Top industry executives share their perspectives on critical success factors for AI success; benefits and gains; and pain points and challenges for regional enterprises.
How to secure transformation around AI
The objective should be to make AI useful without making its behaviour unpredictable. Securing AI transformation means building prevention, governance and runtime control into the workflow from the outset, rather than adding them after deployment says Ram Narayanan at Check Point Software.
Check Point’s AI Defence Plane provides a unified architecture for governing and protecting AI across employees, applications and Agents. AI Agent Security provides visibility into Agents, their tools, data access and behaviour, with runtime controls for prompts, tool calls and actions.
AI Red Teaming enables organisations to test AI applications and Agents against attacks before deployment and as systems change. Workforce AI Security governs employee interaction with AI tools, while the AI Network Firewall extends inspection and control to AI traffic across the enterprise from the firewalls organisations already run.
Together, these capabilities provide security controls across the AI lifecycle, rather than treating each AI deployment as a separate security problem.
Critical success factors
Prevention should come first, because once an unsafe AI-driven action is detected, it may already have taken place. That begins with control over what an AI system is allowed to access and do. An enterprise does not need every process to be redesigned before introducing automation, but it does need clear ownership, defined permissions and a security architecture that can accommodate machine-driven activity across network, cloud, workspace and AI systems.
That means knowing which data an Agent can access, which tools and applications it can call, and which actions require human approval. Identity also needs to evolve beyond traditional user accounts because Agents increasingly operate with their own credentials and delegated access. Enterprises should be able to discover which AI tools and Agents exist, govern what they can do, and protect activity in real time.
The technology environment also needs sufficient integration for Agents to work with existing applications and data without creating uncontrolled side channels. An open, platform-based approach matters because Agents operate across environments from many vendors. Just as importantly, security teams need visibility into AI activity before it moves into production.
The business condition is equally important. There needs to be a clear reason for giving an Agent autonomy in the first place. Automating a poorly defined process simply makes a poor process run faster. The strongest starting point is a bounded workflow with measurable outcomes, clear accountability and defined limits on autonomous action.
The objective should be to make AI useful without making its behaviour unpredictable. Securing the AI transformation means building prevention, governance and runtime control into the workflow from the outset, rather than adding them after deployment.
Benefits and gains
The immediate benefit is operational leverage. Automation can remove repetitive work and allow people to spend more time on decisions that require judgement, while Agents can handle defined tasks across systems at a speed that manual processes cannot match.
The bigger opportunity comes when individual automations begin working together. An Agent can retrieve information, interpret it, call the appropriate tool and move a business process forward without requiring a person to coordinate every step.
That can shorten the distance between an event and the business response. In the medium term, this creates more consistent execution across teams and reduces the gaps that appear when processes depend entirely on manual handovers.
Over the longer term, the value is less about replacing individual tasks and more about changing how enterprises operate. AI can become part of the operating fabric of the business, helping organisations coordinate activity across applications and environments while allowing people to focus on higher-value decisions.
The organisations that benefit most will treat security as an enabler of that transformation, not a constraint on it.
There is also a security benefit when automation is designed correctly. Threat intelligence and security controls can be translated into action at machine speed rather than waiting for manual intervention. The important distinction is that greater automation should not mean greater acceptance of risk.
At machine speed, detecting an unsafe action after the fact is often too late. The strongest enterprise models will combine autonomous execution with controls that understand context and stop an unsafe action before it becomes an incident.
Pain points and challenges
The harder problem is maintaining trust as autonomy increases. An Agent may perform a legitimate action in one context and create risk in another, particularly when it is working with sensitive data or tools that can change enterprise systems.
Security teams need to understand the sequence of actions an Agent is taking, the context behind those actions and whether its behaviour remains within the authority it was given. Visibility must cover not only the Agent, but also the prompts, tools, data and downstream actions involved in each workflow.
The challenge is therefore not simply controlling access. It is controlling outcomes without removing the autonomy that makes Agentic systems useful. That requires runtime security, continuous testing and governance that can keep pace as Agents, permissions and workflows change.
Organisations must strike the right balance between innovation and control, ensuring AI Agents remain effective, trustworthy and aligned with business objectives as they evolve.
Key takeaways
- Organisations that benefit most will treat security as an enabler of transformation and not a constraint.
- Prevention should come first, because once an unsafe AI-driven action is detected, it may already have taken place.
- Identity also needs to evolve beyond traditional user accounts because Agents increasingly operate with their own credentials and delegated access.
- The business condition is important and there needs to be a clear reason for giving an Agent autonomy in the first place.
- Automating a poorly defined process simply makes a poor process run faster.
- The starting point is a bounded workflow with measurable outcomes, accountability and defined limits on autonomous action.
- The objective should be to make AI useful without making its behaviour unpredictable.
- Securing AI transformation means building prevention, governance and runtime control into the workflow from the outset.
- The opportunity comes when individual automations begin working together reducing the distance between an event and business response.
- The value is less about replacing individual tasks and more about changing how enterprises operate.
- The strongest enterprise models will combine autonomous execution with controls that understand context and stop an unsafe action.
