Advice & Resources, Executive Search & Interim, Industry Insight, Technology
By Lee Clarke, Senior Business Director
– Last Updated: June 2026
Specialist interim expertise is far more critical to ERP implementation success in an AI-enabled environment, not less. AI accelerates delivery when the foundations are solid, however, when they are not, it accelerates failure at the same pace. Across every phase of ERP implementation, the variable that determines success or failure isn’t the platform or the system integrator, but the quality of the people in the room when decisions get made – and AI doesn’t change that. It sharpens it.
AI tools are being deployed across ERP programmes to automate discovery work, accelerate data cleansing, expand testing coverage, and improve post-go-live visibility. Each of those capabilities raises the ceiling on what a well-resourced team can deliver. Yet, they also raise the consequences of weak governance, poor programme structure, and under-experienced delivery resource. AI amplifies what it finds. Specialist interims determine what it finds worth amplifying.
According to McKinsey’s 2025 research, around 80% of organisations are now using generative AI in at least one business function. Fewer than 40% report any measurable financial impact at an enterprise level. In ERP delivery, that gap has a specific cause: AI tools are being deployed into programmes that lack the governance and specialist experience to use them well.
In the discovery phase, AI is compressing work that previously took weeks. Process mapping, requirements gathering, and gap analysis against the chosen platform can all be partially automated, producing faster outputs with broader coverage than traditional workshop-led approaches.
The limit is judgement:
For organisations in the early stages of planning, Broster Buchanan’s ERP interim talent practice places specialists with deep phase-one experience across all major platforms.
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AI tools are now being used in the design phase to model configuration options, simulate process flows against business requirements, and pressure-test assumptions in ways that manual design reviews rarely achieve at any speed. The outputs are faster and, in when assessed by experienced hands, more comprehensive.
The risk sits in the word “experienced.” A faster design process only helps if the right constraints are being applied. AI-assisted design can optimise efficiently for the wrong outcome if the brief used going in is poorly defined or if legacy process assumptions are carried forward unchallenged.
Specialist design and gap analysis interims bring the programme context that prevents this – and provide the independent challenge that internal teams and system integrators are often not positioned to give.
Data migration is where AI is having its most tangible impact on ERP delivery – but, by the same token, this is where the risk of over-reliance is highest. Automated data profiling, cleansing logic, and migration validation are materially reducing the manual burden on migration teams, improving coverage, and catching quality issues earlier in the cycle.
McKinsey research shows 70% of ERP rollouts fall short, with the issue typically not being the software itself but how organisations approach the change. AI addresses the detection and remediation of data quality issues faster than any previous approach. It does not, however, make governance decisions. Which data migrates, how legacy inconsistencies are resolved, and how completeness is validated under time pressure – those decisions still require experienced human judgement, and specifically the kind that comes from having managed complex migrations before.
AI-driven testing tools are expanding coverage significantly, running scripts at a scale that manual testing cycles cannot hope to match and identifying defect patterns earlier in the delivery cycle. For complex ERP implementations across finance and business transformation programmes, this represents a genuine shift in what’s achievable within a fixed testing window.
What doesn’t change is the need for someone who understands the business processes those tests are validating. Broader test coverage only reduces risk if the right things are being tested. Determining what the right things are – and making informed decisions when defects surface under go-live pressure – requires experienced testing and QA leadership, not just better tooling.
Yes – but within clear limits. AI is making a measurable difference in how training content is developed and deployed, personalising learning pathways by role, adjusting communications based on engagement signals, and supporting adoption at a scale that generic change programmes struggle to achieve. For business transformation programmes where user adoption is consistently the point at which ERP value erodes, these capabilities matter.
The limit is that AI-assisted delivery sits on top of a change management strategy. It does not replace one. Designing that strategy, building leadership alignment behind it, and owning the organisational change through go-live and beyond — those still require dedicated, experienced change management resource. Technology-assisted delivery is not a substitute for change leadership.
Post go-live, AI is improving hypercare significantly, too – flagging system anomalies faster, supporting issue triage, and helping stabilisation teams act before problems escalate. In the first weeks after deployment, this visibility is valuable and increasingly expected on well-run programmes.
The go-live moment itself is unchanged. It remains a high-pressure window where preparation either holds or it doesn’t, and where experienced people who have managed go-lives before are the difference between controlled resolution and escalating incidents. AI-generated dashboards are, of course, useful in that environment, but experienced go-live and hypercare leads are essential.
Broster Buchanan places experienced interim professionals across all six phases of ERP implementation – from discovery and design through to data migration, testing, change management, and go-live. The common pattern across the programmes that succeed is consistent: AI tools deployed by people with the delivery experience to apply them well, inside programme structures with the governance to support them.
If you’re planning an ERP implementation and want to discuss how to build the right interim team around it – including where AI capability fits and where specialist experience remains the determining factor – speak to Lee Clarke and the Broster Buchanan team.
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Does AI reduce the need for interim ERP specialists?
No. AI tools accelerate the delivery of work across every phase of an ERP programme, but they amplify whatever foundations are already in place. Strong governance, experienced programme leadership, and specialist delivery resource become more important in an AI-enabled environment, not less — because the pace and scale of failure increases alongside the pace of delivery.
Which ERP phases benefit most from AI?
Data migration and testing see the most immediate impact, with AI improving data quality detection and expanding test coverage significantly. Discovery and post-go-live hypercare are also changing rapidly. Design and change management benefit from AI-assisted tools but remain the phases most dependent on experienced human judgement.
What interim roles are most in demand on AI-enabled ERP programmes?
Demand is strongest for programme directors and delivery leads with experience across AI-integrated implementations, data migration specialists with governance experience, testing and QA leads, and change management professionals who understand technology-assisted adoption. Broster Buchanan places across all of these disciplines.
How early should interim ERP resource be brought in?
As early as discovery. The decisions made in the first phase — on scope, vendor selection, and programme structure – compound through every phase that follows. Bringing in experienced interim resource at the point of go-live preparation is common. Bringing them in at discovery is what separates programmes that run smoothly from those that recover.
Lee Clarke – Senior Business Director
Technology & Business Transformation
