By Kevin Rutter, CEO, AIQ Markets.
AI is rapidly becoming part of the buy-side technology stack. But before a portfolio manager can act on a recommendation or a trader can execute an idea generated by AI, firms must answer a more fundamental question: can they trust it?
As buy-side firms move from experimentation toward production deployment, the focus has shifted from what AI can do to how it can be governed within existing regulatory structures. Consider a credit trader using AI to identify liquidity opportunities. While the technology can surface actionable insights in seconds, firms still need confidence that every recommendation is explainable, auditable and compliant before it can influence a trading decision.
This is a trend being seen across many industries. A survey of almost 1000 C-suite business leaders from earlier this year found that while 75% of boards have approved on major AI investments, 48% had still not set AI governance expectations and 46% had not integrated AI risk into ongoing oversight.[1] Behind the scenes, this is where much of the hard work is now being focused.In many organisations, the most influential voices in AI adoption discussions are now the compliance officers, risk teams and technology governance committees tasked with understanding how these systems operate, what risks they introduce and whether they can meet the standards required in regulated financial markets.
History repeating itself?
Historically, the introduction of new trading technology has followed a relatively familiar pattern. Firms evaluate functionality, assess integration requirements, conduct operational testing and determine whether the benefits justify the investment. Questions around security and compliance are always part of the process, but they are generally well understood when it comes to traditional trading systems, analytics platforms and market data tools. These are largely deterministic, so users can typically trace how outputs are generated and identify the underlying data used to produce them.
AI introduces a different set of considerations. When firms evaluate AI platforms today, the due diligence process often extends far beyond traditional technology assessments. Compliance and risk teams want to understand where data is stored, how information is processed, whether client data could be exposed to third parties and what controls exist to prevent misuse. They also want to know whether outputs can be audited, whether responses can be traced back to source data and whether the reasoning behind a conclusion can be reconstructed after the fact.
These concerns are full justified and reflect the realities of operating in highly regulated markets, where firms are expected to maintain robust governance standards around investment decision-making and technology risk.
The fixed income challenge
The challenge is particularly acute in fixed income markets. Unlike equities, where information is often centralized and prices are continuously visible, corporate bond markets remain highly fragmented. The U.S. corporate bond market alone comprises tens of thousands of individual securities, each with its own liquidity profile, trading history and issuer-specific characteristics. Trading activity can be highly episodic, with meaningful periods between transactions. Market participants frequently need to combine data from multiple sources to build a complete picture of relative value and market conditions.
This complexity creates an environment where AI can deliver genuine benefits. Fixed income professionals spend significant amounts of time searching for information, monitoring market developments, analyzing liquidity conditions and evaluating potential portfolio actions. AI has the potential to accelerate many of these workflows, helping users surface relevant information more quickly and focus more of their time on investment decisions.
However, the same complexity that makes AI valuable also increases the importance of governance. Investment professionals need confidence in how the answers they receive were generated. If an AI system identifies a trading opportunity, highlights a portfolio risk or suggests a potential rebalancing action, users must be able to understand the underlying data, assumptions and logic that contributed to the output.
This is particularly important because investment decisions often need to be justified long after they have been made. Compliance reviews, internal audits and regulatory examinations frequently require firms to demonstrate what information was available at a particular point in time and how decisions were reached. Any technology that becomes part of the investment process must support that level of scrutiny.
As a result, explainability is emerging as one of the most important considerations in AI adoption. For many buy-side firms, the key concern is whether the responses they receive from a platform can be understood, validated and defended within existing governance frameworks.
This is where the industry’s attention is increasingly focused. Firms are evaluating audit trails, source attribution, model controls, user permissions and data governance alongside traditional measures such as accuracy and productivity gains. The ultimate goal is to ensure an AI system can operate within established compliance processes rather than requiring entirely new oversight structures.
In practice, this means the most successful AI platforms will be those that combine advanced models with transparency, preserve human oversight and integrate effectively with existing governance requirements.
Man and machine
The buy-side’s approach to AI is therefore becoming more pragmatic and more mature. Despite headlines predicting autonomous investing and machine-led decision making, most institutional investors are not looking to replace human judgement. They are looking to augment it by equipping professionals with better information, delivered more efficiently, so they can make more informed decisions themselves.
That distinction reframes the role AI should play within investment organisations. The technology is most valuable when it acts as decision support rather than decision replacement. In that context, transparency, accountability and governance become critical features rather than secondary considerations.
The first phase of AI adoption in financial markets was driven by curiosity about what the technology could achieve. The next phase will be shaped by a practical question of how these capabilities can be deployed in a way that satisfies the operational and compliance obligations that define institutional investing.
Answering that question will determine which platforms move beyond pilot programs and become embedded within everyday workflows. More importantly, it will determine how quickly the buy-side can capture the productivity and analytical benefits that AI promises to deliver.
[1]https://www.grantthornton.com/services/advisory-services/artificial-intelligence/2026-ai-impact-survey
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