- The challenge is not controlling access; it is controlling outcomes without removing autonomy that makes Agentic systems useful.
Prevention should come first, because once an unsafe AI-driven action is detected, it may already have taken place.
Building a foundation for enterprise scale automation
The solution is not to modernise everything before starting and a better approach is to establish a practical baseline. Identify where ageing systems, poor data quality and architectural sprawl create the most operational friction. Then build a unified view across the existing estate says Soliman Ayman at DXC Technology.
This year, DXC Technology will launch LabX, the AI-native product incubator, designed to turn a validated business challenge into a production-ready solution in 90 days or less. For customers, that means a faster and more controlled route from AI ambition to business value.
They can prove whether a solution works before committing to broader investment, reduce the risk of pilots that never reach production and adopt technology that has already been tested in enterprise conditions. The result is practical innovation, faster learning and a clearer path to scale.
Critical success factors
One obstacle that consistently slows automation and Agentic AI is technical debt. Years of deferred upgrades, duplicated platforms and fragmented data create an environment where an agent may be intelligent, but still cannot see enough, connect enough or act reliably enough to deliver business value.
The answer is not to modernise everything before starting, as that is unlikely to be feasible. Instead, a better approach is to establish a practical baseline. Identify where ageing systems, poor data quality and architectural sprawl create the most operational friction. Then build a unified view across the existing estate, rather than assuming every system must be replaced.
From there, leaders can make sharper choices. Some applications should be retired or consolidated. Others need targeted modernisation. A few may simply need better interfaces, controls or data access. Prioritisation should follow business impact, not technical elegance.
The business preparation matters just as much. Give each initiative a clear owner, a measurable outcome and agreed boundaries for human oversight. That creates a workable foundation for enterprise scale automation.
Benefits and gains
AI is not simply another tool to add to the way people already work. Its value, across all the time horizons mentioned, comes when organisations rethink the work itself and the relationship between people and technology.
Initially, automation can remove routine tasks and bring information to employees when they need it. This saves time, improves consistency and reduces the friction of moving between disconnected systems.
As adoption grows, the bigger opportunity is to redirect that time towards better judgement, stronger collaboration and faster customer problem-solving. Over time, technology can adapt to the individual rather than forcing every individual through the same process.
Of course, that shift will not happen automatically. Enterprises must design around the workflows, decisions and needs of different roles. Done well, AI becomes more than a tool people operate. It becomes an extension of how they think, collaborate and create value.
Pain points and challenges
The central challenge is not building or deploying the agent. It is understanding the work well enough to build the right one.
Leaders may see the objective but not the workarounds, friction and breakdowns that shape daily work. Systems capture the formal process, while employees may describe symptoms without identifying the structural problem. A sound solution can therefore automate the wrong thing.
The answer is disciplined human-centred research. Surface leadership assumptions, compare them with customer and employee experience, map real journeys and validate patterns at scale. Then involve users in design and testing to see whether the solution reflects their reality.
The added value of this approach is that people are more likely to adopt technology when they recognise their own workflows made better.
AI becomes more than a tool people operate and becomes an extension of how they create value.
Key takeaways
- One obstacle that consistently slows automation and Agentic AI is technical debt.
- Years of fragmented data create an environment where an agent cannot see enough to deliver business value.
- The central challenge is not building the agent. It is understanding work well enough to build the right one.
- Give each initiative a clear owner, a measurable outcome and agreed boundaries for human oversight.
- AI technology can adapt to the individual rather than forcing every individual through the same process.
- Leaders may see the objective but not the workarounds, friction and breakdowns that shape daily work.
How AI can help redesign the enterprise
Speed can change the dynamics of a business and faster processes can translate into quicker decisions and better overall experience. In the medium term, the opportunity is to redesign how the organisation works. Agentic AI can coordinate activity across functions and keep services running around the clock, says Joe Dunleavy at Endava.
Endava helps organisations identify where automation and AI can create the most value, build the technology and data foundations to support it, and design, implement and scale solutions into live operations. Our approach spans strategy and discovery, engineering and integration, governance and ongoing optimisation.
Dava.Flow is Endava’s AI-native engagement lifecycle, connecting Signal to Evolve as ideas through discovery, delivery, and live operation. Agents take on more of the build and testing activity, while engineers govern decisions, standards and risk. Telemetry makes every change and intervention traceable.
The result is a continuous flow from intent to measurable outcome, helping organisations move from isolated AI experiments to faster, safer and more accountable technology solutions.
Critical success factors
The barrier to entry is lower than it has ever been. For basic automation, businesses do not necessarily need perfect enterprise data, complex infrastructure, or even deep IT involvement. Generative AI tools increasingly allow any team to automate routine tasks and experiment with new ways of working using capabilities that are already available to them.
The more important question is what conditions are needed to move beyond experimentation and create meaningful, repeatable value. That starts with identifying the right problems: workflows where automation can improve speed, quality, or customer experience, while freeing people from repetitive work to focus on higher-value activities.
As automation becomes more sophisticated and agents begin interacting with enterprise systems and data, organisations need stronger governance and controls to manage the additional risk and complexity.
