Apply to Register Here

Founded in 2015, Activant is a global investment firm with $1.5 B AUM that partners with high-growth founders and teams that are transforming commerce.

Activant

We’re always looking to learn about new perspectives, new research, and new ideas.

Get in touch

Disclaimers & Terms of Use

Privacy Policy

New York

334 Brannan St, 
San Francisco, 
CA 94107 USA

San Francisco

Berlin

17 Jamieson St
Gardens
Cape Town, 8001
South Africa

Cape Town

17 Old Kings Highway South, 
Darien, 
CT 06820 USA

Darien

Harish is providing the Amazon Prime fulfillment experience to everyone else through a combination of AI and an asset-light network of warehouses across the United States.

Alex is automating workflows for the world’s largest enterprises by using process mining to build the connective tissue for businesses.

Mayan is delivering Autonomous IT to the largest companies in the world through systems of intelligence.

Carsten built one of the largest commerce platforms in the world, which powers enterprise merchants and retailers across the globe and now serves as the SAP commerce engine.

Soups is stopping financial crime in its tracks by building the first end-to-end platform for fraud, risk, and compliance.

Adam is enabling restaurant owners to take back their customer relationships by digitizing their entire online and offline operations in a single platform.

Activant is a research-led growth firm that invests in relentless founders

Read past issues

Monthly Newsletter

Browse Research Library

Research

Home

Since 2015, we have invested in category defining companies during their most critical phases of growth.

Our portfolio

Companies

Activant is a research-led global investment firm that partners with high-growth companies.

We partner with founders who have won their initial battles and are ready for the next challenge.

About

A team built to support and advise yours.

Latest Research

Founders who aspire to build something bigger than themselves chose to partner with us at their most critical point.

Team

Rebecca grew up in Durban, South Africa and later studied at the University of Cape Town, where she found her passion for the world of finance. Rebecca works within Activant’s research team, contributing to its rich knowledge of disruptive markets.

"Activant celebrates continuous learning and collaboration, which really sets it apart from many organisations. The opportunity to work alongside individuals with deep-rooted investment experience, as well as founders, is something that really excites me."

Oceanrock is a provider of high-quality research, analysis and decision-making support to the UK investment community: private equity funds, family offices, hedge funds, asset managers, M&A boutiques and corporates

Fleet electrification was supposed to be inevitable. Governments across the world set aggressive targets to decarbonize freight, starting with trucks. California’s 

 rule mandates that every new truck sold after 2036 be 

 CO₂ reduction for new vans by 2035 and 

Both efforts were built on the logic that 

climate urgency requires intervention because markets move too slowly

 of U.S. emissions, and freight trucks are the fastest-growing 

 In the EU, trucks and buses make up just 2% of total vehicles on the roads, yet they account for 

 But where Europe doubled down on state-led mandates, the U.S. has moved back toward a market-led experiment.

President Trump’s new administration has already revoked California’s waiver authority, ending its ability to enforce stricter standards than the federal baseline, and frozen unspent EV infrastructure funds. Environmental Protection Agency (EPA) rules requiring roughly 

 of new truck sales to be electric by 2032 are being 

At first glance, this is a simple political reversal, but the reality is that 

electrification was never just a regulatory problem - it’s an infrastructure problem.

 No matter who’s in office, that can’t be legislated away. To achieve their electrification goals, governments should focus more on reducing the actual barriers to fleet electrification, such as infrastructure challenges and high upfront costs. Without this reprioritization, large scale fleet electrification will remain out of reach for most countries.

But it’s not all doom and gloom. Developments in renewable energy infrastructure have made it not only cheaper to deploy than fossil fuel alternatives but also faster to scale. When paired with battery energy storage systems (BESS), renewables can smooth the “duck curve” - stabilizing supply and enabling continuous, cost-efficient power delivery. Applied to battery electric vehicle (BEV) fleets, especially in last-mile logistics where range is no longer a constraint, this integration could radically reshape delivery economics. 

We see a significant opportunity for investors willing to underwrite the infrastructure deals to enable broad-based fleet electrification.

