Bringg's 2026 Last-Mile Performance Outlook shows what 150 retail and logistics executives at $1 billion-plus companies plan to invest in, and expect to materialize in 2027.
Ninety-four percent of respondents expect their last-mile budget to increase between 2026 and 2027, and nearly half expect increases of 10% to 15%. The data offers a preview of what last-mile operations will look like in 2027. It also provides insight on the table-stakes investments versus those that will be differentiators for companies that want to gain an edge.
Planned last-mile budget growth between 2026 and 2027
Most executives plan to increase their last-mile budgets by 2027. Roughly half expect 10%-15% increases.
- 10% to 15% planned investment increase: 45% of executives
- Less than 10% planned investment increase: 42% of executives
- More than 25% planned investment increase: 7% of executives
- No change in planned investment: 4% of executives
- Decrease in planned investment: 2% of executives
The budget increases support another finding: 94% of executives believe last-mile delivery is one of the most strategic components of their organization’s business.
So where are the dollars behind that strategy headed?
AI, visibility, and communication lead 2027 last-mile investments
Five categories account for most of the planned increases in last-mile investment by 2027.
- Artificial intelligence (AI) for planning, routing and forecasting: 68%
- Visibility, including tracking, ETAs, alerts and analytics: 66%
- Real-time communication between customers and drivers: 50%
- Worker tools such as driver apps, scanning, and proof of delivery: 38%
- Dispatch and real-time delivery management: 37%
AI's position at the top of the investment list reflects a doubling down on what's already in place. Adoption in the two workflows AI investment targets most directly is already high: 72% in routing and 74% in reporting and visibility. Confidence follows the same pattern. Sixty-four percent of executives say they're extremely or very confident in their current AI deployments, and only 11% report low confidence.
Read together, these five investment categories point toward baseline last-mile operations in 2027 that:
- Run on AI-informed planning and routing (even more than they do today)
- Extend real-time visibility to customers and internal teams
- Keep drivers, dispatchers, and consumers connected throughout the delivery
Execution across these baselines will determine who keeps up with the competition in 2027. But these investments won't be enough on their own; how well a company adopts, integrates, and iterates on the technology throughout the year will matter just as much.
What companies plan to cut in 2027
Investment increases don't happen in a vacuum. Budgets that grow in one category typically shrink somewhere else, and the deprioritization data tells its own story about where the market will stop placing bets in 2027.
- Curbside pickup disinvestment: 39%
- Vehicle and fleet size disinvestment: 36%
- Ship-from-store disinvestment: 26%
- Carrier diversification disinvestment: 25%
- Visibility (tracking, ETAs, alerts, analytics) disinvestment: 15%
Curbside pickup was a significant strategic priority during the 2020 pandemic and had a 55% adoption rate that year. However, adoption was cut in half by 2024 and its position at the top of the 2027 deprioritization list marks a continued decline. Combined with the pullback from fleet size and ship-from-store, the pattern points to a broader move away from owned-asset investment and toward more flexible, hybrid delivery models that carry lower fixed costs.
Companies weighing new investment in any of these three categories heading into 2027 should treat that pullback as a market signal worth investigating before committing further.
AI’s position at the top of the investment list reflects a doubling down on what’s already in place.
Last-mile investments for a competitive edge
Last-mile operators have some runway to invest in the workflows the market has largely passed over to differentiate themselves.
For example, AI adoption and investment in routing, reporting, visibility is high. But the data shows other AI workflows that have low adoption and little upcoming investment:
- Exception handling: 28% adoption, 18% plan to increase investment
- Billing and invoice reconciliation: 26% adoption, 14% plan to increase investment
- Carrier selection and management: 25% adoption, 13% plan to increase investment
These three workflows drew the least planned investment of any category for 2027. Yet, they carry the highest manual burden of any last-mile operation, and the highest exposure to margin loss when something breaks. A missed exception becomes a failed delivery. An unreconciled invoice becomes margin leakage. A manual carrier assignment becomes a cost per delivery no one tracks against performance data.
Operators can gain an edge on the rest of the market in 2027 if they invest in automating these manual workflows, which will go largely untouched by the current wave of last-mile investment.
Make baseline last-mile investments work harder
More powerful and abundant AI, visibility, and real-time communication tools are expected to be table stakes in 2027, given that two-thirds of the market plans to invest in them.
Whether those investments produce any real advantages will come down to two things most last-mile budgets don't account for directly: how precisely they’re applied, and whether the data underneath the investments is unified enough to support them.
Segment consumers with table stakes technology
Visibility and communication tools perform differently depending on who they're built for. Bringg's 2026 Delivery Experience Study found real-time tracking matters to 71% of power shoppers (who order 11 or more times a month) versus 59% of regular shoppers (0-5 orders a month). Power shoppers also generate outsized revenue and they're twice as likely to leave after a single bad delivery.
Operators can appeal to power shoppers specifically with self-service delivery rescheduling enabled from the tracking page, without a support call. They can also prioritize on-time arrival and clear communication over faster delivery windows, as this segment values certainty and flexibility over speed.
Product category segmentation works the same way. Big and bulky items like furniture and appliances carry routing, handling, and installation complexity that parcel delivery doesn't. Customers have different expectations as well. Over half (62%) of power shoppers and half of regular shoppers hold big and bulky delivery to higher standards than parcel. And 44% of power shoppers cite poor communication as a major big and bulky delivery concern.
