The Orchestration Gap
Why 75% of AI Investments Still Fail to Deliver
Why it matters: Three quarters of enterprise AI investments fail to produce measurable value. New cross-sector evidence from Broadridge, Gartner, and verified case studies across energy, logistics, and financial services reveals a consistent cause. The problem is not the AI. It is the absence of an orchestration layer connecting discrete technologies into coordinated, accountable workflows. Organisations that close this gap are reporting 50% cycle-time reductions, millions in annual savings, and production gains of 10–20%.
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Why is the gap between AI spending and AI value widening?
Something peculiar is happening in enterprise technology. Spending on AI has never been higher. Adoption has never been faster. And yet, the majority of AI investments still fail to return measurable business value.
The Broadridge 2026 Digital Transformation Study, surveying more than 900 financial services technology and operations leaders globally, documents a striking acceleration: 80% of firms now report active AI use, up from 31% in 2025 [1]. Some 26% are already deploying agentic AI, with more than half of those deployments moving beyond pilot programmes into operational use [1]. One US-based asset manager reported saving approximately 350 hours per month in its KYC function alone through agentic AI [1].
Yet look beneath the headline adoption figures and a more uncomfortable picture emerges. Forty-three percent of firms believe they will need to rebuild their entire technology stack to succeed in the age of AI [1]. Sixty-five percent have no formal mandate or incentives to use AI. Thirty-seven percent cite talent shortages as a barrier to scaling agentic systems [1].
The pattern is consistent across sectors. Investment is accelerating. Value realisation is not keeping pace. According to McKinsey, while the economic potential of AI is enormous, most organisations struggle to move from pilot to production at scale. The question is: why?
What is orchestration and why does it matter?
The answer, increasingly supported by cross-sector evidence, is orchestration. Not the AI models themselves. Not the data. Not the algorithms. The layer that sits between all of them, coordinating decisions, enforcing governance, and connecting discrete technologies into workflows that actually produce outcomes.
Service Orchestration and Automation Platforms (SOAPs) have evolved from specialist IT scheduling tools into what Gartner now recognises as a distinct, critical market category [2]. Redwood Software, named a Leader in Gartner’s Magic Quadrant for SOAPs for two consecutive years, frames the challenge plainly: the most competitive enterprises are no longer differentiating on the smartness of their algorithms, but on how effectively their orchestration platforms connect insight to decision to execution [2].
Think of it this way. A hospital might have an excellent diagnostic AI, a robust electronic patient record, and an efficient scheduling system. Without orchestration, each operates in isolation. The diagnostic insight does not automatically trigger the right appointment. The appointment does not automatically update the patient record. The record does not automatically inform the discharge process. Each handoff introduces delay, error, and cost.
Orchestration eliminates those handoffs. It turns a collection of capable tools into a coordinated system. The UK’s National Cyber Security Centre has long advocated integrated, defence-in-depth approaches to technology architecture, and orchestration applies the same principle to operational AI.
The enterprises realising extraordinary AI returns are not the ones with the smartest algorithms. They are the ones with orchestration platforms that connect discrete technologies into coordinated workflows.
What does orchestration look like in practice?
Financial services: from pilots to production
The Broadridge data confirms financial services is leading the orchestration shift. Among firms deploying agentic AI, adoption is most advanced at large institutions. Nearly one third of firms managing more than $250 billion in assets report active agentic AI deployments [1]. These are production systems handling underwriting, compliance interpretation, and customer onboarding.
The critical enabler is orchestration. Autonomous agents handling KYC checks or loan decisioning only work at scale when an orchestration layer governs their behaviour, ensures compliance with frameworks such as the EU AI Act and UK GDPR, maintains audit trails, and integrates with legacy ERP systems without disruption.
Energy: process mining before automation
OMV Petrom, Europe’s largest oil and gas producer, provides a compelling case study in orchestration-first transformation. The company’s S4Strive programme consolidated 170 legal entities under a single SAP S/4HANA platform, integrating business process management frameworks, process mining analytics, and robotic process automation for financial workflows [3].