Europe’s Mandates vs. America’s Markets

Europe’s approach is pure industrial policy; set the direction, force the market, and backfill the costs with subsidies and carbon penalties. The EU’s aggressive mandates work because the bloc treats decarbonization as a collective obligation, even if it means higher short-term costs or uneven national readiness. The U.S., by contrast, is letting markets decide. The argument is that if electrification makes operational sense - on cost, uptime, and total cost of ownership - fleets will transition voluntarily.

So far, Europe’s model has delivered clearer direction but limited scale. 

 EU truck makers are largely on track to meet the 2025 CO₂ target of a 15% reduction from 2019 levels, and the bloc’s new 

 initiative is knitting together cross-border charging 

 of new truck sales in Europe were electric, an order of magnitude below the 38% target set for 

In the U.S., adoption is more fragmented. Federal incentives under the Inflation Reduction Act pushed battery trucks closer to cost parity in several classes. The law allowed commercial EVs to receive a tax credit for up to 

 of the sales price, up to $7,500 for vehicles under 14,000 pounds and up to $40,000 for all other 

 By 2024, U.S. electric truck sales surpassed 

, exceeding the total sold between 2015 and 

 medium- and heavy-duty EVs were deployed in 

 With the passing of the One Big Beautiful Bill Act (OBBBA) in July 2025, many of the incentives of the Inflation Reduction Act were curtailed. Without stable regulation, national uptake remains inconsistent.

The stakes of these approaches are not just climate outcomes, but cross-border trade competitiveness, innovation pathways and political durability. 

The elephant in the room is that both strategies are being met with the same set of constraints, the physics of energy, mass, and infrastructure. Without solving for these, fleet electrification will never fully evolve.

The Bottlenecks No One Wants To Talk About

Full fleet electrification remains a heavy lift, constrained by capital costs, inadequate infrastructure, and change-management hurdles that are slowing down fleet electrification.

The availability of public charging infrastructure is still developing in many regions, making long-distance travel and convenient charging a challenge in some areas. In Europe, for example, McKinsey estimates that, by 2030, more than 

 public and private charge points will be required across the continent for medium- and heavy-duty trucks, up from roughly 10,000 in 

 The progress towards achieving these goals is bleak. Building out robust networks of chargers will require approximately 

 of capital investment until 2040. Of this, €7 billion of investments are needed until 2030, less than a quarter of which has been publicly committed 

 Analysis by Atlas Public Policy suggests that the U.S. will need to commit between 

 in charging infrastructure investments this decade to support an acceleration in electric truck 

 Charging electric vehicles requires large amounts of power. To charge more than one heavy-duty truck at a time, and get them all back on the road quickly, a charging station may need to pump out 20MW of power or more. This is the equivalent of powering 

 For Europe to create the required charging infrastructure to achieve its goals, it would consume 

 of electricity annually by 2030, roughly 0.5 percent of Europe’s total electricity 

 Gaining access to all this energy will often mean securing additional capacity on an already congested grid. Global annual investment in power transmission alone will need to more than double, from about 

 in 2023 to as high as $300 billion in the mid-2030s, if the world is to meet rising demand for power and emissions reduction 

 For electric trucks to reach mass adoption, the total cost of owning an electric truck must be able to compete with the cost of owning a traditional diesel truck. In Europe, the cost of battery electric vehicle (BEV) heavy-duty trucks are 

 that of Internal-combustion-engine (ICE) heavy-duty 

 The main expense for a BEV truck is the battery, which accounts for 

 This price discrepancy is highlighted by the exhibit below.

Having said this, battery costs are continually coming down. From 2010, when the Nissan Leaf was first released, to 2023, battery pack costs fell by 

 Should this trend continue, price parity may not be too far off. Chinese fleet operators now report that their electric freight trucks cost 

 tariffs on imported automobiles and auto parts that were introduced by the Trump administration in April 

 This could jeopardize whether large-scale fleet electrification even happens in the U.S. at all, at least for the short-term.