This includes appropriate access controls, traceability and clear data ownership, alongside explicit guardrails defining what agents can do autonomously, and where people retain authority. Security, compliance, human oversight, and performance measures need to be built into the workflow.
For Agentic AI to scale beyond isolated use cases, enterprises also need an operating model that can coordinate agents, systems, and people. So, the minimum conditions to start are increasingly modest. The real challenge is creating the foundations to move from accessible experimentation to intentional, governed automation, focused on the areas where it can create the greatest business value.
Benefits and gains
In the short term, the gains are practical and measurable. Automation can remove repetitive manual work, reduce errors, shorten cycle times, and lower operating costs. Importantly, it also frees people to focus on higher-value activities that require judgement, creativity, or deeper customer engagement.
Speed can change the dynamics of a business too: faster processes can translate directly into quicker decisions, more responsive customer service, and better overall experience. This is where AI can start to help organisations capture value through efficiency and productivity.
In the medium term, the opportunity is to redesign how the organisation works. Agentic AI can coordinate activity across functions, keep services running around the clock, and help teams better respond to customers or change conditions in real time. This is where businesses move from isolated productivity gains to a different operating model while still supported by Human-In-The-Loop.
The long-term opportunity is more value and growth. Agentic AI can make products and services economically viable for customers who were previously too costly or difficult to serve. In financial services, for example, personalised advice, once reserved for wealthy clients, could be offered on a scale to people with modest balances.
More broadly, expertise that was once scarce and expensive can become more accessible, adaptive and continuously available. This is often referred to as more of the value creation side of AI.
Expertise that was once scarce and expensive can become accessible, adaptive and continuously available.
Key takeaways
- For basic automation, businesses do not necessarily need perfect enterprise data, complex infrastructure, deep IT involvement.
- Generative AI tools allow any team to automate routine tasks and experiment with new ways of working.
- The important question is what conditions are needed to move beyond experimentation and create repeatable value.
- Identify the right problems: workflows where automation can improve speed, quality, customer experience.
- Speed can change the dynamics of a business: faster processes can translate into quicker decisions.
- For Agentic AI to scale beyond isolated use cases, enterprises need an operating model that can coordinate agents, systems, and people.
Agentic AI is ongoing business transformation
Introduce Agentic systems incrementally, starting with small, focused use cases where you can measure business impact and safely manage autonomy. Agentic AI, needs appropriate governance including defined permissions, security controls, auditability, and a human-in-the-loop approach, explains Ellen Cox at Epicor.
Successful adoption starts with a clear business problem. Enterprises should ensure they have a defined problem and that AI is the right solution to solve. Good starting points are typically highly manual, repeatable efforts.
Enterprises should also have well-defined, repeatable processes, clean and accessible data, a secure technology foundation, and integration features that allow systems to work together. Strong leadership sponsorship, clear success measures, and employees who are prepared to adopt new ways of working are equally important.
For Agentic AI, organisations also need appropriate governance. This includes defined permissions, security controls, auditability, and a human-in-the-loop approach. Introduce Agentic systems incrementally, starting with small, focused use cases where you can measure business impact and safely manage autonomy.
Benefits and gains
Short-term: automation can reduce manual, repetitive work, improve response times, and help employees access and act on information more quickly. Agentic AI can assist with multi-step tasks, helping teams improve productivity while keeping people involved in decision-making.
Medium-term: organisations can achieve more consistent processes, improved data quality, faster decision-making, and better coordination among departments. As systems become more connected, AI can assist in identifying patterns, recommend next-best actions, and continuously improve workflows.
Long-term: automation and Agentic AI can help create a more adaptable enterprise. This can look like resiliency and ability to respond quickly to shifting customer needs, market conditions, and other business demands. Human expertise is still important. AI enables employees to focus on higher-value, strategic, and customer-facing work.
Pain points and challenges
Short-term, the main challenges are often practical: unclear processes, inconsistent data, limited integration or legacy systems, and uncertainty about where to begin. We get a lot of concerns about employees worried about the implications of AI on their roles, which makes communication, training, and change management essential.
Beyond that, the challenge is scaling past individual pilots. Enterprises need to scale across functions by establishing consistent governance, monitoring performance, controlling costs, and ensuring AI outputs are accurate, explainable, and consistent with business policies. Without a coordinated approach, organisations may create isolated solutions that do not deliver enterprise-wide value.
Long-term, businesses need to continually adapt their skills and governance structures as AI capabilities evolve. They will also need to address issues such as accountability, privacy, cybersecurity, regulatory requirements, and maintaining trust among employees, customers, and partners.
The organisations most likely to succeed will treat Agentic AI as an ongoing business transformation versus a one-time technology implementation.
We get a lot of concerns about employees worried about the implications of AI on their roles.
Key takeaways
- Automation and Agentic AI can help create a more adaptable enterprise.
- Main challenges are practical including unclear processes, inconsistent data, limited integration or legacy systems.
- Businesses need to continually adapt their skills and governance structures as AI capabilities evolve.
- Without a coordinated approach, organisations may create isolated solutions that do not deliver enterprise-wide value.
