The future of enterprise AI isn’t just about insights – it’s about a monumental evolution of how businesses buy and sell in the global economy.
AI agents are poised to take automation beyond any capabilities that we’ve witnessed to date, shifting from AI tools that assist decision-making to independently thinking entities that augment execution at scale.
Deloitte predicts that by 2027, half of all companies will use GenAI to launch agentic AI pilots or proofs of concept, marking a significant transformation in how businesses operate.
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Challenges on the Path to Agentic Adoption
While agentic AI holds immense promise, organizations must first overcome multiple hurdles. Case in point: Another recent survey found that more than 85 percent of enterprises will require upgrades to their existing technology stack in order to deploy AI agents. Most businesses are still in the early stages of AI adoption, and scaling agentic workflows from initial investments to drive enterprise-wide ROI remains a major challenge.
The road to agentic AI requires rethinking IT infrastructure, ensuring seamless and quality data integration, addressing security and compliance risks, and fostering organizational trust in autonomous solutions – all while ensuring the right guardrails are in place. Without a well-defined strategy, companies risk inefficiencies, implementation barriers, reputational risk, and missed opportunities to harness AI’s full potential.
Complexity in Scaling
Agents individually aren’t enough. They can’t be deployed in isolation and need to work in coordination across systems to execute complex multi-step processes – manifesting as agentic workflows. Unlike monolithic systems with predictable interactions, an agentic workflow orchestrates a network of AI agents to solve intricate and layered problems autonomously with machine-scale analysis and human in the loop decision making.
Businesses need advanced orchestration frameworks capable of managing these complex interactions, ensuring robust error handling and maintaining workflow continuity across teams. Developing a clear roadmap will be critical in helping organizations deploy and scale AI agents effectively.
Accountability and Governance
With multiple agentic workflows operating independently yet collaboratively, ensuring accountability is a major challenge. Without a well-defined governance model, businesses risk a lack of oversight, which can lead to noncompliance, financial discrepancies, and reduced trust in AI-driven processes. Agents need to understand the rules of business that humans follow – rules that are defined by legal frameworks, ethical practices, and captured in contracts between customers, suppliers, and partners.
By “gut checking” decisions against contractual terms before taking action and ensuring clear audit trails are in place across the business, agentic decision-making becomes transparent and traceable, and far less likely to result in unnecessary liability.
Ensuring Data and Privacy
In any enterprise system, it’s critical for organizations to handle sensitive information responsibly and securely. Before deploying agentic workflows, ensure that data is clean and structured so sensitive information may be used by multiple agents simultaneously without exposure.
This applies to bank account details that are necessary for supplier payments, employee personal information, and contract data, as prime examples. Businesses should also establish secure data pipelines and continuous compliance measures to mitigate risks while enabling AI agents to function effectively and responsibly.
Trust and Change Management
Adopting agentic workflows requires more than just technical capability – it demands cultural change. Many organizations struggle with trusting AI agents due to concerns about reliability, accuracy, bias, ethical implications, and lack of transparency.
In fact, a recent study revealed data output quality and security and privacy concerns are among the top 10 barriers to AI adoption. Resistance to change within organizations, combined with a lack of understanding of how AI agents work, can create obstacles.
For businesses to fully embrace agentic AI, increase AI literacy and awareness around how AI agents operate with internal training and a top-down call to action driven by leadership. Emphasizing security protocols and privacy protections will also help to build confidence.
The First Step Toward an Autonomous Enterprise
So where can businesses realize immediate value from AI agents and agentic workflows?
AI agents are only as good as the data they train on. If enterprises want to drive profitability and capture returns from their AI strategy, they should start by looking at the data that drives the flow of commerce. Commercial agreements and the critical data they contain are foundational to how enterprises buy and sell, while also providing the compliance constraints agents need to do their jobs well without adding layers of risk.
The path to agentic AI is not a straight line. Yet by strategically addressing challenges, businesses can unlock new levels of intelligence and operational efficiency to embrace their future as an autonomous enterprise.
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