The results were substantial: a 50% reduction in invoice-to-payment cycle times, more than 225,000 labour hours saved annually, and cost reductions exceeding €8 million [3]. IBM, the implementation partner, confirmed the consolidation of 170 company codes into a single instance [4].
The critical lesson here is sequencing. OMV Petrom did not deploy RPA first and hope for the best. The organisation standardised processes through BPM frameworks, then used process mining to identify bottlenecks and deviations, and only then applied automation to the right tasks in the right order. Orchestration was the design principle, not an afterthought. This mirrors the approach recommended by the NCSC’s Cyber Assessment Framework: understand your current state before implementing controls.
Manufacturing: scheduling as the orchestration centre
BDO’s 2026 Manufacturing Industry Predictions highlight the growing role of predictive modelling and analytics tools for identifying supply chain risks, projecting stock-outs, and adjusting production schedules dynamically [5]. Early adopters report 10–20% production gains and 7–20% productivity improvements from operational AI tied to sensors, schedules, and routings [6].
Scheduling systems are emerging as orchestration centres that translate supply chain anomalies, maintenance signals, and quality alerts into coordinated production decisions. The Deloitte 2025 Smart Manufacturing Survey found that 29% of manufacturers deploy traditional AI/ML for operations, compared to 24% for GenAI, with operational AI delivering significantly higher ROI [6].
Logistics: RPA at scale requires orchestration discipline
Raben Group, a Dutch-based logistics provider managing 1.6 million square metres of warehouse space and nearly 12,000 employees, demonstrates what happens when RPA is deployed with orchestration rigour. The company has deployed more than 200 RPA automations, saving 78,815 employee workdays and over €6 million annually [7].
One telling detail: the average time for manual creation of spot customer offers was 15 minutes. After automation, it dropped to an average of 21 seconds, with over 99% sent to customers within two minutes [8]. Raben’s success correlates directly with its orchestration approach: a proprietary process management platform, a dedicated Centre of Excellence, and systematic identification of high-volume processes before deploying automation [8].
How does the UK public sector approach orchestration?
The UK Government Digital Service Roadmap 2026–2030 signals that orchestration principles are entering public sector strategy. GOV.UK One Login has been used by over 13 million people to access more than 120 government services [9]. The roadmap mandates API-first architecture across public sector organisations, with a target of 1 in 10 civil servants in digital and technology roles by 2030 [9].
For UK suppliers and technology consultancies, this represents both opportunity and obligation. The Digital Commercial Centre of Excellence projects £1.2 billion in annual savings through standardised procurement [9]. Vendors unable to meet government data residency, Cyber Essentials Plus, or ISO 27001 compliance requirements face exclusion from this growing market.
Analysis: three lessons for UK organisations
First, orchestration is not optional infrastructure. Point solutions without orchestration create silos that degrade AI value realisation and increase total cost of ownership. An organisation deploying RPA here, a predictive model there, and an AI chatbot somewhere else without connecting them is building complexity rather than capability.
Second, process standardisation must precede automation. OMV Petrom’s success was built on process mining and BPM standardisation before any RPA was deployed. Raben Group’s results correlate with systematic process identification and governance. The lesson is clear: automating a broken process produces a faster broken process.
Third, governance and orchestration are converging. As agentic AI moves into production, orchestration platforms are becoming the enforcement layer for AI governance under frameworks like the EU AI Act and the UK’s Data Use and Access Bill. They define boundaries for autonomous action, maintain audit trails, and ensure compliance. With 37% of firms citing talent gaps [1], governance cannot rely on human oversight alone. It must be embedded in the orchestration layer.
How Arkava helps enterprises close the orchestration gap
Arkava exists precisely because mid-market UK organisations face the orchestration gap most acutely. Enterprise platforms like Palantir and Workato serve organisations with six-figure technology budgets and dedicated integration teams. The mid-market, typically £50M–£500M revenue, sits in a gap: too complex for off-the-shelf tools, too resource-constrained for enterprise platforms.
Arkava bridges this gap through three interconnected services. Arkava Advisory provides strategic consulting that begins with process mapping and existing technology assessment, identifying where disconnected AI investments are leaking value before recommending solutions. This mirrors the OMV Petrom sequencing principle: understand the landscape, standardise the processes, and only then deploy automation.
