Deploying more AI agents does not automatically make an enterprise AI-ready. Abhijeet Kate, Co-Founder and Principal Consultant at digiCloud Solutions, discusses why clear accountability, connected data, redesigned workflows and human oversight are essential to moving from experiments to dependable business operations.
- As AI agents move from answering questions to taking actions across enterprise systems, the question of accountability becomes increasingly important. When an AI-driven decision leads to a business outcome, who ultimately owns that outcome, and how should enterprises think about human oversight?
It helps to start with a simple truth: an agent can act, but it cannot answer for what it did. So the outcome stays with the business, and more precisely with the person who owns the process the agent works in. If a quote goes out wrong, that is the sales head’s call to own. If a payment is released in error, it sits with finance. The mistake many enterprises make is leaving this question until something goes wrong, when it should be settled before the agent goes live. Oversight then becomes a question of proportion. Not every action needs a human watching. Low-value, reversible tasks can run on their own as long as there is a clear log. Anything costly or hard to undo should pause for approval. Done well, oversight does not slow agents down. It simply makes sure every action can be traced, explained and corrected.
- Many enterprises are responding to the AI opportunity by deploying more copilots and AI agents across functions. Why does having more AI agents not necessarily mean an organisation is more AI-ready? What foundations need to be in place before enterprises can scale agentic systems effectively?
Counting agents is a bit like counting apps on a phone. It tells you how busy things look, not how well they work together. Many organisations end up with a handful of copilots, each sitting on its own data and doing its own thing. That is a set of pilots, not a capability. Readiness lives in what sits underneath. Data has to be clean and connected, because an agent working from poor records simply makes poor decisions faster. Processes need clear owners. Permissions need to define what an agent can see and do, and monitoring needs to show what it actually did. When those basics are settled, adding agents gets easier. When they are skipped, each new agent tends to add more to manage than it adds in value.
- Agentic AI has the potential to do more than automate individual tasks. Could its real impact require enterprises to rethink how workflows are designed altogether? What should businesses consider when deciding whether to automate an existing process or redesign it around AI from the ground up?
Yes, and that is where the bigger value tends to sit. A lot of enterprise workflows carry steps that exist only because older software could not connect systems or make a judgement call. Extra approvals, repeated data entry and manual handoffs are the usual suspects. If an agent simply automates them, the workaround survives and the inefficiency just runs faster. A good way to decide is to ask a plain question: would this process still make sense if the manual effort disappeared? If it would, automate it. If most of its steps were built to get around old limits, it is worth redesigning around the outcome the business actually wants. Mapping where time is lost and where errors creep in usually makes the answer clear. Starting from the outcome, not from today’s steps, tends to give the better result.
- As enterprises begin deploying specialised AI agents across sales, finance, customer service and operations, how do you see these agents working together? Could multi-agent environments create a new layer of complexity around coordination, data access, governance and decision-making?
The best way to picture it is a team that needs to share one view of the customer. A sales agent, a finance agent and a service agent all touch the same account. If each is working from a different version of the facts, they will give conflicting answers and sometimes take conflicting actions.
So yes, a new layer of complexity does come with this. Enterprises will need clear rules on which agent can access which data, how work passes from one to another, and who settles things when two agents disagree. A governance layer covering permissions, logging and escalation to a human becomes essential. The good news is that multi-agent setups work well when this coordination is designed at the start. Added later, it is much harder to retrofit.
- From digiCloud Solutions’ experience of working with enterprises across different sectors, where do you see the biggest gap today between organisations experimenting with AI and those that are actually ready to operationalise it? What are the common technology or process challenges you encounter when businesses try to move from AI pilots to production?
Experimenting is the easy part. A convincing demo can come together in a few weeks. Production is where the real world shows up, with inconsistent data, unclear ownership, security reviews, existing systems to connect to and users who are slow to change their habits. Across the enterprises digiCloud Solutions works with, the pattern is quite consistent. Data sits in separate systems. Nobody on the business side truly owns the pilot. Success is described as “it works” instead of a measurable result, and adoption is treated as an afterthought. The organisations that move ahead usually do something simple: they pick one specific business problem, connect the agent to real systems early, and judge it on an outcome such as faster response or time saved, not on how impressive it looks in a demonstration.
- digiCloud Solutions is exploring the combination of Agentic AI with a Headless 360 approach, where enterprise data and business capabilities can be accessed through different interfaces rather than being tied to a traditional application interface. What opportunities does this create for enterprises, and how do you see this changing the way employees and customers interact with systems such as CRM and ERP?
The idea behind Headless 360 is quite simple. What a system knows and does gets separated from the screen used to reach it. The CRM or ERP stays as the system of record, but people can reach it through whichever channel suits them, whether that is a chat app, a voice command or an agent.
For employees, that takes away a lot of the logging in, clicking through screens and typing that fills a working day. A field sales representative can update a deal or check stock in a single message. For customers, it means getting answers and taking actions in the channel they already use. Training needs fall as well, since people no longer have to learn a complex interface. The system underneath stays the same, but the experience of using it changes a great deal. In a sense, the interface becomes a choice, not a constraint.
- As digiCloud Solutions looks at the next phase of enterprise technology, where does the company see its role in helping businesses move towards more autonomous and AI-driven operations? What are the kinds of solutions or capabilities you believe enterprises will increasingly need over the next three to five years?
digiCloud Solutions sees its role as enabling enterprises to move from isolated AI experiments to governed, dependable operations. In practice, that means fitting agents into real workflows, connecting them to core systems on the Salesforce platform, and putting the right controls around what they do.
Looking three to five years out, a few capabilities will matter more and more. Well-structured, connected data comes first, since everything else depends on it. Then come agents designed around specific business outcomes, not general-purpose tools, along with governance and audit capability for autonomous actions. Conversational and headless access to core systems rounds it out. The enterprises that gain most will be the ones that treat AI as part of how the business runs, not as a separate project sitting beside it.
