How the next wave of AI growth will be driven by services, not software

Artificial intelligence has entered a new phase. Foundational capabilities like generation, prediction, summarisation, and classification are widely available across platforms, clouds, and enterprise stacks. Every major software provider now embeds AI across their products. Every enterprise is exploring proofs of concept and pilots. Every team is experimenting with new workflows.

But the outcomes don’t match the enthusiasm. Deloitte’s latest research shows most organisations take two to four years to see satisfactory ROI from AI initiatives. Organisations are discovering the challenge is not acquiring AI, but operationalising it. This is why the industry needs to move the conversation away from AI’s potential to proof-of-performance. 

Productivity gains such as cost reductions, accuracy improvements, or risk controls, hinges on whether organisations can embed intelligence into business processes, help people work differently, and operate AI systems responsibly at scale. All of that sits squarely in the services layer.

Software provides the intelligence, but services convert that intelligence into sustained business performance. That’s why leaders like Accenture are reorganising around unified reinvention services – they are becoming the central mechanism through which AI actually reaches the enterprise. 

Why the Centre of Gravity Is Moving Toward Services  

The gap between ambition and operational readiness is widening due to three structural dynamics that are reshaping how organisations approach their AI investments.

• Implementation Complexity Remains a Challenge 

Buying an AI-powered platform or selecting a model is only the beginning. Real deployment requires data preparation, systems integration, workflow redesign, monitoring, retraining, and compliance alignment. These layers determine whether a system is reliable, auditable, and efficient – software can’t (yet)  redesign these interlocking systems on its own.
This is where services create value. They enable translation of technology into architecture, integration into existing environments, and alignment with organisational realities. Services teams create value precisely because they orchestrate this complexity of work integration, process re-architecture, and governance design with judgment and contextual understanding

• Organisational Adoption Determines Success 

Adoption hinges on cultural readiness, training depth, and process clarity. However, AI shifts role boundaries and decision-making patterns, creating new responsibilities around oversight and governance. 
Proximity to real workflows enables service teams to understand exceptions, variations, and contextual nuance that determine whether an AI system can evolve from pilot to production. They can guide organisations through these human dimensions of transformation, building the trust and competence that make AI systems feel like natural extensions of existing work.

• Buyers Expect Outcomes Rather Than Functionality 

A consistent theme across the services world is the rise of outcome-based engagements. Organisations increasingly evaluate partners on their ability to deliver measurable improvements: faster cycle times, lower cost of operations, higher accuracy, and reduced risk.  

TSIA research highlights how renewable, value-based models will outpace traditional project revenue streams. Customers want partners who will stand behind results and commit to continuous improvement rather than simply delivering a one-time implementation and moving on. 

Services as the New Platform Layer 

In every major technology wave, the bottleneck eventually shifts from invention to implementation. AI accelerates that shift by compressing the distance between invention cycles. The pace of model innovation is far faster than the pace of organisational change. 

What’s emerging is not “services until the software matures,” but services as a foundational and persistent layer of intelligence and operational leverage. Modern services teams provide intelligence and implementation as a service, deeply embedded within business processes and supported by continuous optimisation. 

The data generated through service engagements, too, becomes a strategic asset. When services organisations capture and structure this data, they create a feedback loop: delivery informs models and playbooks; those, in turn, support faster and more ambitious implementations. 

The Hybrid Orchestrated Delivery Model: Human Expertise + AI Agents 

One of the biggest shifts ahead is the emergence of hybrid delivery models where human consultants and AI agents work side by side to deliver services work.  

In these environments, work gets distributed based on capability rather than category. The division of labour looks different than traditional consulting models, with several key patterns emerging: 

• AI agents perform analysis, data extraction, synthesis, pattern detection, and repetitive judgments that would consume enormous amounts of (human) time. They process vast chunks of information, identify anomalies, suggest configurations, and draft documentation. 

• Human experts handle contextual decision-making, risk evaluation, stakeholder alignment, and  judgment calls that require deep domain knowledge. 

• Orchestration systems coordinate both human and digital resources in unified workflows, ensuring work flows smoothly between human and AI contributors. 

Why Service Teams Are Central to AI-Led Growth 

Services teams hold a vantage point that product development simply cannot replicate. They work inside customer environments, observe operational bottlenecks, and understand the interplay between people, process, and technology. This positioning gives them distinct advantages that make them central to how AI strategies play out.

1. Direct Exposure to Operational Reality

Real workflows rarely look like diagrams. They include undocumented steps, legacy constraints, and embedded behaviours that shape how work actually gets done. Service teams see these nuances repeatedly across engagements, building an intuitive understanding of what works and what fails in practice. 

