvement versus those that don’t.
But the findings also reveal how urgent the challenge is. Most organisations remain stuck—struggling to adapt, lacking ready data, and unable to scale effectively.
The path forward combines strong data foundations, workforce readiness, cross-functional collaboration, and disciplined use of analytics to drive continuous improvement. Those who act now stand to gain a significant and growing advantage.
Case Study Source: Accenture
These industry AI case studies featured on our site are based on publicly available sources and are presented for informational and educational purposes only; we do not claim ownership of these case studies or affiliation with the companies mentioned, and attribution is provided where applicable.