Enterprises need to prepare for the AI operating model
As AI capabilities integrate into established workflows, organisations can move from automating individual tasks to planning multi-step processes. Solutions can combine reasoning capabilities of AI agents with the execution of playbooks, supporting various levels of human oversight, says Tony Zabaneh at Fortinet.
Fortinet is applying Agentic AI across security and network operations. FortiSOAR combines Agentic AI with automation playbooks, allowing agents to support complex workflows while retaining defined controls and predictable execution. FortiSOC provides a unified cloud-delivered approach to security operations, bringing together core capabilities across Fortinet’s SecOps portfolio, while FortiAI introduces Agentic workflows across security operations.
FortiAI-Assist is also being embedded across Fortinet’s portfolio to support tasks ranging from security investigation to network configuration, troubleshooting and optimisation. FortiAIOps meanwhile uses AI and machine learning to provide network visibility, identify anomalies and help teams troubleshoot and resolve network issues more efficiently.
Critical success factors
The introduction of Agentic AI requires enterprises to be clear on how AI will fit into the existing technology and operating architecture. As agents begin retrieving data, interacting with APIs and initiating workflows across business systems, existing controls around identity, policy enforcement and data governance need to extend to those interactions.
It is also important to have a clear definition of where agents can act autonomously and where human approval is needed. Automation can then be introduced within established boundaries rather than giving agents unrestricted authority.
Integration is another practical requirement, as Agentic AI becomes more useful when it can work with existing applications, data sources and operational processes instead of becoming another isolated technology layer.
An organisation’s workforce also needs to be prepared for a different operating model. AI is not only changing how tasks are performed, but also how teams manage increasingly autonomous systems.
Organisations need people with the skills to supervise AI-enabled workflows, interpret outcomes and intervene when required. Fortinet’s 2026 Cybersecurity Skills Gap Report cites 60% of organisations saying their top recruiting challenge is finding cybersecurity talent with specific experience in AI.
Benefits and gains
Reducing the amount of manual effort involved in repetitive tasks is probably the most immediate benefit of automation. In security operations, Agentic AI can assist with investigating alerts, gathering evidence and threat hunting, helping analysts move more quickly from an alert to an understanding of what happened. In network operations, AI can similarly simplify troubleshooting and configuration tasks.
As these capabilities integrate into established workflows, organisations can move from automating individual tasks to planning multi-step processes. FortiSOAR combines the reasoning capabilities of AI agents with the predictable execution of playbooks, supporting varying levels of human oversight, from human-supervised to increasingly autonomous workflows.
The long-term opportunity is to make operations more adaptive. Agentic capabilities can analyse information, determine appropriate next steps and coordinate actions across connected environments.
As environments become more complex, this can help organisations scale operational expertise without requiring a corresponding increase in manual effort. The value extends beyond productivity, allowing teams to allocate more attention to complex decisions while repetitive processes are handled through automation.
Pain points and challenges
The main challenge is increasing autonomy also increases the potential impact of mistakes. Organisations first need to address practical issues such as skills, integration and the readiness of existing infrastructure. Fortinet’s 2026 Cybersecurity Skills Gap Report also showed that 63% of organisations expect greater demand for AI oversight and governance roles over the next three years.
At scale, maintaining accountability becomes a challenge. Organisations need to know what an agent has done, why it took an action, and whether that action remained within policies. Building that level of control without undermining the speed and autonomy that make Agentic AI valuable will remain an important part of enterprise adoption.
Enterprises need to be clear on how AI will fit into the existing technology and operating architecture.
Key takeaways
- It is important to have a clear definition of where agents can act autonomously and where human approval is needed.
- Automation can be introduced within established boundaries rather than giving agents unrestricted authority.
- Agentic AI becomes useful when it can work with existing applications, data sources and operational processes.
- An organisation’s workforce also needs to be prepared for a different operating model.
- AI is not only changing how tasks are performed, but how teams manage autonomous systems.
- Organisations need people skills to supervise AI-enabled workflows, interpret outcomes and intervene when required.
- Fortinet’s 2026 Report cites organisations saying their top recruiting challenge is finding cybersecurity talent with experience in AI.
Three minimum conditions for AI success
The three conditions for success are process clarity, governance architecture, and leadership alignment on what success means. Before any automation is introduced, an enterprise must be able to map its workflows with enough precision that an AI agent can be held accountable to outcomes, not just output, says Fernando Cea at Globant.
Glob.AI is Globant’s AI-native delivery platform, purpose-built to translate raw model capability into production-grade, auditable business outcomes. Its core unit is the AI Pod: a supervised, AI-native team structure where artificial intelligence executes under human accountability.
Glob.AI is model-agnostic, it operates as an intelligent harness across providers, ensuring enterprises always access best-in-class capability without lock-in. The platform provides process observability, model evaluation, context management, and delivery certification at scale.
This is backed by over 20 years of enterprise delivery discipline across seven specialized AI Studios in more than 35 countries, making it infrastructure for AI that ships.
Critical success factors
The real barrier to Agentic AI in the enterprise is not infrastructure, it is institutional readiness. What we consistently see, across industries in the Middle East and globally, is that organisations invest in AI capability but neglect AI governance. That gap is where initiatives go to die.
