- Published date:
- 28 January 2026
By Jeff Blaylock,
Director of Client, UK, Deepki
Commercial real estate leaders are operating in a perfect storm. Operating costs are rising, sustainability regulations are tightening, tenants are demanding better-performing spaces and investors are scrutinising carbon risk more closely than ever. At the same time, asset managers are under pressure to protect and grow value in a market where poorly performing buildings face an increasingly real risk of becoming stranded.
Against this backdrop, retrofitting is no longer a “nice to have”. It is a strategic imperative for aligning portfolios with the net-zero transition and safeguarding long-term asset value. The challenge has been how to do this at speed, at scale and with confidence.
From slow, manual processes to data-driven precision.
Traditionally, retrofit planning has relied heavily on manual engineering studies: costly, time-intensive exercises that can take months and slow decision-making to a crawl. In a fast-moving regulatory and investment environment, that lag can be the difference between acting decisively and missing the window altogether. One of our strategic clients recently highlighted that their new Deepki workflows reduced a month-long process to just five minutes. He highlighted that this allows them to get the capex information they need when asked so they don’t miss budget sign-offs - a huge win for them, and a clearer path to enabling investment decisions.
AI-driven virtual retrofits change the equation. By using digital, simulation-based assessments, commercial real estate professionals can model upgrade scenarios, optimise building performance and assess return on investment before a single contractor steps foot on site.
These AI models ingest historical energy consumption, occupancy patterns and operational data in seconds. They then simulate multiple retrofit pathways, delivering clear, actionable insights without site visits or disruption to tenants. What once took six months can now take days-allowing leaders to respond quickly to incentives, regulatory changes and market opportunities, and to direct CapEx where it will have the greatest impact.
Turning sustainability into a value driver
One of the biggest barriers to retrofit adoption has always been uncertainty. Finance teams need to know that capital invested today will deliver measurable returns tomorrow.
AI-powered retrofit modelling provides credible forecasts for energy savings, carbon reduction and operational performance under different technology or operational choices. When combined with real-time building data, these insights become even more robust, transforming retrofit planning from an engineering exercise into a financial strategy.
This is where sustainability becomes a performance lever rather than a compliance cost. Investors gain confidence in the numbers, asset managers gain a defensible basis for prioritisation, and organisations can clearly link sustainability action to value creation.
Staying ahead of regulation and tenant expectations
Across Europe, energy performance standards, carbon taxes and disclosure frameworks such as TCFD are becoming more stringent. At the same time, tenants are actively seeking buildings that align with their own sustainability commitments—an expectation that is increasingly reflected in leasing decisions and asset valuations.
AI-driven virtual retrofits help owners stay ahead by mapping low-carbon transition pathways, stress-testing assets against future regulatory scenarios and identifying the most cost-effective compliance options. They also simulate the upgrades required to maintain or achieve green-building certifications, reducing regulatory risk and future-proofing portfolios.
The result is a proactive, rather than reactive, approach—one that protects assets today while preparing them for tomorrow’s operating environment.
Efficiency gains that scale across portfolios
Pre-retrofit engineering assessments and audits account for a significant share of project costs. AI automates much of this work. Digital twins reduce the need for repeated site inspections, while automated analysis lessens reliance on specialist consultants.
This allows engineering teams to focus on validating the best scenarios rather than manually calculating every option. For large portfolios, the efficiency gains-and cost savings-scale rapidly.
Crucially, AI also uncovers opportunities that may be missed by human analysis alone, from hidden inefficiencies to smarter technology configurations that improve occupant comfort. The outcome is retrofit strategies that better reflect how buildings actually behave, increasing the likelihood that projected performance gains are realised in practice.
A whole-portfolio perspective
For institutional investors and asset managers overseeing hundreds of assets, prioritising capital has historically been a slow and complex task. AI-driven retrofit analysis enables rapid screening of entire portfolios using consistent, comparable metrics.
This makes it possible to identify where the highest-impact, highest-ROI upgrades lie, and to sequence investments year by year in line with budget constraints. Capital planning becomes more strategic, more transparent and better aligned with increasingly granular sustainability reporting requirements.
From insight to action
The commercial real estate sector is under intense pressure to deliver buildings that are efficient, resilient and sustainable. Traditional retrofit approaches are no longer fast or precise enough to keep pace.
AI-driven virtual retrofits reduce cost, accelerate planning and de-risk investment decisions—turning building performance improvement into a scalable, data-driven process. This is not about technology for technology’s sake. It is about enabling action.
As the most trusted sustainability solution for real estate, Deepki focuses on minimising risk while maximising value. Its AI-powered platform helps real estate leaders move beyond ambition to impact—using insight to drive decisions, and sustainability, to unlock performance and long-term asset value.
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