In 2024, AI experimentation dominated many business planning conversations. Today, leaders are focused on developing strategies to withstand external pressure from economic and regulatory volatility. As a result, the goals for AI are no longer simply about automation and efficiency. Now, companies are looking to AI to help them increase their adaptability and strengthen their business strategy.
In other words, while streamlining workflows is a noble objective and a prime use case for AI, executives must think beyond “How can I make this process faster?” and instead ask “How can I make this process smarter?”
According to Infosys Knowledge Institute, 50% of AI initiatives achieve some or all of their business objectives. The research also indicates AI success is closely tied to how effectively a business adapts its operations and data infrastructure to achieve that. Translated, this means that impact comes not from layering AI onto existing processes, but from rethinking how those processes work in the first place.
Key Steps To Building AI Strategies That Last
The following are key steps that I’ve found can help businesses successfully implement AI in a way that augments their agility and sets them up for the long term.
Define high-value use cases.
The present economic environment has increased the pressure to derive the maximum ROI from AI implementations. Leaders must be strategic about the best uses of these new technologies.
To respond accordingly, align IT, finance, operations and commercial teams to define shared objectives and identify the high-value use cases. Cross-functional alignment ensures the tools selected provide company-wide benefits rather than solving isolated departmental challenges.
For example, don’t just automate billing processes to accelerate invoicing. Extract more value by using a solution that validates rebates and incentives, monitors customer contract compliance and prevents revenue leakage. These processes touch finance, legal, commercial and revenue operations while delivering measurable outcomes that directly support top-line performance.
Build the right data foundation.
Strategic AI/agentic use cases require more robust and higher-quality data than basic operational automation. You must build a strong, integrated data foundation.
I suggest creating a data model that establishes a standardized definition for data fields across sources and applications. This model can enable your organization to move from siloed departmental views to business-wide analysis. For example, data integration can allow commercial teams to target the right customer segments and finance teams to forecast revenue more accurately.
Design for change.
Market conditions constantly shift, which means AI tools must be just as dynamic. Requiring intensive IT support for each change quickly bogs down workflows.
Adopt systems that allow for modular rules, configurable policy inputs and user-driven workflows. This flexibility ensures your commercial and revenue strategies can be recalibrated in hours as the policy and economic landscape evolves.
Invest in predictive analytics.
Rather than stopping at efficiency gains, organizations should leverage the AI systems they’ve already built for advanced analytics. Tools that were initially adopted to automate workflows can provide the clean, structured and timely data required for forecasting and modeling.
Implement AI-powered analytics that can identify market shifts, predict revenue impacts and simulate outcomes from regulations and changes in business strategies. The anticipatory capabilities of AI-powered analytics can help drive nimble adjustments in the turbulent business climate.
Prepare for agentic AI.
As AI matures, businesses can move toward decision automation. Agentic AI—systems that operate autonomously within defined parameters—can handle routine workflows and escalate only exceptions. If an AI model, for instance, detects that all pricing rules and compliance thresholds are met, it can auto-approve a transaction; if something falls outside of the norm, it flags a human reviewer.
When approaching this as part of your strategy, establish well-defined, rules-based processes where the decision criteria are clear and outcomes are measurable. Collaborate closely with domain experts to set parameters, escalation paths and exception criteria, ensuring the system mirrors how experienced teams make decisions today.
Redefining Roles In The Age Of Agentic AI
This “manage by exception” model allows organizations to streamline operations without sacrificing oversight. It’s especially valuable in areas like contract validation, rebate processing or quote approvals, where volume is high, but the logic is consistent. When paired with strong governance, agentic AI can increase operational efficiency while keeping humans focused on strategic decisions.
Maximizing AI’s value also means thinking differently about your employees’ roles. Automating basic tasks—like boilerplate coding and test generation—allows employees to spend their time becoming domain experts. Your employees’ in-depth industry knowledge about specialized workflows, customer pain points and specific compliance requirements is immensely valuable because it helps them shape products and services around customers’ real-world needs.
Armed with this kind of deep contextual knowledge, employees can better interpret AI-generated insights, question outliers and refine outputs. When combined with AI efficiency and insights, this expertise can help make your product stand out.
According to my company’s “State of Revenue Report,” 91% of surveyed business leaders say technology innovation and investment have had a measurable impact on revenue management outcomes, and 98% use or plan to use new technology, including GenAI, advanced analytics and AI/machine learning, for revenue management activities.
Businesses need more than automation and efficiency. What I believe is more important is technology that will solve broader problems spawned by the economic and regulatory climate. When thoughtfully applied, AI can help drive better decision making and increase business agility.
This article was originally published in Forbes.
