Moving from AI pilots to business-wide value requires a superhighway – how to build the ramp

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ZDNET Highlights

• Companies must demonstrate sustained early wins from AI investments to build momentum.
• Companies must invest in quality, controlled data and shared workflows.
• The key to successful agentic transformation is shifting from siled AI to systemic AI.

Scaling agentic AI in business requires a strong data foundation. Companies need reliable quality data as the backbone of agentic AI deployment. Business leaders should identify high-impact workflows to delegate to AI agents as a key capability to scale adoption. And scaling agentic AI starts with rethinking how work is done.

A strong data foundation and governance are important, but how can companies mature from AI agent innovation and pilots’ pockets to achieving business-wide value from AI?

According to accenture ResearchCompanies need to create intelligent superhighways – governed data, clear decision logic and codified workflows, cloud-native, modular architectures and a future-ready workforce.

Five ways AI can create business-wide value

Accenture found that nearly 9 in 10 (86%) organizations plan to increase AI investments in 2026, based on the belief that AI will help increase revenues. That said, only 21% of companies are redesigning end-to-end processes with AI at the core. Accenture research based on more than 6,000 AI engagements has identified five ways AI can create business-wide value.

1. Define AI’s timeline for business impact

Treat AI as a multi-year enterprise construct, not a quarter-by-quarter experiment; This requires long-term planning and action. It also means continued investment and the ability to identify and communicate short-term wins. Business leaders must define potential value targets to build organizational momentum. Accenture found that it takes 12 months or more to realize meaningful value from AI investments on the income statement.

2. Development of operational readiness

According to Accenture, 70% of technology budgets still support legacy systems that slow down the flow of information. To achieve operational readiness, companies must codify end-to-end processes so AI can work rapidly and at scale. The right form of AI must also be applied to how the work is done. Not all tasks require AI agents. AI agents are best used when workflows require logic; Otherwise, traditional automation can do the job. Accenture said many companies overuse agentic AI and leaders should avoid this trap.

3. Strong data base for AI

Accenture found that when data provides consistent context, it makes better decisions. Invest in governance and semantically consistent data, which requires a modern AI-enhanced cloud stack, AI guardrails, and redesigned workflows. AI-ready cloud environments are modular in design and support machine learning, generative, and agentic AI orchestration. A strong data foundation uses clean data to provide the right context – the shift from probabilistic to a more deterministic set of outcomes.

Companies need a consistent data strategy and access to high-quality proprietary datasets. It is data and metadata (data about data) that provide AI agents with the relevant intelligence to execute tasks reliably. Accenture identified two working patterns: rebuild entire processes in which agents organize the workflow in the system, or call agents only when AI increases performance.

4. Talent matters – it’s about people and technology

Only one in three executives believe their talent strategy is fully integrated with their AI strategy. We have to redevelop talent in the workplace. It’s not technology that hinders, it’s people. Accenture found that while more than 40% of organizations are upskilling their people, less than 10% are redesigning roles. Companies should invest in training and reskilling. Companies will also have to put humans forward.

At Salesforce, we’ve found that becoming an agentic enterprise is less about technology change, and more about relational change. Relational changes include the six ‘Rs’:

1. Process redesign with humans and AI.
2. Re-skilling our people.
3. Redeploying people into new high-impact roles.
4. Restructuring of our teams and organizations (financial implications).
5. Recalibrating new performance metrics.
6. Recapturing latent value (content we overlooked in the past that can create value for our stakeholders).

Business value improvement is born when your company becomes increasingly autonomous through digital labor.

5. New AI operating models are the only way to drive value

AI cannot scale inside a pre-AI operating model. A future-ready AI operating model is about shared capabilities, not silos. This means that companies must invest by purchasing, promoting, or creating ecosystem partners. A future-proof AI ecosystem will provide your company with access to talent, better tools, and stronger opportunities for co-innovation.

Barriers to business-wide scale of AI

According to Accenture, the transition from experiments to enterprise-wide value is a journey in three dimensions: Siled AI to prove and diagnose, Structural AI to build systems for scale, and Systemic AI to embed intelligence at the core. Accenture defines each dimension as:

1. Siled AI: Productivity gains are visible in pockets (often in enabling operations), but progress is hindered by fragmented data, ad-hoc governance and weak end-to-end links. Win quick credibility and address bottlenecks by modernizing priority data domains, building joint business-technology governance, and initiating talent reinvestment.
2. Structural AI: As companies build enterprise architectures and operating models for scale, the momentum shifts from experiments to institutional capacity. Organizations that act on key enablers – value leadership, talent, digital core, responsible AI and continuous improvement – ​​are more likely to drive high-value use cases.
3. Systemic AI: Companies in this stage combine technological sophistication with deep changes in talent strategy, role design, and leadership behavior. Intelligence is built into the enterprise core. They consider reinvention as an ongoing capability rather than a one-time change. According to Accenture, only a small group of organizations move toward systemic AI, where intelligence becomes embedded in the enterprise core.

Accenture found that less than one in five organizations have adequately modernized their data, platforms, governance and talent systems to support widespread AI deployment. Accenture research shows that the barriers to business-wide scale of AI lie in outdated operating models. A key finding from Accenture was that organizations that unlock AI’s full potential are more likely to adopt it as a strategic imperative – cloud readiness increasingly separates AI transformational leaders from laggards.

Security is also a top priority. Building resilient AI systems requires security by design. Accenture research shows that while early wins with AI agents are needed to build organizational confidence, it is systemic AI that will determine long-term success and overall business value.

I love this quote from an Accenture report: “AI rewards commitment, not impatience. Nobody wants a racecar in a traffic jam” To learn more about Accenture research, you can visit Here.