How mid-market companies can capitalize on their AI advantage
AI adoption has moved beyond theory into the mainstream and mid-market companies are in a prime position to capitalize on the many benefits. Typically, these mid-market companies are more nimble than their enterprise counterparts, which means that when it comes to technical transformation, they can act quickly, initiate change and deploy AI in targeted ways to solve a real need in the business.
Recent research, however, found that 90% of mid-market companies that have explored AI are still at an early stage or have experienced stalled pilots. Despite their agility, it’s clear many mid-market companies remain stuck in the experimental phases. The challenge is that many are held back by uncertainty over where to start, which technologies to deploy and how to invest with confidence.
This uncertainty and lack of confidence can translate into projects that are not tied to tangible business outcomes and that, ultimately, fail to move into full deployment and deliver real impact.
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For businesses to break out of this pilot purgatory, it’s imperative to overcome three root causes of this stagnation. Specifically, building and mobilizing the right expertise and skillsets, establishing robust data and technology foundations and ensuring the right governance guardrails are in place across each project.
Bridging the expertise gap
Many debates and conversations around AI deployment highlight the importance of building the right skillsets. It stands to reason that having access to specialist expertise is essential to the success of any technology projects, including AI. Without it, businesses are more likely to experience AI paralysis, where they don’t know where to start or become overwhelmed by too many options.
Expertise, however, is a broad term and what is often missed is the exact nature of the skills needed. AI deployments have many facets, including strategy and use case prioritization, tooling and understanding the vendor landscape, through to data readiness, security and privacy.
Teams might feel confident in some areas but unintentionally overlook where they have a fundamental skills gap. What’s interesting is that businesses will often feel confident overall with their capabilities but that confidence might mask a very different reality.
There is a big difference between theoretical confidence and having the practical ability to deliver. Some teams will be equipped to tackle data readiness, for example, but still struggle with the deeper knowledge required in areas such as integration or change management.
What’s needed is a fully rounded team, often with a blend of different resources, that can move beyond theory and establish more mature, execution-focused capabilities.
Addressing the data and technology foundations
It’s not possible to discuss AI success without first looking at the fundamental foundations on which they are built. Putting in place robust data and technology frameworks is essential to moving from pilot to full deployment but many mid-market companies often struggle to achieve this and for good reasons. They’re faced with a multitude of historical issues, including legacy technology, poor data quality and incomplete guardrails.
Enterprise applications such as ERP, CRM and HRIS increasingly come equipped with AI capabilities and, in some cases, as AI-native systems. While these offer many advantages, a lot of companies operate with legacy enterprise applications that lack integration capabilities, have limited functionality and contain outdated data models. All this creates a significant barrier when it comes to AI adoption.
Data quality is also a vital component but, all too often, mid-market companies are faced with poor data quality, which can quickly become one of the biggest blockers of AI deployments.
Data readiness is an integral part of any AI strategy and it’s important for businesses to treat it as such and integrate it into projects from the outset rather than address it as an afterthought. Those that can improve data accuracy, ownership and accessibility will be in the best position to accelerate pilots into production and ensure projects succeed.
Another crucial element of the technology foundation is the guardrails that surround it, including governance, ethics, security and privacy. This is also an area where some mid-market companies fall short. They might have a comprehensive framework in place but have yet to implement formal policies, controls or both.
A practical path forward
Ultimately, for AI to scale beyond pilots, it needs to be embedded into the right environment. For mid-market companies, that requires them to go beyond deploying individual tools and address the systems, data and controls that ensure AI can operate both safely and effectively.
This foundational work is important but it also needn’t hold up projects. Speed can be critical and so addressing issues such as data quality can be done effectively in parallel with deployments, so long as it is considered, mapped out and integrated from the outset.
What is important is that these are all solvable challenges. Knowing and understanding the problems is the first important step. Without full awareness of the issues, companies will inevitably continue to experience the same pilot failures. They can then begin making the right strategic investments, in the right teams to build a full range of skillets, and in the right foundations to create a stronger platform for AI.
Only then will mid-market companies be able to use their agility to its full potential and deliver measurable, sustainable value from their AI investments.
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