Uncertainty over range, reliability, and workload keeps many operators on the sidelines. Long-haul BEVs typically have a limited driving range and require recharging, which can take longer than refueling an ICE vehicle. For example, Volvo recently announced the launch of its battery-electric long-haul truck with a 

 range, compared with more than 1000km for a conventional diesel truck with standard fuel 

 This has created the stigma that ICE vehicles may be better suited for long-haul functions until BEV technology is improved. Currently the strongest use case for BEVs is in last-mile delivery operations. That’s because most light commercial delivery vehicles, specifically those in Classes 2B and 3, rarely drive more than 200 miles in a single day. A vehicle with this range can cover between 

 of all the miles these trucks typically travel, meaning it can handle nearly every delivery route without needing mid-day charging or a larger, more expensive 

The gap between current commitments and long-term requirements underscores the importance of continued investment and coordination. With accelerated rollout of infrastructure and enabling technologies, government efforts to electrify fleets can align more closely with policy ambitions and deliver faster, more efficient outcomes. Solving for the required infrastructure rollout will need participation from private investors. This is an attractive opportunity for private infrastructure funds, who see fleet electrification not just as profitable, but also as a compliant, reportable, and marketable ESG investment. It offers investors stable return potential while meeting sustainability goals.

Fleet electrification will happen, but not on the timelines that governments have set into motion.

Europe's mandate-driven model has created policy certainty while failing to deliver adoption at scale. This was the result of imposing strict adoption targets without first ensuring the supporting ecosystem, such as charging infrastructure, grid capacity, and cost-competitive EV availability, was ready. The U.S. is taking the opposite bet, letting markets lead, but that's producing fragmented progress without the infrastructure coordination needed to make electrification work beyond niche use cases. We are more bullish on fleet electrification in Europe, given that tariffs will likely dramatically slow BEV adoption in the U.S.

Despite today’s bottlenecks, there remains a compelling opportunity for investors in the next phase of fleet electrification. These private investors could be the long-term winners, as early investors can secure strong returns through long-term contracts, demand growth, and first-mover advantages in a market with high barriers to entry and government support. We believe securing financing for these large-scale infrastructure projects will define the next decade of climate investment.

The information contained herein is provided for informational purposes only and should not be construed as investment advice. The opinions, views, forecasts, performance, estimates, etc. expressed herein are subject to change without notice. Certain statements contained herein reflect the subjective views and opinions of Activant. Past performance is not indicative of future results. No representation is made that any investment will or is likely to achieve its objectives. All investments involve risk and may result in loss. This newsletter does not constitute an offer to sell or a solicitation of an offer to buy any security. Activant does not provide tax or legal advice and you are encouraged to seek the advice of a tax or legal professional regarding your individual circumstances.

This content may not under any circumstances be relied upon when making a decision to invest in any fund or investment, including those managed by Activant. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Activant. While taken from sources believed to be reliable, Activant has not independently verified such information and makes no representations about the current or enduring accuracy of the information or its appropriateness for a given situation.

Activant does not solicit or make its services available to the public. The content provided herein may include information regarding past and/or present portfolio companies or investments managed by Activant, its affiliates and/or personnel. References to specific companies are for illustrative purposes only and do not necessarily reflect Activant investments. It should not be assumed that investments made in the future will have similar characteristics. Please see “full list of investments” at https://activantcapital.com/companies/ for a full list of investments. Any portfolio companies discussed herein should not be assumed to have been profitable. Certain information herein constitutes “forward-looking statements.” All forward-looking statements represent only the intent and belief of Activant as of the date such statements were made. None of Activant or any of its affiliates (i) assumes any responsibility for the accuracy and completeness of any forward-looking statements or (ii) undertakes any obligation to disseminate any updates or revisions to any forward-looking statement contained herein to reflect any change in their expectation with regard thereto or any change in events, conditions or circumstances on which any such statement is based. Due to various risks and uncertainties, actual events or results may differ materially from those reflected or contemplated in such forward-looking statements.

The Fleet Electrification Experiment Playing Out in Real Time

Ideology vs. Infrastructure Reality

Artificial intelligence won’t build your house tomorrow, but it may already be designing it, estimating the budget and optimizing your jobsite. Long perceived as a digital laggard, the construction industry is slowly shifting gears. Traditionally reactive and slow to adopt new technologies, the industry is now increasingly exploring AI not just as a tool, but as a transformative collaborator.