The minimum conditions are three: process clarity, governance architecture, and leadership alignment on what success means. Before any automation is introduced, an enterprise must be able to map its workflows with enough precision that an AI agent can be held accountable to outcomes, not just outputs. That is a harder conversation than most organisations are prepared to have.
On the technical side, the infrastructure requirement is less about specific platforms and more about observability. Can you audit what the AI did, why it did it, and what business result it produced? Without that traceability, you are not running an enterprise AI system, you are running a very expensive chatbot.
And from a leadership standpoint, the shift that matters most is from measuring AI by features deployed to measuring it by certified, governed business outcomes. Enterprises that make that cognitive shift early, particularly those aligned with the UAE’s national AI agenda and Saudi Vision 2030, will compound their advantage rapidly. Those that do not will be perpetually in pilot mode.
Benefits and gains
The short-term impact is immediate and measurable, and the proof is already in the market. Some examples: FIFA increased throughput efficiency by 20%. YPF compressed contract processing timelines by 40%. A major bank’s COBOL migration, scoped at 14 months, was delivered in two. These are not projections, they are validated outcomes.
For enterprises in MENA, particularly in financial services, government transformation, and energy, the opportunity to achieve similar step-changes is real and accessible today.
In the medium term, the value proposition evolves from efficiency to structural transformation. Agentic AI begins rewriting how entire business processes operate, not just accelerating them, but redesigning them. Claims processing, financial close, document intelligence, contract management, these functions do not just get faster, they get smarter and more adaptive.
Commercial models shift accordingly: organisations move from paying for time and headcount to paying for certified outcomes, and the institutional knowledge generated compounds over time.
Long term, the implications are at the level of business-model reinvention. Pharmaceutical companies can accelerate molecule discovery. Insurers can build risk profiles that transcend historical data.
For National Agendas aligning economies actively diversifying beyond hydrocarbons, Agentic AI is not a technology upgrade, it is an economic infrastructure play. The enterprises that commit now will define what industry leadership looks like in the next decade.
Pain points and challenges
The short-term challenge is almost never technical, it is executional. Enterprises deploy AI tools without the governance scaffolding or process precision needed to move beyond proof-of-concept. The result: 80% of initiatives stall before generating real value. In the medium term, the challenge becomes cultural.
Organisations must rewire leadership thinking away from headcount metrics and toward outcome accountability, and building genuine human-AI collaboration takes deliberate organisational design, not just tooling. Long term, the hardest challenge is strategic coherence: the AI landscape evolves faster than most governance frameworks.
Enterprises must build the institutional muscle to self-disrupt continuously without sacrificing auditability, quality standards, or the human judgment that keeps AI honest.
What we consistently see is that organisations invest in AI capability but neglect AI governance.
Key takeaways
- The minimum conditions for AI are three: process clarity, governance architecture, and leadership alignment on what success means.
- Before any automation is introduced, an enterprise must be able to map its workflows with precision.
- AI agents need to be held accountable to outcomes and not just outputs.
- Infrastructure requirement is less about specific platforms and more about observability.
- Without traceability, you are not running an enterprise AI system, you are running an expensive chatbot.
- Enterprises that commit now will define what industry leadership looks like in the next decade.
- The short-term challenge is almost never technical, it is executional.
- Long term, the hardest challenge is strategic coherence since the AI landscape evolves faster than governance frameworks.
Enterprises accountable for decisions made by autonomous systems
Agentic AI raises the bar because it can make decisions and execute actions beyond predefined rules. Organisations need guardrails, continuous monitoring, audit trails and a designated person who stays accountable for the outcome. It is essential to have foundations set up right to avoid costly rework later, says Satish Tulsiani at Mindware.
Everything Mindware builds for partners starts with a simple question: how do you make the day easier for partners? Mindware’s marketplace answers it by provisioning software licenses automatically with vendors, so the manual back-and-forth of ordering and activation simply goes away.
Mindware’s partner portal does the same for the things partners used to call us about, invoices, backlogs, serial numbers, and a clear view of the business they do with us, by brand and by country. Behind the scenes, Mindbots automates the repetitive work, validating documents, quoting, reconciling data, generating reports.
Mindware is now bringing all of this together behind a single interface that gives partners one place to reach every application. Most exciting of all is the launch of MAGIC, the AI platform that helps partners take on complex opportunities and build proposals faster, drawing on Mindware’s product knowledge.
The thread through all of it is simple: technology only earns its place when it moves at the speed of the business it serves.
Critical success factors
The most successful automation and AI deployments I have seen shares the same starting point: strong foundations. Four foundations matter most, and they need to be solid before automation or agents go live.
#1 Data must be accessible, reliable and clearly owned. Otherwise, AI will amplify errors at scale.
#2 Business processes must be standardised and documented. A workflow that changes depending on who performs it cannot be automated effectively.
#3 Core platforms such as ERP, CRM, data analytic layers should be connected through secure, stable APIs, rather than relying on screen scraping.
#4 Mature identity and access management is non-negotiable. An AI agent should never have broader privileges than the employee it supports.
