Section 2:

AI Adoption and Buyer Expectations

AI is no longer an isolated initiative or a future-facing concept; it’s becoming embedded across core business functions and daily workflows. As organizations grow more confident in AI’s potential, expectations around performance, scale, and strategy are rising, especially in the mid-market, where adoption is accelerating fastest.

Trend #1:

AI Use Is Expanding Across Business Functions

AI is no longer confined to pilots or innovation labs. It’s being actively deployed across departments to solve real problems and improve performance. The most common use cases today are found in customer experience, cybersecurity, and cloud—where AI’s impact on service quality, speed, and scalability are both visible and measurable.

Let's take a closer look…

Top Investment Areas in AI for Cybersecurity, Cloud, and CX

AI + Cybersecurity Investments

Cybersecurity remains a top priority for AI deployment, with buyers applying AI to real-time threat detection, automated incident response, and preventing vulnerabilities. Enterprise buyers are especially focused on conducting code reviews and penetration testing, while mid-market firms are investing in threat identification and prevention automation—often to compensate for lean internal teams. This is a key area where advisors can introduce scalable, AI-infused solutions that don’t require in-house security experts.

AI + Cloud Investments

AI is reshaping cloud management strategies, but the path looks very different for enterprises versus mid-market organizations. Large enterprises typically have internal teams dedicated to cloud ops, DevOps, AI engineering, and FinOps—giving them the runway to focus on automation at scale, resource provisioning, and cloud cost optimization.

Mid-market organizations, by contrast, often lack the talent and bandwidth to implement AI solutions independently. They're interested in LLMs, gen AI pilots, and better deployment practices—but face resource and risk constraints.

That’s where technology advisors come in. By providing vetted vendors, managed solutions, and expert engineering support, TAs help mid-market clients bypass internal limitations and focus on outcomes like uptime, efficiency, and AI readiness—without building enterprise-sized teams.

AI + CX Investments

Customer experience is where many organizations first test AI, and where ROI shows up fast. Mid-market and large enterprise buyers alike are investing in agentic solutions including virtual agents and real-time agent assist, as well as leveraging marketing and messaging campaign management to improve service while reducing manual workloads. Advisors can lead by showing how AI augments human service, enhances satisfaction, and drives top-line growth.

It's worth noting that that mid-market's higher planned investments in data analytics and quality assurance practices versus their enterprise peers are likely due to midmarket challenges with fewer resources. Mid-market tends to prioritize AI investments that deliver immediate operational improvements and measurable revenue impact. Data analytics and AI-driven QA offer clear efficiency gains that free up human resources for higher-value activities. Rather than viewing QA and analytics as overhead functions, mid-market companies recognize them as revenue enablers that can directly contribute to their bottom line.

Trend #2:

Mid-Market Leads in AI Adoption

The mid-market is outpacing large enterprise peers when it comes to AI adoption. Their agility, openness to experimentation, and flatter structures enable them to deploy AI across departments faster. This agility gap means mid-market firms are gaining operational efficiency, data-driven insight, and competitive advantage—while large enterprises continue investing but move at a slower, more deliberate pace.

While both mid-market and large enterprise buyers are adopting AI to boost productivity, efficiency, and customer experience, their priorities diverge beyond that. Mid-market organizations are far more likely to emphasize innovation and modernization, while large enterprise buyers remain focused on cost reduction and headcount control. Identification of project motive early in a sales cycle will enable advisors to properly guide selling conversations based on desired outcomes.

Trend #3:

Support Is Needed for AI Readiness and Strategy

When it comes to external support, large enterprises and mid-market buyers are asking for help in different areas. Large enterprises are more likely to need assistance with non-generative AI (79%), reflecting the complexity of integrating predictive analytics, forecasting, and ML models. Mid-market buyers, on the other hand, rank cloud modernization as their second-highest support need (63%)—signaling a strong opportunity for advisors to guide migrations and infrastructure upgrades.

IT Buyers Need the Most External Support for AI and Cybersecurity. Cloud Modernization is a Must for Mid-Market.

Governance and security remain a major hurdle for both buyer segments. Mid-market firms are especially concerned with in-house talent availability and expertise. Not surprisingly, large enterprises cite cost as a much larger barrier than their mid-market counterparts.

Concerns with Managing AI Today

Expert Insight:
Tailor Support to Buyer Readiness

As AI adoption accelerates, organizations are at varying stages of readiness. An IDC survey revealed that only 36% of enterprises mandate AI and generative AI awareness training across their organizations, highlighting a significant skills gap.² Meanwhile, Forrester's predictions indicate that 60% of generative AI skeptics will embrace the technology, knowingly or not.³

Advisors should assess each client's current AI maturity and provide tailored support to bridge these gaps. This includes offering training programs, developing AI strategies aligned with business goals, and ensuring robust data governance practices. By doing so, advisors can position themselves as essential partners in their clients' AI transformation journeys.

Even though approximately one-third of buyers ranked data readiness as a top IT buying driver, only a small share of advisors are stepping into the role of data readiness advisor. 44% of advisors say they rarely or never discuss data readiness with clients. This is a clear gap—and a missed opportunity for advisors to lead.

Less than 1/3 of Advisors Discuss Data Readiness with Customers

Expert Insight:
What Makes Data Truly “AI-Ready”

A 2024 report by MIT and Snowflake found that 78% of businesses lack a “very ready” data foundation to support generative AI.⁴ To help clients succeed, it’s critical to break down the building blocks of AI readiness:

Data hygiene – Is your data clean, current, and error-free?
Data accessibility – Can the right users and systems securely access it?
Data quality – Is it consistent, accurate, and complete?
Context – Is it labeled and structured so AI models can interpret it?

Advisors who help clients evaluate and improve their data foundation will not only drive stronger ROI, but also open the door to additional opportunities across AI strategy, cloud, and cybersecurity.

The takeaway…

Readiness is the gateway to AI success. Advisors who grow comfortable leading these conversations—on data, compliance, and scaling strategy—will be best positioned to help clients unlock the full value of AI moving forward.

Looking for ideas on how to incorporate data readiness into your sales conversations?

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