AI has the potential to fundamentally shift how the sector operates but the path remains long, fragmented, and full of resistance. In RICS’s AI in Construction 2025 survey, 

 of respondents said their organisations don’t use AI, 34% are only in early pilots, and fewer than 12% regularly use it in a specific 

 Still, the tide is turning: the report found that AI has emerged as the leading construction technology for increased investment, with 

 of respondents planning to allocate more funds to it than the previous 

The construction industry is cautiously optimistic about AI.

 While there appears to be excitement around productivity and safety gains, adoption is slowed by legacy systems, fragmented workflows, and a lack of AI literacy among field teams. Construction’s slow digitization leaves data unstructured, keeping processes manual and AI underutilized. This creates a classic paradox: AI depends on digitization to work, yet digitization often stalls without clear AI-driven value. Risk aversion deepens the gap as contractors are hesitant to adopt AI tools that aren’t nearly flawless, especially when errors could lead to catastrophic outcomes like structural failures.

As part of our ongoing research within the construction space, we’ve mapped out where AI is taking hold and where gaps persist. Construction Technology (ConTech) startups, in particular, offer an easier and simpler route for construction players to begin implementing AI solutions. While adoption is far from uniform, we're starting to see meaningful traction in specific functions where efficiency gains are undeniable.

Laying The Groundwork: From CAD to Cognitive Agents

AI-powered systems for construction have been evolving over the last two decades. The roots trace back to the 1960s, when Computer-Aided Design (CAD) first made it possible to digitally draft and optimize construction plans. In the following decades, Computer-Aided Manufacturing (CAM) and Computer Numerical Control (CNC) machines began streamlining the production of building components.

By the early 2000s, machine learning emerged as a new frontier. Initially, AI's role was largely confined to predictive analytics, primarily applied to asset maintenance and project scheduling. Construction firms began leveraging machine learning models to forecast equipment failures, identify cost overruns, and optimize workforce deployment. These early use cases were often siloed, but they laid the groundwork for a broader shift in mindset across the industry.

A turning point came in the 2010s, as Building Information Modeling (BIM) became more data-rich and accessible computing power unlocked new AI capabilities. While BIM isn’t an AI technology, it provided the structured data needed to fuel machine learning models. Robotic automation then began appearing on construction sites: 

 developed systems for robotic bricklaying, proof that AI could now physically shape the built environment.

In the late 2010s and early 2020s, the rise of computer vision and wearable tech enabled proactive safety measures. Construction companies could now use AI to predict and prevent hazards in real time, marking a shift from reactive to predictive safety cultures and helping firms mitigate one of the most persistent risks in construction.

The timeline below highlights the various technological changes that we believe have helped pave the way for the introduction of AI within construction.

Today, AI supports every phase of construction, from generative design that optimizes cost and efficiency, to bidding platforms that streamline procurement, and digital twins that simulate projects before breaking ground. Even 3D printing is evolving with AI-guided modular and nuclear components. Experts see the greatest impact in pre-construction, where design, estimating, and bidding can be largely automated.

Despite this progress, widespread adoption remains uneven. 

Many firms still struggle with outdated systems, fragmented data environments, and a workforce in need of digital upskilling. 

The opportunities are vast, but the ability to fully implement AI solutions is out of reach for many small and mid-sized players in the construction space.

Experts describe construction as a “slow-moving business," where technology trends can take 15 to 20 years to take hold. Despite its potential, AI adoption in construction remains limited. A 2025 MIT Sloan study revealed that 

 of construction leaders reported their companies are not yet using AI or predictive analytics for planning or 

The chart below highlights construction’s slow AI adoption, showing how it lags well behind sectors like manufacturing, information, and healthcare. With one of the lowest shares of high-intensity AI use, the industry’s gap highlights both the urgency and the opportunity to accelerate adoption.

This paints a picture of an industry that is eager to embrace the potential of AI but is still grappling with significant challenges. The rise of ConTech startups is helping accelerate experimentation and progress, but integration into legacy systems remains complex. In our recent report, 

, we mapped the ConTech space and identified a growing wave of players with an AI-focused 

 We believe that these startups are likely to become the catalysts for meaningful AI implementation within the industry.