Arkava Ignite delivers AI transformation assessments that evaluate an organisation’s readiness for orchestrated automation, covering data quality, process maturity, governance frameworks, and integration architecture. The assessment produces a prioritised roadmap calibrated to the organisation’s actual capabilities rather than aspirational targets.
Arkava Spark, the subscription automation platform, provides the orchestration layer itself, built on Industry-leading workflow orchestration technology and deployed within UK sovereign infrastructure. This means data remains under UK jurisdiction, meeting UK GDPR, DPA 2018, and defence-sector compliance requirements without exposing organisations to CLOUD Act risks associated with US-headquartered cloud providers.
The outcome-based pricing model aligns Arkava’s incentives with client results. Rather than billing for implementation hours regardless of outcome, Arkava ties success to measurable value realisation. This is the difference between buying technology and buying outcomes.
For organisations recognising the orchestration gap in their own operations, a practical first step is the Arkava Ignite Readiness Assessment: a structured evaluation of current AI investments, process maturity, and integration architecture that identifies the highest-value orchestration opportunities. Contact Arkava to discuss your organisation’s specific situation.
Risks and constraints
Integration complexity is real. Consolidating legacy systems under an orchestration layer is a multi-year undertaking, as OMV Petrom’s experience demonstrates. For mid-market organisations with smaller IT teams, external expertise is often essential.
Vendor lock-in risk. SOAP platforms are becoming critical infrastructure. Organisations should evaluate data portability, API openness, and exit costs before committing. UK sovereignty considerations add another dimension: where is your orchestration data stored, and under whose jurisdiction?
The talent gap applies here too. Orchestration requires people who understand both technology and business processes. The UK Tech Workforce shortage means this hybrid skillset remains scarce.
Measurement remains imperfect. Attribution is complex when isolating orchestration value from underlying technology value. Organisations should expect ROI measurement to be iterative.
What should you do next?
For boards and executives: Commission an orchestration capability assessment. The question is not “do we have enough AI?” It is “are our AI investments connected to each other and to business outcomes?”
For technical leaders: Evaluate readiness for orchestration platforms. Prioritise process mining and standardisation before deploying additional automation. Establish governance frameworks for agentic AI embedded in the orchestration layer, aligned with ICO guidance and sector-specific regulations.
For mid-market organisations: Start with your highest-value, highest-volume processes. Apply the OMV Petrom sequence: standardise first, mine for bottlenecks second, automate third. Even connecting two previously siloed tools into a coordinated workflow can deliver measurable returns within months.
Disclaimer: This article represents analysis based on publicly available information as of March 2026. It does not constitute legal, financial, or professional advice.
*If your organisation needs support connecting AI investments to measurable outcomes, Arkava helps mid-market enterprises turn technology spending into business results through sovereign orchestration and governance frameworks.*
References
[1] Broadridge Financial Solutions. “2026 Digital Transformation & Next-Gen Technology Study.” *Broadridge*, 25 February 2026.
[2] Redwood Software. “AI and Automation Trends 2026: From Efficiency to Enterprise Resilience.” *Redwood*, 28 January 2026.
[3] Ionescu, V. & Popescu, A. “Digital integration for sustainable competitiveness: the role of BPM, process mining and ERP in accounting automation.” *Applied Studies in Agribusiness and Commerce*, 18(1), 31 December 2025.
[4] IBM. “OMV prepares for sustainable growth.” *IBM Case Studies*.
[5] BDO USA. “2026 Manufacturing Industry Predictions.” *BDO Insights*, 16 March 2026.
[6] Phantasma Global. “AI and Automation in Manufacturing: Trends, Challenges & High ROI Use Cases in 2026.” *Phantasma Global*.
[7] Supply Chain Brain. “How Robotic Process Automation Helps Reduce Business Costs.” *Supply Chain Brain*, 10 September 2024.
[8] UiPath. “Automation saves Raben Group €6 million a year.” *UiPath Case Studies*.
[9] Government Digital Service. “Our roadmap for modern digital government.” *GDS Blog*, 20 January 2026.