Agentic AI raises the bar because it can make decisions and execute actions beyond predefined rules. Organisations therefore need clear guardrails, continuous monitoring, audit trails and a designated person who stays accountable for the outcome.
Ultimately, successful adoption requires leadership clarity, workforce readiness and disciplined execution. Enterprises must begin with a defined business problem, measurable value, capable people and an honest assessment of readiness.
Benefits and gains
Early on, AI and automation can deliver speed, accuracy and operational relief by handling repetitive, rules-based activities such as validating and consolidating documents, easing the quoting process, reconciling data, and generating automated reports for internal and external users.
Mindware developed its own Mindbots application to address these needs, and it has become a proven success story.
As adoption matures, the value will shift from performing existing tasks faster to redesigning how work gets done. This is where Agentic AI starts to matter instead of automating a fixed path, agents handle exceptions and multi-step tasks that used to need a person chasing information across systems.
Organisations that establish strong data foundations early will be better positioned to expand automation, improve decision-making and introduce new use cases more efficiently.
Looking further ahead, AI and automation will become embedded in the enterprise operating model rather than being treated as individual technology projects. This should enable lower service costs, faster and more consistent decisions, greater responsiveness and scalable growth. I believe the greatest benefit will be an enterprise that is more intelligent, agile and resilient.
Pain points and challenges
At the outset, the obstacles are practical: poor-quality or fragmented data, disconnected legacy systems, skills gaps, and understandable resistance from people who fear being automated out of relevance.
Over the medium term, attention shifts to governance: controlling costs, monitoring agent actions, protecting data, maintaining auditability and building trust in AI-supported outcomes that may not always be predictable. Clear accountability therefore remains critical.
The harder, longer-term challenge is maintaining architectural flexibility as AI technologies evolve. It is essential to have the foundations set up right to avoid lock-in and costly rework later. In the end, organisations remain accountable for every consequential decision made or supported by an autonomous system.
Data must be accessible, reliable and owned, otherwise AI will amplify errors at scale.
Key takeaways
- The longer-term challenge is maintaining architectural flexibility as AI technologies evolve.
- It is essential to have the foundations set up right to avoid lock-in and costly rework later.
- As adoption matures, the value will shift from performing existing tasks faster to redesigning how work gets done.
- Successful adoption requires leadership clarity, workforce readiness and disciplined execution.
- Enterprises must begin with a defined business problem, measurable value, capable people and an assessment of readiness.
- Agentic AI raises the bar because it can make decisions and execute actions beyond predefined rules.
- AI and automation will become embedded in the enterprise operating model rather than being treated as individual technology projects.
Managing autonomous decisions at scale
Over the long term, enterprises must manage a new class of operational risk. Accountability, auditability, cyber resilience, data sovereignty and human override mechanisms become critical. Organisations must also avoid AI debt by preventing duplicated agents and unmanaged automation. The challenge is governing autonomy says Haider Amjed at NTT DATA.
NTT DATA, combines advisory, engineering and managed services to take enterprises from automation to governed Agentic AI at scale. Its capabilities include the Smart AI Agent Ecosystem, AI Studio, GenAI Technology Hub, intelligent automation and RPA modernisation, agent build and orchestration, and managed agent services.
NTT DATA integrates agents with enterprise platforms and hyperscalers including Microsoft Azure, AWS and Google Cloud, supported by data, cloud, network, cybersecurity and responsible-AI capabilities. The differentiator is end-to-end execution: identifying valuable use cases, redesigning workflows, integrating with existing systems, establishing guardrails, and continuously operating and optimising agents against measurable business outcomes over time.
Critical success factors
The minimum condition is not a particular AI platform; it is organisational clarity. Enterprises need a business outcome worth improving, an accountable executive owner, a process that can be understood end-to-end, and a clear definition of where human judgement must remain. The experience is that Agentic AI amplifies the quality of the operating model it enters and it can equally amplify ambiguity.
On the IT side, the essentials are trusted, accessible data; secure identity and access controls; integration into core systems through APIs or automation layers; observability and audit trails; and architecture that can enforce policy, privacy and human approval. Data does not need to be perfect, but ownership, quality thresholds and permitted usage must be explicit.
The final condition is organisational readiness before the first agent is deployed. Employees need to understand how their roles will change, leaders need measurable value and risk metrics, and governance must match the level of autonomy granted to an agent.
This is especially important across Europe, the Gulf and Africa, where data residency, regulation, language and operating environments differ. The right starting point is therefore not Where can we deploy AI? but Which outcome can we safely redesign around intelligence and automation?
Benefits and gains
In the short term, automation and Agentic AI deliver practical gains: faster cycle times, lower manual effort, more consistent service, 24/7 execution and better access to enterprise knowledge. The first value often comes from removing friction around employees rather than replacing them automating preparation, coordination, retrieval and routine decisions so people can focus on judgement and relationships.
In the medium term, the opportunity becomes more strategic. Enterprises can redesign workflows end-to-end rather than automating isolated tasks. Multiple agents can coordinate across functions such as customer service, finance, IT, supply chain and operations, while humans supervise exceptions and higher risk decisions. This creates a more responsive organisation and helps address skills constraints that are particularly visible in fast-growing markets.