The Architecture of Change: Construction’s AI Shift

The construction industry is only just beginning to embrace AI in meaningful ways, but the foundation is being laid for far broader and deeper integration. Change is happening gradually­—often in isolated pilot programs or within forward-thinking firms—but as technologies mature, costs decrease, and the pressure to innovate grows, we expect adoption to accelerate. AI enables predictive planning, real-time site monitoring, and automation of repetitive tasks. These capabilities can help reduce delays, improve resource allocation, and enhance safety, potentially boosting construction productivity by up to 

We see four major shifts shaping construction’s AI transformation:

AI is shifting from a background utility into an active participant in construction workflows. What used to involve a lengthy chain of communication and multiple tools can now be managed by a single AI agent, reducing both time and error. These agents are increasingly capable of handling complex sequences of tasks. Communication between clients, contractors, and suppliers is speeding up, with some adopters reporting efficiency gains of as much as 

 of construction tasks, this doesn’t necessarily translate to job 

 Instead, it’s likely to automate what many consider the "second job" of project managers and superintendents: scheduling, reporting, compliance, and other repetitive administrative functions.

By offloading routine work, AI allows construction professionals to focus on high-value responsibilities like decision-making, risk mitigation, and stakeholder coordination. Job descriptions will evolve to reflect the orchestration of both human teams and AI agents.

The modern construction tech stack is notoriously fragmented. Teams often use one platform for scheduling, another for design, and still others for compliance and safety tracking. Autodesk found that 

 of construction teams are manually transferring data between apps that don’t 

 This fragmentation introduces inefficiencies and silos but AI has the potential to unify these scattered systems. A well-designed AI assistant could eliminate the need to juggle multiple platforms, creating streamlined, outcome-focused workflows that reduce confusion and error.

4. The New Contract: Trust and Transparency

As AI adoption on construction sites grows, so do questions about data ownership and transparency. To earn the trust of users and stakeholders, the industry must implement systems akin to "data passports". These permissions-based frameworks define who can access, use, and audit the data captured from drones, sensors, BIM models, and wearables.

AI in construction remains nascent, but its influence is now visible across nearly every part of the value chain. While adoption is far from uniform, we're starting to see meaningful traction in specific functions where efficiency gains are undeniable.

Mapping The Frontlines of AI Disruption in Construction

AI is rapidly being woven into the core of how work gets done within construction. We see six areas where disruption is underway across the value chain, led most strongly by Estimating & Bidding, Design & Engineering and On-site Safety, Operations & Risk Management.

 AI is reshaping building design by improving accuracy, optimizing layouts, and ensuring compliance from the start. Zaha Hadid Architects report that AI tools have 

 Generative tools allow architects to test thousands of options based on cost, sustainability, and codes, while AI-enhanced BIM simulates conditions, detects clashes, and automates routine tasks.

 enables developers, architects, and contractors to instantly generate optimized site plans, considering zoning, cost, and yield constraints.

, a "generative copilot" for architects, uses AI graph technology, and advanced algorithms to create floor plans, massing layouts, and performance feedback.

 provides an AI-powered, no-code platform that transforms the way civil engineers and construction professionals interact with project documentation.

 Construction estimations and bidding (“takeoff”) have traditionally been manual and time-consuming. After architects and developers create a design, general contractors prepare bids from blueprints and then repeat the process with subcontractors. AI-powered software now automates material estimation with greater precision, speeding up workflows by up to 

 automates construction plan takeoffs, completing up to 80% of the process in seconds, rather than hours.

 transforms manual, spreadsheet-heavy estimating into fast, data-driven workflows, delivering accurate cost estimates and market benchmarks in minutes.

 uses generative tools to automate electrical takeoffs from blueprints, cutting estimator time by up to 70% while improving accuracy.

On-Site Safety, Operations & Risk Management:

 AI is improving construction safety by identifying hazards, enforcing protocols, and reducing accidents in real time. According to Autodesk and Deloitte, enhanced data utilization has already boosted on-site safety conditions for 

 Tools like computer vision check PPE and unsafe behaviors, drones track progress against BIM models, and AI surveillance provides 24/7 site monitoring.

 uses smart wearables, real-time location tracking, and predictive analytics to create a digital twin of the construction workforce. This improves site safety, productivity, and compliance.

 is an AI-powered risk-management platform for the construction industry that helps general and specialty contractors, insurers, and sureties rapidly review contracts, specifications, and other project documents to uncover hidden risks.

 uses AI and sensors to detect risks like missing PPE, falls, or dangerous zones, alerting teams before accidents happen.