You can have the best API and the most sophisticated pre-trained model, but converting those capabilities into reliable processes requires hands-on expertise. Integration work represents a massive opportunity precisely because legacy systems, existing workflows, and organisational structures rarely accommodate new AI capabilities without friction. Services professionals translate between technical capabilities and business requirements to ensure that deployed systems solve actual problems. 

The visibility that services teams maintain across implementations allows them to spot patterns that would remain invisible to product teams working in isolation. They see where data quality issues consistently derail progress, where organisational resistance concentrates, where technical debt creates the most drag. 

1. Pattern Recognition Across Industries

Services teams encounter similar challenges across clients – data readiness issues, integration friction, governance gaps, or process fragmentation. Over time, these experiences translate into repeatable accelerators, templates, and frameworks that drive value. 

This accumulated knowledge represents a form of institutional intelligence that compounds with each engagement. Services organisations build libraries of proven approaches, diagnostic tools, and risk mitigation strategies, allowing them to recognise familiar problems in new contexts and apply tested solutions with confidence.
The data flowing through repeated implementations creates another layer of value, transforming institutional experience into intellectual property for further growth.

1. Operational Discipline and Accountability

Services organisations operate with rigor that comes from resource planning, milestone sequencing, dependency management, quality controls, and governance structures built over decades of complex project work. AI adoption benefits from this discipline, particularly when AI systems require monitoring, reinforcement learning, and continuous improvement.
Maintaining clear data standards, consistent practices, and structured feedback mechanisms generates the high-quality training data that AI systems need to improve. 
Accountability structures also matter. Services organisations that tie their compensation to outcomes must develop robust measurement systems, creating visibility into what’s working and what isn’t. This transparency benefits everyone – clients gain confidence in their investments, product teams receive actionable feedback, and the services organisation itself can continuously refine its delivery approach. 

Priorities for Services Leaders in the AI Age 

Future competitiveness will rely on the capabilities services leaders build over the next 12–24 months. The organisations moving fastest are focusing on interconnected priorities that build on one another.

1. Codify Repeatable AI Playbooks

Successful implementations must shorten time-to-value and guide customers through proven adoption paths. These playbooks need to capture: 

• Organisational change management approaches that reduce resistance 

• Stakeholder engagement patterns that build buy-in 

• Governance structures that maintain trust while enabling velocity 

• Training methodologies that accelerate competence development 

• Risk mitigation strategies that prevent common failure modes

1. Embed Governance Into Delivery 

The organisations scaling AI most effectively build compliance, monitoring, explainability, and human-in-the-loop structures directly into their standard delivery patterns. This requires deliberate design choices about where human oversight happens, how decisions get documented, what monitoring occurs continuously, and when systems should pause for review. 

1. Shift to Outcome-Centric Measurement

Project metrics should anchor around what clients actually care about. The shift involves moving from utilisation rates and task completion toward business impact metrics: 

• Time-to-value reductions that prove operational efficiency gains 

• Accuracy improvements that demonstrate reliability increases 

• Experience scores that reflect user satisfaction and adoption depth, and more 

This measurement approach creates the foundation for outcome-based pricing models that tie service provider success to client results.

1. Develop Hybrid Talent Profiles 

Teams need individuals who combine domain depth with AI fluency and delivery discipline. These people understand both the technology and the business context deeply enough to make sound judgment calls about where AI should be deployed, how it should be configured, and when humans should override machine recommendations. Services organisations need to cultivate these hybrid profiles through structured development programs, careful knowledge transfer from experienced practitioners, and rotation models that expose consultants to both technical and business challenges. 

1. Strengthen Partner Ecosystems

Service specialisations and co-delivery programs allow firms to scale validated expertise while maintaining quality. Vendors are emphasising this through their partner and ecosystem frameworks, recognising their own success depends on robust service ecosystems that can deploy their technology effectively. 
Services organisations that invest in these partnerships gain early access to capabilities, preferred pricing, and collaborative relationships that create competitive advantages.  

The Work of Making AI Work 

Software will continue to advance rapidly. But we need to recognise that long-term competitive advantage will be determined by who can integrate intelligence into the rhythms of their business. 

This is why 2026 is shaping up to be a turning point for services. Companies are moving past pilots and proofs of concept and into a phase defined by performance and delivery.  Services teams need to treat delivery as an engineered system that’s automated where possible, tightly orchestrated, and designed for consistency. The next wave of AI growth will reward organisations that excel in the services layer where technology meets operational nuance and where transformation becomes tangible. This is where real value lives, and where the future is being built.