The Future of Automation: From Deloitte to Gartner, What Awaits Us in 2027–2028

The future of business automation is coming into ever sharper focus. The converging forecasts of Gartner, Deloitte, McKinsey, and Forrester paint a picture in which, by 2028, agentic Artificial Intelligence will radically transform the way companies operate, compete, and create value. The most significant prediction comes from Gartner: by 2028, 33% of all enterprise software will include agentic AI capabilities, up from less than 1% in 2024. This is not an incremental change: it is a revolution that will redraw business models, organisational structures, and the skills required. In this article we analyse the most authoritative forecasts, the converging technologies that will accelerate this change, the expected impact on business models, and — most importantly — what companies can do today to prepare for the future of process automation.

Gartner's Forecasts: Agentic AI in 33% of Software by 2028

The Gartner forecast is the most widely cited and most significant data point for understanding the trajectory of intelligent automation. A 33% penetration of agentic AI in enterprise software by 2028 implies exponential growth compared to the current situation, with profound implications for every aspect of corporate technology.

What This Means in Practice

Concretely, this forecast implies that by 2028:

• ERP systems will include AI agents that autonomously manage end-to-end processes such as procurement planning, accounting reconciliation, and the order-to-cash cycle
• CRMs will integrate agents that manage the entire sales funnel — from lead qualification to offer personalisation — with human involvement only for complex negotiations
• Productivity platforms (email, documents, project management) will feature agents that anticipate user needs, automate recurring tasks, and coordinate activities across teams
• Cybersecurity systems will use AI agents for automated threat detection and response, drastically reducing reaction times
• HR platforms will employ agents for application screening, personalised onboarding, and proactive talent management

The Evolutionary Roadmap According to Gartner

Gartner identifies three phases in the adoption of agentic AI in enterprise software:

• 2025–2026: The evolved copilot phase: AI assistants become more autonomous, moving from passive suggestions to executing simple tasks with human oversight
• 2026–2027: The specialised agent phase: AI agents dedicated to specific functions (finance, HR, supply chain) emerge, capable of managing complete processes with human checkpoints
• 2027–2028: The multi-agent orchestration phase: multiple AI agents collaborate with one another to handle complex cross-functional processes, coordinated by orchestrators that optimise the entire business flow

Deloitte's Strategy for Agentic AI

Deloitte has published its strategic framework for agentic AI, positioning it as the next major technological discontinuity after cloud computing and the first wave of generative AI. The Deloitte framework is structured around four strategic dimensions that companies must address.

Dimension 1: Strategy and Vision

According to Deloitte, companies must develop a strategic vision for agentic AI that goes beyond the tactical automation of individual processes. This means rethinking the entire value chain through the lens of autonomous agents, identifying where AI autonomy can create new value — not just where it can reduce costs. Companies that limit themselves to automating existing processes will miss the true transformative potential of the technology.

Dimension 2: Technology Architecture

Deloitte recommends that companies begin today to build the enabling infrastructure for agentic AI: scalable cloud platforms, modern APIs, unified data lakes, and orchestration environments for multiple agents. Organisations that reach 2027 with fragmented legacy architectures will be unable to leverage new capabilities, potentially losing competitiveness in an irreversible way.

Dimension 3: Governance and Risk

AI agent governance is identified as the critical success or failure factor. Deloitte proposes an AI governance model that includes: an AI ethics committee at board level, operational policies for agent development and deployment, continuous performance and risk monitoring, and incident response plans specific to agentic AI scenarios.

Dimension 4: Talent and Culture

Skills transformation is perhaps the greatest challenge. Deloitte estimates that by 2028, 40% of business roles will require skills relating to interaction with AI agents. Companies must invest today in reskilling and upskilling programmes to prepare their workforce for the AI augmentation paradigm.

Technological Convergence: AI + IoT + Blockchain

The future of automation will not be driven by Artificial Intelligence alone, but by the convergence of multiple technologies that mutually reinforce one another. Three technologies in particular are converging to create radically new capabilities.

AI + IoT: The Autonomous Enterprise

The combination of AI agents and the Internet of Things enables the autonomous enterprise: factories, warehouses, and logistics chains where IoT sensors provide real-time data that AI agents process to make autonomous operational decisions. A smart factory in 2028 will have AI agents that continuously monitor sensor data on production lines, predict failures days in advance, automatically reorder materials, reallocate production in the event of disruptions, and optimise energy consumption — all without human intervention for routine operations.

AI + Blockchain: Trust and Transparency

Blockchain addresses one of the most critical problems of agentic AI: trust. In an ecosystem where AI agents from different organisations interact with one another (for example, a supplier's agent negotiating with a customer's agent), blockchain provides an immutable, verifiable record of every transaction and decision. Smart contracts on the blockchain can encode the rules of engagement between agents, ensuring that interactions take place according to agreed and verifiable terms.

The Convergent Triad in 2027–2028

The full convergence of these three technologies will lead to scenarios that are difficult to imagine today: fully autonomous end-to-end supply chains where AI agents manage procurement, production, and distribution based on real-time IoT data, with every transaction recorded on blockchain to guarantee transparency and compliance. The digital workforce of 2028 will be an ecosystem of specialised AI agents collaborating with one another and with humans in a fluid and dynamic way.

The Impact on Business Models

Agentic automation will not merely make existing business models more efficient: it will create entirely new ones. Understanding these transformations is essential for companies that want to be drivers — not victims — of change.