Treating big and bulky as a scaled-up version of parcel delivery cuts into the margin those categories are supposed to protect. And across both big and bulky and parcel, a visibility investment applied evenly across every customer underserves the segment responsible for the most revenue and with lowest tolerance for failure. Businesses can put their existing AI, visibility, and communication investments to even better use if they segment delivery based on shopper frequency and order type.
A last-mile investment…only performs as well as the data it’s working with.
Fix the data foundation underneath every investment
Two companies can adopt the same tool and get very different results. A last-mile investment—regardless of whether it’s rooted in AI, visibility, and communication—only performs as well as the data it's working with. Untangling a messy or siloed data layer is its own investment, separate from the technology itself. And it’s often the difference between an investment that underperforms and one that actually delivers.
On Deliver: The Last-Mile Performance podcast, retail technology executive Shweta Bhatia said siloed systems are rarely the result of bad decisions. They’re usually the natural byproduct of organizations that grow and solve problems at different points in time. Bhatia offered a few strategies unify isolated systems:
- Full system consolidation isn't a realistic goal for most large logistics organizations, since their systems were built over time to handle scale and operational complexity that a single platform can’t absorb.
- The more effective approach is smarter orchestration: Let each system do what it's good at while creating a shared, real-time layer that aligns decisions across them.
- Done well, this removes the false choice between stability and agility, so a company doesn't need to revamp working systems to achieve coordinated decision-making.
Google Cloud's Director, Global Strategic Industries, Supply Chain & Logistics Paula Natoli made a similar point. She said a team can hit 100% of its own metrics and still deliver a broken customer experience because functional KPIs don't capture what happens at the doorstep. Her argument rests on three ideas:
- A data foundation has to be in place before AI investment can produce meaningful results; it’s a prerequisite rather than a parallel workstream.
- A connected data layer allows for proactive exception management instead of reactive cleanup: a company catches delivery failures hours before they reach the customer, rather than after the customer already knows something went wrong.
- That early-detection window is where she argues the loyalty gap between leading retailers and everyone else actually gets built.
Most LSPS are not ready for AI, according to Jamie Andrade, SVP of product management at SEKO Logistics. And buying tools before fixing data makes the problem worse. Andrade described the fix:
- LSPs that find real results with AI identify internal pain points first, then find the tool. Those that start with the vendor demo consistently fail to get value.
- LSPs that build the data model correctly from the first brick find that every new tool and carrier they add feeds into a platform that already knows how to handle it.
- The LSPs that get this right build internal capabilities to adapt to each client's format, rather than expecting every client to adapt to a standard API.
Bhatia and Andrade describe the same fix from two different vantage points. Neither argues for tearing out what already exists. Both argue for building a foundation—a shared real-time layer for Bhatia, a flexible data model for Andrade—that lets different systems and clients work together without forcing everything into one format first.
Any company can buy new platforms in 2027. The most successful ones will also fix what's arguably more important: the data foundation that the AI, visibility, and communication investments (new or existing) already rely on.
Build an effective 2027 last-mile investment strategy
In 2027, baseline last-mile operations are predicted to run on AI-informed planning, extend real-time visibility to customers and internal teams, and keep drivers, dispatchers, and customers connected throughout the operation. Half or more of the market already plans to fund these categories, which means having them in place gets a company to the same starting line as most competitors, not ahead of it.
Two paths lead past that starting point. The first is to compete where competitors choose not to. Exception handling, carrier selection and management, and billing and invoice reconciliation remain the most manual, most margin-exposed workflows in the last-mile. They’re also the least funded. The market has decided to ignore these workflows despite their outsized operational impact, and that’s where efficiency gains sit unclaimed.
The second path is to hone existing tools for greater segmentation while everyone else plans to simply “use the tech” in a generalized way. A generalized approach, even with shiny new capabilities, accomplishes less than it appears to when consumers aren't segmented and the data underneath those investments sits siloed or tangled across systems. A visibility investment applied evenly across every customer, or an AI tool built on top of disconnected systems, returns a fraction of its potential value.
Acting on both principles gives last-mile operators a real path to success in 2027. They stand a better chance to win customer attention instead of settling for parity with a market throwing money at the same three categories.
FAQ
What last-mile delivery investments will enterprises make in 2027?
Retailers and 3PLs plan to direct most last-mile investment toward AI for planning, routing, and forecasting (68%), visibility tools like tracking and ETAs (66%), and real-time communication between customers and drivers (50%), according to Bringg's 2026 Last-Mile Performance Outlook.
Is AI still the top last-mile delivery investment priority for 2027?
Yes. AI ranks first among planned last-mile investment increases at 68%, ahead of visibility and communication tools. Adoption is already high in the most visible AI workflows: 72% in routing and 74% in reporting and visibility. But workflows like exception handling, carrier management, and billing reconciliation have far lower adoption and investment plans.
What last-mile investments will enterprises cut or deprioritize in 2027?
Curbside pickup is the most commonly deprioritized last-mile investment for 2027, cited by 39% of executives, followed by vehicle and fleet size (36%) and ship-from-store fulfillment (26%). The pattern points to a shift away from owned-asset models toward more flexible delivery networks.
Where is the clearest last-mile delivery investment opportunity for 2027?
The clearest opportunity sits in workflows the broader market has underfunded, rather than in matching where competitor spending already concentrates. Exception handling, carrier selection and management, and billing and invoice reconciliation carry the highest operational burden but the lowest planned investment increases, at 18%, 13%, and 14% respectively.
How much will enterprises increase last-mile budgets in 2027?
Ninety-four percent of executives expect their last-mile budget to increase between 2026 and 2027. Nearly half expect an increase between 10% and 15%, and 7% expect an increase of more than 25%.
