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Generative AI has moved from the edge of innovation to the centre of boardroom strategy.
Once seen as an emerging technology, it’s now on the agenda of virtually every board—shaping decisions about risk, productivity, customer experience, and long-term competitiveness. Directors are no longer asking if GenAI matters. They’re asking how fast it’s evolving, where the risks lie, and whether their organisation is moving quickly—and safely—enough.
From regulatory scrutiny and reputational risks to transformative use cases and new business models, GenAI is changing the questions boards ask—and the answers they expect from leadership.
Let's explore ten of the most pressing board-level questions about generative AI and how to answer them effectively.
1. What are the ethical and regulatory risks of generative AI that we must manage?
GenAI systems are not only capable of producing innovative outputs—they can also generate biased, inaccurate, or misleading content that creates reputational and legal exposure. The concern is no longer hypothetical. In fact, references to AI as a risk factor in Fortune 500 filings increased by 473% year-over-year. With the EU AI Act and similar legislation gaining traction globally, boards now treat AI-related risks with the same seriousness as financial or cybersecurity threats.
Creating a Responsible AI Framework is essential and should encompass:
- Human oversight of AI outputs
- Bias testing and explainability
- Audit trails and model monitoring
- Clear guardrails on what data is fed into AI systems
With Kalisa, for example, organisations retain full control over their data — it’s never used for model training — helping mitigate the risk of data leakage or regulatory non-compliance. Such a platform also provides observability and traceability, so boards get ongoing visibility into how AI behaves across departments.
2. Where are the strategic opportunities for generative AI to transform our business model?
Boards increasingly see GenAI as a generational shift—not unlike the internet or mobile. Analysts forecast trillions in economic value creation, largely from reinventing business models. Boards are therefore pressing executives not only to reduce costs, but to identify new sources of growth.
Strategic value often emerges from:
- Hyper-personalised customer experiences (e.g., GenAI-powered client portals)
- Self-service business models (e.g., subscription-based access to AI knowledge agents)
- Workflow automation and decision support systems
Kalisa enables all three — turning institutional knowledge into revenue-generating assets and internal productivity tools. Forward-thinking boards will push for “lighthouse” initiatives that show ROI while paving the way for broader adoption.
3. How do we ensure proper data governance and protect our IP when using generative AI?
GenAI introduces a new layer of complexity to data strategy. There are growing concerns about proprietary or customer data inadvertently entering third-party model training sets, as well as legal ambiguity over ownership of AI-generated content. Boards are therefore insisting on comprehensive data governance policies.
The organisation should:
- Define data access policies for AI tools (especially SaaS)
- Use platforms that do not train on internal data (like Kalisa)
- Vet AI vendors for IP indemnification and secure architecture
- Track AI inputs/outputs with logging systems to monitor for misuse
Data governance needs to move from theory to operational discipline.
4. How should we integrate generative AI into our organisational workflows and processes?
Research from McKinsey indicates that over three-quarters of companies are already using AI in at least one function—but the real gains come from reengineering workflows, not just plugging in tools. Boards are right to ask: where does AI offer the most meaningful uplift?
Boards should encourage management to:
- Map Workflows: Identify knowledge-heavy, repetitive, or time-intensive tasks across the enterprise.
- Run Pilot Programs: Prioritise use cases with measurable impact, such as contract drafting or client query handling.
- Embed Change Management: Don’t underestimate the role of internal champions, training, and cross-functional design.
The most successful implementations integrate AI directly into digital workflows, enabling teams to focus on high-value judgement tasks while offloading execution or synthesis to machines.
5. What are the implications of generative AI for our talent and workforce, and how are we preparing?
Board members recognize that AI is not just a technology issue but a people issue. GenAI can augment employees’ capabilities – for example, automating low-value tasks so staff can focus on higher-level work – but it also demands new skills and can spark job displacement fears. Boards are questioning how AI will impact jobs and employee morale in their organisations. They want to see plans for reskilling and upskilling employees to work alongside AI.
Optimal use of AI requires a commitment to user training, and boards have a responsibility to monitor how AI affects employee engagement and productivityThis includes ensuring a culture of continuous learning so that the workforce can fully leverage AI tools rather than feel threatened by them.
