About
Vikram Grover
Enterprise technology and commercial leader based in the Greater Toronto Area. Twenty years across IT transformation, consulting, product leadership, and North American enterprise sales — currently focused on how large organizations turn enterprise AI, cloud, and platform investments into governed, measurable business capability.
What I do
I lead North America sales at LTM for Microsoft Business Applications, low-code and integration platforms — working with CIOs, CTOs, VPs of IT, transformation leaders and business sponsors across Canada and the United States on enterprise programs that touch Dynamics 365, Power Platform, Azure, and adjacent integration and data platforms.
Day to day, that means solution architecture, proposal leadership, executive advisory, and delivery oversight on programs in insurance, financial services, life sciences, manufacturing, and public sector. My work sits at the intersection of enterprise technology strategy and the commercial decisions that get it funded and executed.
Where I have been
My consulting foundation was built at PwC and Cognizant Business Consulting, where I led large digital transformation and technology programs before moving into product leadership and enterprise sales. That combination — consulting rigour, product perspective, and sales accountability — shapes how I think about enterprise technology: it is never only a technology decision. It is an operating-model decision, a commercial decision, and an adoption decision at the same time.
What I write about
This journal exists to make my working perspectives publicly available. The pieces are written for the people I speak with every week — CIOs and their leadership teams, VPs and AVPs of IT, client partners, sales directors, transformation heads, solution architects, and business sponsors — and they focus on the decisions that meaningfully change outcomes rather than the vendor cycle that happens to be running that quarter.
Recurring themes:
- Enterprise AI strategy and operating models. How organizations turn AI experiments into governed, scalable capabilities.
- Multi-model AI architecture. Designing for model choice without recreating fragmentation.
- Agentic AI and enterprise autonomy. Moving from assistants that advise to agents that act — with identity, authority and accountability designed in from the start.
- Integration architecture for enterprise AI. Why the AI estate inherits the strengths — and the weaknesses — of your underlying integration layer.
- Application rationalization and capital reallocation. Redirecting technology spend from unnecessary complexity toward modernization, resilience and AI readiness.
- Cloud modernization and platform-led transformation. Microsoft Azure, Dynamics 365, Power Platform, integration platforms.
- AI governance and responsible AI. Governance as part of the delivery workflow, not a gate before it.
- The commercial side of enterprise technology. How value is measured, priced, and defended.
Why independent perspectives
Most of what is published on enterprise AI today is either vendor content or generalized commentary written by people who have never had to defend a business case, sign a statement of work, or answer for a program in production. I write from the other side of that table.
Nothing here represents the views of any current or former employer. It is written to be useful to the person reading it — the executive trying to decide whether to fund the next wave of AI investment, or the technology leader trying to move from proof of concept to production without inheriting a governance problem in eighteen months.
Contact
I am reachable by email and on LinkedIn. If you are a CIO or IT leader working through an enterprise AI, cloud, D365, Power Platform or integration decision — or if you are a colleague in the same field with a perspective worth exchanging — get in touch. If you would like to be notified when new pieces are published, subscribe here.
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