Josh Levy
CEO and co-founder, Document Crunch

We are generally less interested in AI tools within project planning and management, sustainability and energy efficiency and construction automation, but there are still players within these segments that are worth noting.

 AI is improving construction planning by enabling smarter scheduling, predictive analytics, and proactive risk management. A 2024 study published in the Journal of Construction Engineering and Management found that the use of AI in construction planning can reduce project delays by up to 

 By factoring in weather, supply chains, and labor data, it helps prevent delays and budget overruns. Even basic AI-enhanced management tools can be game-changing, especially for small and mid-sized contractors.

 uses computer vision and BIM integration to provide fully automated, real-time construction progress tracking. This enables teams to deliver projects up to 11% ahead of schedule and reduces billing friction by as much as 21%.

 leverages a repository of over 750,000 historical project schedules to predict construction timelines and risks.

 AI is helping the construction industry reduce its environmental impact by optimizing materials, energy systems, and carbon assessments. This technology's speed and efficiency make difficult tasks, such as connecting renewable energy sources to the electric grid, much more manageable. This is a crucial development, as according to a 2024 UN report, the global building and construction sector is responsible for 

 of real estate sector emissions, pushing the construction industry to adopt more sustainable 

 This shift is driving demand for environmental disclosures like Environmental Product Declarations (EPDs) and the summaries of a Life Cycle Analysis (LCA), which includes a verified assessment of a building material’s environmental impact.

, a New York-based platform, automates the creation of real-time LCAs and EPDs for building material manufacturers.

 AI-driven automation is redefining how buildings are physically constructed, with machines increasingly taking on repetitive or hazardous tasks. These technologies cut labor costs and timelines, and unlock new levels of consistency and design complexity that are difficult to achieve with traditional methods.

 enables autonomous excavation, grading, and trenching, reducing labor bottlenecks while increasing speed and precision.

 acts as a digital assistant for construction teams, using AI-powered SMS, QR codes, and smart access to automate daily field processes.

Despite the benefits, several obstacles continue to impede widespread AI adoption in construction. The 

industry’s inherent resistance to change remains one of the main barriers

. Construction has traditionally been slow to adopt new technologies, often due to concerns about cost, complexity, and potential disruption to established workflows. Many construction firms, particularly smaller ones, may lack the resources or expertise to implement AI effectively.

 itself presents another challenge. Roughly 

 of construction firms cite lack of training and technical integration as major adoption 

 The industry must address this digital skills gap by providing comprehensive training that encourages collaboration with AI tools rather than fear of replacement. 

 adds another layer of complexity, with highly technical professionals working alongside field crews, making intuitive, user-friendly interfaces essential for adoption across the job site.

 AI solutions is another obstacle, particularly for small and medium-sized construction firms. AI systems require substantial upfront investment in software, hardware, and training. The combination of high implementation costs and complex ROI justification processes, particularly due to the need to "sell software twice" (to both contractors and project owners), complicates adoption. Given that construction is typically a low-margin business with an average profit margin of 

, players are hesitant to fund experimentation, preferring proven technologies over untested 

 limitations compound these challenges. Construction sites, especially in remote locations, may lack the necessary connectivity and hardware to support AI solutions effectively. Reliable internet access, robust data management systems, and sufficient computing power are required to make the most of AI tools and construction companies will need to invest in upgrading their infrastructure to fully leverage them. This also impacts vendors, as inconsistent IT hardware setups across project teams pose significant deployment hurdles.

To overcome these challenges, a phased approach to AI integration offers a practical way forward. Rather than attempting full-scale implementation, firms can begin with smaller, more manageable AI projects targeting specific pain points, such as improving safety monitoring or automating routine tasks. This approach allows for a gradual learning curve and helps build internal expertise and confidence in AI technologies.

Collaborating with ConTech partners experienced in AI can further accelerate adoption, providing access to shared resources and training.

Contractors face a pivotal moment: the opportunity to leapfrog outdated digital tools through AI, while contending with the industry’s deep-rooted traditions that make modernization a major challenge.