From Selling Products to Selling Outcomes

In a world where AI agents autonomously manage complex processes, value shifts from the product to the outcome. Software companies will no longer sell licences but guaranteed outcomes: not accounting software, but managed accounting; not a CRM, but an optimised sales pipeline. This evolved as-a-service model will be the norm by 2028 for the majority of enterprise technology providers.

The Rise of Zero-Operational-Headcount Companies

For certain types of business — particularly digital services, intermediation, and data management — it will become possible to operate with a workforce composed almost entirely of AI agents, with a small human team focused on strategic, creative, and governance functions. This does not mean the end of human work, but its evolution towards roles of oversight, strategic direction, and innovation.

Hyper-Personalisation at Scale

AI agents will make it possible to offer every individual customer a completely personalised experience — from the product to the communication, from the price to the support — without the prohibitive costs that today limit personalisation to premium segments. Hyper-personalisation at scale will become a baseline competitive requirement, not a differentiator.

How to Prepare Today: Strategic Investments

Companies that want to be ready for 2027–2028 must act now. The strategic decisions and investments made today will determine tomorrow's competitive capacity.

Investment 1: Data Infrastructure Modernisation

AI agents are only as effective as the data they can access. Investing in creating a unified, clean, API-accessible data layer is the number-one technical prerequisite. This means consolidating data silos, implementing robust data governance, migrating to cloud-native architectures, and adopting interoperability standards. The typical investment for a mid-sized company ranges between £200,000 and £500,000, spread over 12–18 months.

Investment 2: Scalable Automation Platform

Selecting and implementing today an intelligent automation platform that natively supports evolution towards agentic AI. Best-in-class platforms in 2026 already offer multi-agent orchestration capabilities in preview, ensuring a natural evolution path as the technology matures.

Investment 3: Skills and Culture

Launching structured workforce upskilling programmes focused on the skills needed to work with and supervise AI agents. The key roles to develop are: AI product manager, AI trainer, prompt engineer, AI auditor, and AI ethics officer. Investment in training should represent at least 3–5% of the annual IT budget.

Investment 4: AI Governance and Security

Defining and implementing an AI governance framework before the complexity of autonomous agents makes it an emergency necessity. This includes policies for agent development and deployment, risk assessment procedures, monitoring systems, and incident response plans. The cost of preventive investment is a fraction of the cost of an emergency response to an incident.

Investment 5: Strategic Pilot Projects

Launching 2–3 pilot projects with AI agents in selected areas of the business to build practical experience, validate the potential, and identify the challenges specific to your organisation. Pilots must be ambitious enough to generate meaningful learning, yet contained enough to manage risks. A budget of £50,000–100,000 per pilot is a reasonable investment for a mid-sized company.

The Skills of the Future: What Will Be Needed in 2027–2028

The digital workforce of 2028 will require a radically different mix of skills from what is needed today. Companies that begin developing these skills now will have a significant competitive advantage.

Emerging Technical Skills

• AI orchestration: the ability to design, configure, and manage ecosystems of multiple AI agents collaborating towards common objectives
• Advanced prompt engineering: expertise in crafting complex, context-rich instructions for AI agents, including the definition of boundaries, objectives, and escalation criteria
• AI auditing: the ability to assess the correctness, fairness, and security of AI systems, including bias analysis, compliance verification, and adversarial testing
• Data engineering for AI: skills in preparing, structuring, and managing the data that powers AI agents, with a focus on quality, governance, and privacy

Valued Cross-Functional Skills

• Critical thinking and judgement: the ability to critically evaluate AI recommendations and make decisions in ambiguous situations becomes the most valuable human skill
• Creativity and innovation: the capacity to envision new ways of using AI to create value remains an exclusively human domain
• Adaptive leadership: leading hybrid human-AI teams requires new leadership skills that balance the efficiency of automation with people engagement
• Ethics and accountability: the ability to navigate the ethical questions raised by AI becomes a core competency for managers and executives

The Challenges to Address

The path towards the autonomous enterprise is not without obstacles. Companies must be aware of the main challenges in order to address them proactively.

The Skills Gap

Demand for professionals with agentic AI skills dramatically outstrips supply. McKinsey estimates a gap of 2.5 million AI professionals in Europe by 2028. Companies that do not invest today in internal reskilling risk being unable to find the necessary skills on the open market.

The Complexity of Governance

Governing an ecosystem of autonomous AI agents is orders of magnitude more complex than governing traditional IT systems. Policies must be dynamic, monitoring systems sophisticated, and intervention capabilities instantaneous. Few organisations today have the necessary maturity.

Organisational Resistance

Digital transformation encounters resistance at every level: from employees who fear for their jobs, to middle management who see their sphere of control shrinking, to senior leadership who hesitate in the face of the scale of change. A well-structured change management strategy is indispensable.

Conclusion: The Future Is Built Today

The forecasts of Gartner, Deloitte, and the other analysts converge on a clear message: 2027–2028 will mark a turning point in business automation with the advent of agentic AI at enterprise scale. The 33% of software with agentic AI by 2028 is not a remote possibility: it is the most likely trajectory, underpinned by billion-pound investments and exponentially growing demand. Companies that begin investing today in data infrastructure, automation platforms, skills, and governance will be best placed to seize these opportunities. Those that wait risk being left with a competitive gap that is difficult to close. If you want to build your AI automation strategy for 2027–2028, contact us for a strategic session with our digital transformation experts.