{"id":61973,"date":"2026-03-20T08:00:06","date_gmt":"2026-03-20T12:00:06","guid":{"rendered":"https:\/\/clarkstonconsulting.com\/?p=61973"},"modified":"2026-03-18T17:46:16","modified_gmt":"2026-03-18T21:46:16","slug":"2026-program-and-project-management-trends","status":"publish","type":"post","link":"https:\/\/clarkstonconsulting.com\/insights\/2026-program-and-project-management-trends\/","title":{"rendered":"2026 Program and Project Management Trends"},"content":{"rendered":"

\"2026<\/a><\/strong><\/h2>\n

Download the full 2026 Program and Project Management Trends Report here<\/span><\/a>. <\/strong><\/h2>\n

This free trends report outlines industry perspectives and expert advice from our team of consultants. You can view an excerpt of the report below, and if you\u2019d like to discuss any of the trends or other challenges in the program and project management space, connect with our team today<\/a>.<\/p>\n

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Key Program and Project Management Trends<\/strong><\/h2>\n

In 2026, leading PMOs will move beyond reporting to serve as enterprise enablement engines. They will connect strategy to execution,\u00a0<\/span>build readiness into delivery<\/span><\/a>\u00a0from the start, and ensure governance keeps pace with AI and agent deployments. Just as important, they will treat\u00a0benefits\u00a0realization as a core discipline, with clear ownership, defined baselines, and early indicators that show whether value is\u00a0actually being\u00a0created.\u00a0<\/span>\u00a0<\/span><\/p>\n

Clarkston’s program and project management<\/a> consultants have highlighted the top trends that businesses should consider and keep top-of-mind throughout the year:<\/p>\n

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  1. Governance and Risk Management for Agentic AI<\/li>\n
  2. Scaling Native AI Delivery Across Functional Operations<\/li>\n
  3. Value realization as the main KPI for PMOs<\/li>\n
  4. PMOs Shift to Persistent Product Teams<\/li>\n
  5. From Project-Based Change Management to Sustained Enterprise Change Capability<\/li>\n<\/ol>\n
    Trend 1:<\/span><\/span><\/strong><\/h5>\n
    Governance and Risk Management for Agentic AI<\/span><\/span><\/strong><\/h6>\n

    Agentic AI is the first wave of automation that behaves less like a tool and more like a junior\u00a0operator\u00a0planning steps,\u00a0calling systems,\u00a0and completing multi-stage work.\u00a0This\u00a0shift\u00a0\u2013 mov<\/span>ing\u00a0from people-\u00a0and technology-integrated workflows to\u00a0agentic\u00a0AI\u00a0\u2013 is happening fast,\u00a0and\u00a0it comes with a hard reality check.\u00a0<\/span>\u00a0<\/span><\/p>\n

    Gartner expects more than\u00a0<\/span>40%\u00a0of agentic AI projects<\/span><\/a>\u00a0to be canceled by the end of 2027 when teams\u00a0fail to\u00a0demonstrate\u00a0clear\u00a0value or build the right risk controls. At the same time, however,\u00a0adoption will keep accelerating.\u00a0<\/span>Reuters reporting<\/span><\/a>\u00a0on Gartner\u00a0notes that agentic AI is expected to autonomously handle 15%\u00a0of daily business decisions by 2028 and be embedded in 33%\u00a0of enterprise software by 2028. In other words,\u00a0many organizations will\u00a0continue\u00a0moving forward even while a large share\u00a0struggle\u00a0to scale responsibly.<\/span>\u00a0<\/span><\/p>\n

    For\u00a0PTM\u00a0and change leaders,\u00a0the implication is clear:\u00a0agent rollout\u00a0isn\u2019t\u00a0just\u00a0an IT release\u00a0but rather\u00a0an operating model change.\u00a0Before agents touch regulated or revenue-impacting processes,\u00a0such as pricing claims\u00a0and\u00a0returns,\u00a0trade settlement,\u00a0or\u00a0quality events, programs must\u00a0establish\u00a0human-in-the-loop decision rights,\u00a0defined ownership (RACI)\u00a0for exceptions,\u00a0audit trails,\u00a0and production monitoring. If you wait until after\u00a0deployment to add guardrails,\u00a0you\u2019ll\u00a0either throttle the agent into irrelevance or accept unmanaged risk. The risk\u00a0isn\u2019t\u00a0just\u00a0model accuracy. It extends to\u00a0process accountability:\u00a0who approved an action,\u00a0which data was used,\u00a0what exceptions occurred,\u00a0and how the organization detected and corrected failure.<\/span>\u00a0<\/span><\/p>\n

    Business reality reinforces why governance\u00a0has to\u00a0lead. Benchmarks\u00a0<\/span>cited\u00a0in Business Insider<\/span><\/a>\u00a0show agent performance can be under 25%\u00a0success on the first attempt and only around 40%\u00a0even after eight tries. That gap between a cool demo and a\u00a0<\/span>trusted operator<\/span><\/a>\u00a0is the governance workstream. The winners will treat agents like a new workforce class with training through prompt and workflow design,\u00a0supervision through controls and monitoring,\u00a0and performance management through value and quality KPIs.\u00a0Those that treat agents as simple plug-ins will quickly discover that the real complexity of the business lives in the exceptions.<\/span>\u00a0<\/span><\/p>\n

    Trend 2:<\/span><\/span><\/strong><\/h5>\n
    Scaling Native AI Delivery <\/span>Across Functional Operations<\/span><\/span><\/span><\/strong><\/h6>\n

    The AI conversation is shifting from which use cases to\u00a0pursue to\u00a0a deeper question:\u00a0<\/span>what changes when\u00a0AI becomes\u00a0embedded in the way work\u00a0actually gets\u00a0done?<\/span><\/i>\u00a0<\/span><\/p>\n

    Gartner\u00a0also\u00a0reports that\u00a0<\/span>61%\u00a0of organizations<\/span><\/a>\u00a0are evolving or rethinking their data and analytics operating model because of AI.\u00a0This\u00a0signals that the constraint has moved from model availability to enterprise readiness.\u00a0AI programs will succeed or stall based on whether teams can standardize processes, clarify decision rights, and\u00a0<\/span>build practical adoption routines<\/span><\/a>\u00a0that reflect how people actually work. That includes role-based training, adjustments to job design, and policy updates that remove ambiguity.<\/span>\u00a0<\/span><\/p>\n

    Workforce behavior shows how quickly the ground is shifting. Financial Times reporting\u00a0indicates\u00a0that\u00a0<\/span>48% of workers<\/span><\/a>\u00a0in a large survey now use AI every day, up from 31%\u00a0the prior year. Adoption is moving faster than most formal enablement and governance efforts. When that gap widens,\u00a0shadow AI becomes inevitable and outcomes grow inconsistent, which is exactly the risk transformation leaders are expected to manage.<\/span><\/p>\n

    Continue reading by downloading the full report below.<\/strong><\/em><\/span><\/p>\n

    Download the Full 2026 Program and Project Management Trends Report Here<\/a> <\/span><\/p>\n

     <\/p>\n

    That shift is why\u00a0AI-native delivery\u00a0(AI being embedded in the entire project life cycle)\u00a0is a program and change management issue,\u00a0not a technology one. Gartner\u2019s forecast that at least\u00a0<\/span>30%\u00a0of GenAI initiatives<\/span><\/a>\u00a0will be abandoned after proof of concept by the end of 2025 is the clearest warning that scaling fails when organizations\u00a0don\u2019t\u00a0address the\u00a0basics\u00a0data readiness risk controls and measurable value.<\/span>\u00a0<\/span><\/p>\n