Retail solutions providers: Lead with data or risk irrelevance

B2B software developers, value-added resellers (VARs), and managed services providers (MSPs) often view a long-term, stable POS installation as a major win. We’d like to think that it represents predictable maintenance revenue and a loyal client. However, new research from IHL Group suggests that this mindset might be putting your business, and your customers, at severe risk.

I recently sat down with Greg Buzek, Founder, President and Principal Analyst of IHL Group, to discuss their latest study of over 400 mid-sized SMB retail brands across North America. The findings serve as a stark wake-up call for the retail IT channel. As Buzek cautioned, the retail landscape is largely composed of tech laggards. Indeed, the retailers most likely to be running the legacy systems you sold them five to ten years ago are the very businesses the data identifies as falling behind. This represents both your biggest risk of customer churn and your greatest opportunity to capture an emerging replacement wave.

The reality of the retail divide

To understand the scope of this challenge, it helps to look at how IHL Group categorizes the market based on 2025 performance. Sales winners and profit winners achieved growth of 10% or more, average retailers saw growth between 0% and 10%, and laggards experienced flat or negative growth. The study focused specifically on mid-sized SMB retailers with revenues of $500 million or below, spanning specialty and general merchandise segments such as apparel, mixed goods, cosmetics, and hard goods.

When we look at the data, the technological gap between winners and laggards is absolute. If you only defend renewals on legacy systems, you are actively defending the losing side. You must intentionally transition from being box resellers to becoming a unified platform and services partner, leading the conversation toward modernization before a competitor does.

Ten critical findings for the retail IT channel

The data points collected by IHL Group outline a clear sequencing story for technology providers. Here are the realities you need to confront:

1. 1. 1. The “POS system” gap is absolute. Zero percent of retail laggards run up-to-date software, and zero percent have a unified data lake. In comparison, 44% of profit winners already operate a current data lake.
      2. A major replacement wave is already in motion. The data shows that 73% of sales laggards plan to purchase new software within the next 12 months. The reseller who frames the refresh now will capture that pipeline, while those who wait for a formal request for proposal will be forced to compete on price.
      3. Inaction is highly concentrated in a specific slice of the market. Half of technology laggards have no plans to invest in new POS technology, which is more than three times the rate among average retailers. It’s time to segment your customer base and stop spending equal service energy on accounts that refuse to invest in their own survival.
      4. Modernization drives measurable financial out-performance. Profit winners prioritize software refresh at 59%, compared to just 25% for laggards. Furthermore, retailers who completed a software refresh in 2025 achieved sales growth 55% above the survey average and profit growth 45% above the average. This provides clear ROI proof for your next upgrade pitch.
      5. Monolithic architectures are fading out. Zero percent of laggards run a current microservices architecture, and 91% have no plans to adopt one. Meanwhile, 20% of sales winners have already deployed microservices, and another 40% plan to move within the next year. If your portfolio consists of a closed, monolithic box, you risk being designed out of the next technology cycle.
      6. Infrastructure dictates artificial intelligence readiness. The divide between being AI-ready or AI-excluded comes down to infrastructure, not intent. Because a unified data lake is the absolute prerequisite for AI, and laggards have none, your sale can no longer be just a terminal. Rather, it must be a single source of truth that makes everything downstream possible.
      7. Rushing AI carries a heavy financial penalty. Retailers who bolted generative AI onto existing applications before fixing their data foundation took a severe near-term hit, with sales growth 119% lower in 2024 and 2025. Although they project 80% higher profit growth once the foundation catches up, clients chasing AI on dirty data will underperform and may blame the technology or their provider. Your service model should sell data cleansing and integration first, and AI second.
      8. Mobile solutions represent an easy, high-margin attach opportunity. Sales winners are nearly five times more likely to have current mobile software, running 486% higher than laggards, while profit winners are 467% higher. This is a highly visible signal of a modern store and an entry point for future associate-facing tools.
      9. Omnichannel services remain a wide-open opportunity for partners. Only 15% of all respondents are fully optimized for buy-online-pickup-in-store (BOPIS). Additionally, 71% of laggards are not optimized for BOPIS, and 75% are not optimized for ship-from-store. These systems fail on fragmented data, creating an ongoing services line for configuration, integration, and inventory accuracy.
      10. Store associates are an investment priority for winning retailers. Sales leaders prioritize loss-prevention technology 413% higher than laggards. Among sales winners, 60% plan to add store staff over two years, compared to just 18% of laggards. We should position smart tech as an associate capability multiplier, not a headcount-elimination tool, to match how winning retailers actually buy.

The hidden support cost of point integrations

For VARs and MSPs, the temptation is often to solve every individual client problem by stitching on another point solution. However, this creates what Buzek calls a severe integration tax.

According to the IHL Group research, a retailer running just six connected systems can end up managing up to fifteen active integrations, each of which can break independently. Every point solution you stitch on today becomes a potential weak point you are forced to support tomorrow. Moving customers toward a unified data model reduces our own backend support costs while significantly raising the strategic value you deliver to the retailer.

Three strategic priorities for the next 24 months

If you are adapting your business model to capture this shifting market, Buzek outlines three critical priorities that should guide your roadmap over the next two years:

1. 1. 1. Reposition as a data-foundation partner. Lead every single engagement with the single-source-of-truth and cloud-native story, and then attach AI capabilities later. The 73% replacement intent among laggards is your pipeline, and the massive data lake gap is your definitive proof.
      2. Segment and triage the base. Since roughly half of laggards have no plans to invest, you must concentrate your service capacity on the movers. Build a structured upgrade path for the clients planning a refresh, and stop subsidizing accounts that refuse to invest in their own survival.
      3. Build a recurring services line around data accuracy. Omnichannel optimization and data cleansing are ongoing requirements, not one-time events. Sequence data before AI so your customers avoid near-term revenue dips caused by rushing into advanced technology with fragmented data.

The road to 2030

The technological divide in retail is widening and compounding. Winners are constantly reinvesting their financial advantages into more stores, more technology, and more AI with every single cycle.

As we look toward 2030, the ultimate separator between winners and losers will be whether a retailer treats unified data as a continuously produced operating asset rather than a one-off project. Those who safely unify their data early will run mature AI applications, including demand forecasting, autonomous replenishment, dynamic pricing, and shelf intelligence, while also monetizing that data with consumer-packaged goods partners. The laggards, conversely, will remain completely AI-excluded by their own legacy infrastructure, regardless of how large their eventual AI budget size may be.