Long term, Agentic AI can change the operating model itself. Processes become continuously monitored, adaptive and increasingly software-defined; services can be personalised at scale; and organisations can create new digital products, service models and revenue streams.
The biggest benefit is therefore not simply cost reduction. It is organisational capacity: the ability to make more decisions, serve more customers and respond to change without increasing complexity at the same rate. Value compounds as data, trust and learning loops mature across markets, sectors and industries.
Pain points and challenges
Short term challenges are practical: fragmented data, legacy integration, unclear use cases, security concerns and unrealistic expectations of what agents can safely do. In the medium term, the harder issues become organisational, workforce adoption, skills, process ownership, model reliability, cost control, vendor sprawl and governance across many agents.
Long term, enterprises must manage a new class of operational risk: autonomous decisions at scale. Accountability, auditability, cyber resilience, data sovereignty and human override mechanisms become critical. Organisations must also avoid AI debt by preventing duplicated agents and unmanaged automation. The challenge is not deploying agents; it is governing autonomy without slowing innovation.
Governance must match the level of autonomy granted to an agent.
Key takeaways
- NTT DATA integrates agents with enterprise platforms and hyperscalers including Microsoft Azure, AWS and Google Cloud.
- Agentic AI amplifies the quality of the operating model it enters and it can equally amplify ambiguity.
- The condition is organisational readiness before the first agent is deployed.
- Data does not need to be perfect, but ownership, quality thresholds and permitted usage must be explicit.
- The right starting point is which outcome can we safely redesign around intelligence and automation.
- The biggest benefit is not simply cost reduction; it is organisational capacity.
- Value compounds as data, trust and learning loops, mature across markets, sectors and industries.
Are your controls in place before the Agent goes live?
Identity and access management need to be mature enough to give an Agent its own identity with defined permissions, much like you would for a new employee. You need proper logging and monitoring so that every action the Agent takes can be traced says Walid Gomaa at Omnix International.
Omnix offers a broad set of technology solutions and services to help enterprises automate processes and adopt Agentic AI in a practical and controlled way. The company’s capabilities include AI and GenAI platforms, intelligent automation, low-code, no-code development, conversational AI, data and analytics, API integration, and cloud infrastructure.
Omnix helps customers integrate AI Agents with core enterprise platforms such as ERP, CRM, HR, and other business applications, enabling Agents to access data, execute workflows, and interact across multiple systems. The full services cover the full lifecycle, from use-case identification and process assessment to solution design, integration, implementation, and managed services.
The company addresses underlying requirements needed to deploy Agentic AI at enterprise scale, including data readiness, identity and access management, security, governance, monitoring, and human-in-the-loop controls.
Depending on customer requirements, solutions can be deployed across public cloud, private cloud, or on-premises environments, with appropriate consideration for data residency and regulatory requirements.
Critical success factors
Most of the automation projects we see stall for reasons that have little to do with AI itself. On the IT side, the minimum requirement is data that an Agent can access and trust. That means core systems such as ERP, CRM, and HR need to be accessible through APIs rather than locked behind screens, with reasonable data quality in the processes you want to automate.
Identity and access management also needs to be mature enough to give an Agent its own identity with defined permissions, much like you would for a new employee. You need proper logging and monitoring so that every action the Agent takes can be traced.
There also needs to be a clear decision on where the models will run, which in the UAE and Saudi Arabia often comes down to data residency and regulatory requirements.
On the business side, the first condition is having a process owner who cares about the outcome. The process should have already been documented, reasonably stable, and measured, because without a real baseline, you have nothing to measure against to demonstrate value.
Leadership also needs to agree on how much autonomy an Agent should have and where human approval is required. A basic governance policy covering acceptable use, risk, and accountability should be in place before the first Agent goes live, not after.
Benefits and gains
In the short term, the gains are practical. Repetitive tasks involving rules like matching invoices, processing onboarding requests, service desk tickets and report preparation gets done faster and with fewer errors. Staff notice it first, because they get time back for work that needs judgement.
In the medium term, the advantage becomes process-focused rather than task specific. The Agents will be able to take care of the request end to end, by pulling data from several sources, validating it against policy and escalating only those that are exceptions.
The time cycles get reduced, services stay available round the clock and the organisation can absorb growth without hiring at the same pace. For government entities in the UAE and Saudi Arabia, this ties directly to service quality targets and the push toward proactive, paperless services.
In the long term, the change is structural. Organisations that can automate their systems well end up with far better data about how they operate, which improves planning and decision-making. The roles become designing systems around supervising and managing Agents instead of doing transactional tasks.
The organisations build a skillset that can be reused, so each new process is quicker and cheaper to automate than the last and more cost effective with each cycle. This is where the competitive advantage sits.
Pain points and challenges
Initially, the biggest challenge is usually the data. Information sits in disconnected systems, much of it is unstructured, and access rights are rarely clean. There is also understandable fear among team members about what automation will do to their job roles.
In the medium term, it comes down to control: this entails monitoring an increasing number of Agents, mitigating security threats such as prompt injection, and costs that escalate unexpectedly.
