AI market tilt: mid-sized US firms call for government action
A new survey of 250 US C-suite and data leaders finds growing concern that artificial intelligence is helping large enterprises lock in customer advantage at the expense of mid-sized rivals. Sixty-seven percent fear AI could be weaponized by big players to squeeze out smaller competitors, and over three-quarters want regulatory support to level the field.
AI now sits at the center of customer engagement, but the gains are flowing to firms with deep tech stacks and data moats. Nearly two-thirds say AI-driven loyalty programs hand large enterprises an unfair edge, and almost three-quarters warn they will struggle to compete without major AI investment.
"AI is fundamentally changing the rules of customer engagement, but the gap between what large enterprises and smaller businesses can deliver is widening, not because of a lack of innovation, but due to unequal access to high-quality data, skills and advanced technology," said Rick Boyce, Chief for Engineering at AND Digital.
What's driving the gap
Cost and complexity are the biggest blockers. More than half cite the expense of AI-led loyalty programs, and 50% say they lack the internal skills to deploy the tech safely.
In a rush to keep up, nearly three-quarters are prioritizing fast AI spend instead of fixing data quality and governance first. That trade-off can hit performance and raise compliance exposure.
Why this matters for government
The survey signals a risk of market concentration: a small set of tech-savvy firms could gain decisive advantages in winning and retaining customers. That affects consumer choice, pricing dynamics, and the resilience of local economies.
This isn't about stifling innovation. It's about creating rules and supports that let mid-sized firms compete on product, service, and trust-not just on access to data, compute, and loyalty ecosystems.
Policy actions to consider
- Data portability and interoperability: Set standards for loyalty and personalization systems so customers and businesses can move data easily via read/write APIs. Consider extending consumer data rights to include AI-enriched profiles and audience segments.
- Guardrails for AI-driven pricing and targeting: Require independent testing for fairness, exclusionary effects, and dark-pattern risks. Use existing authority to address exclusionary conduct and clarify expectations through FTC/DOJ guidance.
- Access to compute and models: Pilot national AI credits or shared compute pools for SMEs. Tie credits to open interfaces and multi-cloud portability to reduce vendor lock-in.
- Shared data resources: Stand up privacy-preserving data collaboratives and synthetic datasets that mid-sized firms can use without breaching consumer trust.
- Skills and safety programs: Fund training and apprenticeships that start with data foundations, security, and evaluation. Align with the NIST AI Risk Management Framework for practical, risk-based adoption (NIST AI RMF).
- Compliance-by-design templates: Provide standardized model cards, vendor due diligence checklists, incident reporting forms, and procurement clauses that mid-sized firms can adopt with minimal overhead.
- Regulatory sandboxes and assurance labs: Create routes for pre-market testing and third-party validation so mid-sized firms can ship safely without drowning in process.
- Enforce what already exists: Tackle predatory bundling, deceptive AI claims, and algorithmic collusion using current consumer protection and competition rules. Signal that scale will not excuse harmful conduct.
Use procurement as a lever
Federal and state purchasing can set the tone. Require open standards, data portability, independent testing, and small-business participation in AI contracts. Favor solutions that support interoperability and exit options, not closed ecosystems.
What to measure
- Share of mid-sized firms participating in AI-enhanced marketplaces and loyalty ecosystems.
- Customer mobility across loyalty programs (switching costs, data transfer time).
- Compliance cost as a share of AI spend for mid-sized firms.
- Incidents tied to AI-driven targeting, pricing, or exclusionary practices.
- Time-to-deployment for mid-sized firms using sandboxes or pre-certification.
Risks to avoid
- Rushing AI adoption without fixing data quality and governance.
- One-size-fits-all rules that burden mid-sized firms more than incumbents.
- Vendor lock-in disguised as "free" credits or bundled services.
The window for action
The direction is clear: AI will separate firms that can invest in data, skills, and safe deployment from those that can't. Without policy support, the market will tilt harder toward incumbents.
"If we want a truly competitive and fair digital economy, we need to support mid-sized firms in overcoming these barriers, whether through investment, education, or regulation," Boyce added.
Helpful resources
- Executive Order on Safe, Secure, and Trustworthy AI
- NIST AI Risk Management Framework
- Curated AI training by job role (for grant and workforce programs)
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