Platforms like Kalisa include role-specific training and intuitive interfaces, enabling non-technical staff to benefit from GenAI. Boards must ensure HR and L&D teams are not sidelined in AI strategy but brought to the core.
6. What oversight and accountability mechanisms should we put in place for generative AI?
AI governance is no longer optional. Boards must now oversee AI risks with the same diligence as financial or cyber. This includes embedding responsible use principles and holding management accountable for outcomes.
Effective oversight includes:
- A formal Responsible AI committee (or board subcommittee)
- Regular risk reports on AI usage, performance, and incidents
- Defined accountability (who signs off on which AI use case?)
- Ethics and compliance embedded into the deployment lifecycle
Kalisa’s observability and transparency features support these goals by making AI outputs traceable and reviewable. Boards should treat AI governance like cybersecurity: a strategic, not technical, responsibility.
7. How do we evaluate and select the right generative AI vendors or partners (and manage third-party risks)?
With thousands GenAI vendors active globally—many of them venture-backed and volatile—the selection process carries real strategic and operational risk. Boards are concerned about data security, continuity, and alignment.
Boards should ask for a vendor selection framework that covers:
- Data control and privacy (Kalisa, for instance, offers a ‘no-train guarantee’)
- Financial stability and support maturity
- IP indemnification clauses
- Transparent roadmaps and update policies
8. How will generative AI impact our competitive landscape, and are we at risk of falling behind?
Strategic directors are keenly aware that AI could upend competitive dynamics in many industries. A competitor that harnesses generative AI effectively might dramatically lower costs, personalize offerings, or innovate faster. Boards are asking for analysis of how industry peers and disruptors are using AI, and what the company must do to keep up. There is a broad recognition that taking a cautious “wait-and-see” approach carries its own risks: an organisation that lags in AI adoption could lose customers or talent to more nimble competitors and miss the learning curve on using the technology.
This sense of urgency is driving board-level discussions on accelerating AI initiatives where strategically appropriate, so the company remains competitive and doesn’t cede market share to AI-enabled rivals.
9. Do we have the necessary expertise and governance structure at the board level to oversee AI?
A 2024 survey showed that nearly 80% of boards had “limited to no knowledge or experience with AI”. Yet fiduciary duties remain—and AI oversight is fast becoming an expectation from shareholders and regulators.
To improve the board's AI literacy, executives can ensure:
- AI education sessions for the board
- Bringing in advisors and AI experts for training sessions
- Recruiting new directors with AI/data expertise
- Assigning AI oversight to a dedicated committee
AI is no longer a ‘tech topic’ but a boardroom imperative. Platforms like Kalisa can also provide executive dashboards and Agents to make AI more accessible to non-technical decision-makers.
10. How will we measure the success and ROI of our generative AI investments?
With the rush to invest in generative AI, boards don’t want these initiatives to turn into sinkholes of money or mere hype projects. A pressing question is: what value are we getting from AI deployments, and how do we track it? Boards are pushing management to define key performance indicators (KPIs) for AI projects and to report on progress regularly.
This means establishing:
- Defined KPIs from Day One: Metrics such as efficiency gains, CX improvements, or new revenue streams.
- Dashboards and Reviews: Quarterly reporting that tracks both adoption and business outcomes.
- Lessons Learned: Evaluation of failed pilots to inform future investment decisions.
AI ROI isn’t only financial—it includes strategic dividends such as brand differentiation, agility, and capability-building. Boards must align evaluation frameworks accordingly.
Final Thoughts
Boards are no longer spectators in the generative AI conversation. They are stewards of risk, opportunity, and strategic direction in a rapidly shifting landscape. By asking the right questions — and having strong answers — they can ensure their organisations thrive in the AI-powered decade ahead.
GenAI platforms like Kalisa are built to help leaders do just that: embedding AI securely, responsibly, and strategically — not just to automate work, but to unlock new ways of delivering value.
Powering the next generation of professional services
Kalisa offers everything you need to deliver valuable GenAI experiences to your clients and team.
- Chat agents with subject-matter expertise
- AI Workflows to automate business processes
- AI workspaces for your team
- Self-serve client portals and dashboards
- Subscriptions and monetisation
- Securely combine public and private data
- API for systems integration
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