Over the long term, organisations must keep pace with regulation that is still taking shape, avoid lock-in to a single platform, and reskill their people for roles that did not exist a few years ago.
Most of the automation projects we see stall for reasons that have little to do with AI itself.
Key takeaways
- Leadership needs to agree on how much autonomy an Agent should have and where human approval is required.
- A basic governance policy covering acceptable use, risk, and accountability should be in place before the first Agent goes live, not after.
- Without a baseline, you have nothing to measure against to demonstrate value.
- There needs to be a decision on where the models will run, which in UAE and Saudi Arabia comes down to data residency and regulatory requirements.
- For government entities in UAE and Saudi Arabia, the benefits tie directly into service quality targets and push toward paperless services.
Connecting intelligence with execution in AI enterprises
Automation and orchestration capabilities from the vendor help connect applications, data, infrastructure and workflows across hybrid environments. At the same time, governance, visibility and human oversight allow enterprises to adopt AI responsibly says Miles Bowker at BMC Helix.
BMC Helix provides an open, AI-powered platform for ServiceOps, bringing together IT service management, IT operations, observability, automation and Agentic AI capabilities.
HelixGPT and its growing fleet of AI Agents help organisations move from insight to action by understanding operational context, recommending next steps and automating defined workflows. The platform also supports multi- Agent approaches, where specialised Agents can work together across more complex processes.
Automation and orchestration capabilities help connect applications, data, infrastructure and workflows across hybrid environments. At the same time, governance, visibility and human oversight allow enterprises to adopt AI responsibly.
The overall approach is about connecting intelligence with execution, rather than deploying AI in isolation.
Critical success factors
The introduction of automation and Agentic AI should start with a clear business objective, rather than an AI-first technology agenda. Organisations need to identify processes where repetitive work, operational complexity, slow resolution times or fragmented decision-making are creating measurable business impact.
A defined business case, accountable stakeholders and agreed success metrics should therefore be the starting point.
From an IT perspective, organisations need a reliable foundation of connected data, visibility across applications and infrastructure, and integration with existing enterprise systems. Agentic AI is only as effective as the context it can access and the actions it is permitted to take. Strong identity, security, governance and data-quality practices therefore need to be in place before autonomous actions are scaled.
The ability to introduce automation progressively becomes important. Enterprises should start with well-understood, lower-risk workflows, validate the outcomes and then expand into more complex processes. Human oversight, clear escalation paths and auditability remain important as AI Agents move from making recommendations to acting.
The minimum condition is not simply having AI-ready technology. It is having a clear business objective, trusted data, connected systems and the governance required to automate with confidence.
Benefits and gains
In the short term, automation and Agentic AI can deliver measurable improvements in productivity, response times and employee experience. Repetitive tasks can be automated, incidents can be prioritised more intelligently, and employees can receive faster support and resolution.
This allows IT teams to spend less time on manual activities and more time on higher-value work. Vendor capabilities are designed to support this shift from reactive processes towards proactive and autonomous operations.
In the medium term, organisations can achieve consistency and scalability across workflows. AI Agents can be applied across service and operations processes, helping teams identify patterns, anticipate issues and execute defined remediation actions. This creates connected operations and reduces dependency on fragmented tools and manual handoffs.
Over the longer term, the opportunity extends beyond IT efficiency towards a more adaptive enterprise. Agentic AI can coordinate people, systems, data and automated processes, enabling organisations to respond faster to changing business requirements. The focus ultimately shifts from automating individual tasks to orchestrating complete business outcomes.
This progression can improve operational resilience, make better use of skilled talent and create a stronger foundation for continuous innovation.
Pain points and challenges
In the short term, organisations need to address data quality, integration complexity, security requirements and the identification of suitable use cases. There can also be uncertainty around where AI should be allowed to act autonomously and where human approval should remain mandatory.
In the medium term, scaling beyond individual pilots can introduce challenges around governance, change management, skills and integration across both legacy and modern environments. Organisations also need to establish clear accountability for AI-driven decisions and actions.
Over the longer term, maintaining trust, controlling Agent behaviour, managing increasing AI complexity and continuously measuring business value become critical. Strong governance, observability and human oversight therefore remain essential as the level of autonomy increases.
Over the long term, trust, Agent behaviour, AI complexity, business value, become critical.
Key takeaways
- The introduction of automation and Agentic AI should start with a business objective, rather than an AI-first technology agenda.
- The minimum condition is not simply having AI-ready technology; it is having a business objective, trusted data, connected systems and governance.
- A defined business case, accountable stakeholders and agreed success metrics should be the starting point.
- From an IT perspective, organisations need a reliable foundation of connected data, visibility across applications, integration with enterprise systems.
- Agentic AI is only as effective as the context it can access and the actions it is permitted to take.
- Identity, security, governance, data-quality practices, need to be in place before autonomous actions are scaled.
- The ability to introduce automation progressively becomes important.
- Enterprises should start with well-understood, lower-risk workflows, validate outcomes and expand into complex processes.
- Human oversight, escalation paths, auditability remain important as AI Agents move from making recommendations to acting.