AI adoption in construction is still in its infancy, with many firms navigating legacy systems, fragmented tools, and cultural resistance. We’re seeing fragmented early-stage implementation where AI is the hammer, and everything's the nail. There are also many point solutions addressing isolated issues such as codes, compliance, permitting and take-off; and industry leaders are beginning to invest in AI not as a trend, but as a practical solution to long-standing challenges in efficiency, safety, and sustainability.

The transformation may not be uniform or rapid, but the direction is clear. AI is set to redefine how buildings are designed, planned, and built. We believe that the firms that choose to experiment, invest, and integrate AI technologies today will not only gain operational advantages, but also position themselves as leaders in the next phase of construction.

AI's Role in Construction

The Blueprint for a Smarter Future

Google gives us an impeccably organized view of the entire internet. Type a query, and the most relevant page appears instantly. Inside a company, however, search often degrades into: “Which folder did I bury that in?” It’s a familiar headache. Now, a new wave of AI-driven search tools is bringing Google-like relevance and speed to our internal knowledge stores.

As AI becomes central to knowledge work, enterprise search is emerging as a critical foundation. Knowledge workers depend on vast unstructured data–across emails, documents, and chats–that traditional search can't handle. Large language models (LLMs) enable smarter, context-aware discovery, but rely on enterprise search to access and retrieve relevant internal information. Without it, even the best AI operates in a vacuum, disconnected from the knowledge that drives decisions. Enterprise search has expanded beyond internal file systems to include custom applications, infrastructure, and third-party tools. 

This new era of information retrieval brings deeper intelligence, reasoning, memory, and agentic research capabilities, enabling smarter, more contextual discovery across a broader data landscape.

At Activant, we believe that enterprise search could potentially be a game-changer. In line with our work on 

, we hold a strong conviction that if all work begins at the enterprise search platform, which centralizes data and systems, it has the potential to become the AI-era 

 of employees dissatisfied with internal search functions, the need for enterprise search tools that actually deliver is 

 As companies adopt more SaaS tools (Slack, Notion, Salesforce, Asana, Google Workspace, etc.), information becomes increasingly siloed. As a result, employees spend an average of 

 a day, or 9.3 hours per week, searching for and gathering 

 That’s nearly 25% of 40-hour work week lost to inefficiency!

Additionally, horizontal enterprise search solutions have struggled to differentiate meaningfully and often deliver similar generic search experiences across industries and roles. This has caused several early players to rethink their approach. 

, for example, publicly scrapped its initial AI search product due to a lack of real traction or unique 

, once positioned as an enterprise search engine, has pivoted toward becoming more of an AI workflow and agent builder, with search as a foundational layer rather than the core product. These shifts reflect a broader realization:

 search alone isn’t enough. Successful tools must deliver role-specific utility, context-awareness, and actionability to stand out.

Office

Hometown

Residence

Rebecca
Rodseth

Research Analyst

Office

Hometown

Residence

Research

Ideology vs. Infrastructure Reality

The Blueprint for a Smarter Future

Enterprise Search Is Entering a New Era

Accelerating the shift to stablecoins

AI is Rewriting the Rules of Supply Chain

AI in Procurement

Breaking Ground

Vertical Software Is Having A Moment

Supplier Intelligence

IIoT at a Tipping Point

A Key Piece of the Renewables Puzzle

Pain at the Pump

The Future of Demand Forecasting Software

Why Using Internal Tools to do More with Less Matters Today

Every Company Will Be a Logistics Company

Relevant Experiences

Office

Hometown

Residence

Rebecca Rodseth

Research Analyst

Office

Hometown

Residence

Research

Ideology vs. Infrastructure Reality

The Blueprint for a Smarter Future

Enterprise Search Is Entering a New Era

Accelerating the shift to stablecoins

AI is Rewriting the Rules of Supply Chain

AI in Procurement

Breaking Ground

Vertical Software Is Having A Moment

Supplier Intelligence

IIoT at a Tipping Point

A Key Piece of the Renewables Puzzle

Pain at the Pump

The Future of Demand Forecasting Software

Why Using Internal Tools to do More with Less Matters Today

Every Company Will Be a Logistics Company

Relevant Experiences

Rebecca
Rodseth