Most data leaders are confident articulating technical decisions, architectural trade-offs, or analytical outcomes, but many report that communicating effectively with senior executives remains one of their harder challenges. Often, this comes down to data leaders not being as familiar or comfortable with the granular priorities of business leaders, while also having seemingly different priorities in their strategies.

Research backs this up. In the Gartner Chief Data Officer Agenda 2023, 91% of organisations stated that data and analytics were “critical to business success.” However, only 42% of surveyed CDOs believed their initiatives had reached widespread adoption across their organisations. This gap suggests a lack of alignment between data functions and executive expectations.

This guide clarifies what actually creates that misalignment and offers a communication framework based on patterns consistently observed in high-functioning enterprise environments.

Why Executives and Data Leaders often talk past each other

The differences outlined below are widely observed tendencies shaped by role requirements and incentive structures.

1. Different information priorities

• Data leaders often prioritise precision, completeness of context, and transparent caveats, because their decisions can materially affect analytical accuracy, technical debt, and long-term scalability.
• Executives prioritise clarity, decision-relevance, and timeframes. Their role requires them to make trade-offs quickly across finance, operations, risk, and strategy.

Neither priority is “better.” But when each side defaults to its own style, the other may interpret it as misalignment.

2. Different definitions of risk

Data leaders typically define risk as:

• Model accuracy issues
• Data quality problems
• Architectural fragility
• Operational inefficiency

Executives define risk more broadly:

• Financial exposure
• Reputational consequences
• Regulatory impact
• Opportunity cost
• Strategic drift

If the connection between technical risk and business risk is not made explicit, an executive may miss the significance of the issue, while the data leader may feel unheard.

3. Different time horizons

Executives often work in quarterly or annual cycles. Data initiatives sometimes require multi-year runway. Both timeframes are valid, but it’s key that communication bridges them and creates a shared understanding for both parties.

An framework for communicating with Executives

The following framework is modelled on common decision structures used in consulting, risk committees, and investment governance. It clearly aligns information to how senior leaders routinely evaluate choices.

Step 1: Context

Start from business relevance, not technical detail

Executives need to understand why the topic matters now, framed in operational or strategic terms.

Examples:

• “Claims cycle times increased 14% this quarter.”
• “Customer self-service adoption has plateaued for three consecutive quarters.”

This anchors the conversation in metrics the executive already tracks.

Step 2: Consequence

Quantify or describe what happens if nothing changes

Executives make prioritisation decisions every day, this consequence step gives them comparative weight. You don't need perfect numbers, as ranges or directional impacts are acceptable if stated transparently.

Example:

• “If cycle times continue at this rate, our processing capacity could fall short by 15–20% next year based on current demand.”

This isn’t prediction for prediction’s sake, it’s giving the executive enough information to assess urgency.

Step 3: Options

Present feasible paths, not explanations

Executives are accustomed to choosing between options, not analysing raw detail.

An effective options set includes:

• Approximate cost (time, effort, resources)
• Expected outcomes
• Dependencies
• Trade-offs

Example:

Option A (short-term): Automate the three highest-volume decision points.

• Expected impact: 10–20% faster throughput in < 90 days.
• Trade-off: Limited to specific segments.

Option B (foundational): Improve upstream data quality and workflow design.

• Expected impact: Long-term scalability and reduction of manual remediation.
• Trade-off: Slower initial returns.

This gives the executive room to compare investment sizes and time horizons without overwhelming detail.

Step 4: Recommendation

Give a clear, reasoned view

Executives expect functional leaders to take a position.

A good example of this would be: “I recommend Option A to unlock immediate capacity, combined with a scoped version of Option B to secure long-term efficiency. This balances delivery speed and structural improvements.”

A recommendation supported with transparent reasoning builds trust.

Practical techniques that improve executive conversations

The following practices have repeatedly shown to increase clarity and alignment in enterprise settings. Next time you’re in conversation, try and implement these until they become part of your standard way of communicating. Obviously you need to tweak each conversation to the specific leader, as a Chief Financial Officer will think differently to a Chief Operating Officer, yet conversations with both will benefit from these practices.

1. Lead with the operational outcome, not the capability

Executives don’t need to understand how a model or dataset works unless it directly affects risk or cost. They need to understand the business effect, as that’s what they’d be discussing with the board when there is success or challenges post-decision.

2. Avoid binary language

Data initiatives rarely succeed or fail in binary ways. Use ranges, scenarios, and constraints, this aligns better with how executives evaluate financial and operational futures. Providing nuance with clarity is an art form to be mastered.

3. Make abstract work visible

Show before/after workflows, time saved, steps removed, or handoffs reduced. This answers the common question of “so what”. Executives respond well to changes they can map to real processes.

4. Align time horizons explicitly

If something takes 12–18 months, explain why, and offer milestone markers that show progress along the way. As it’s common for projects to exceed their expected timelines, ensure you buffer in time for challenges instead of overselling which leads to disappointment.

5. Socialise ideas before formal decision points

This is standard governance behaviour, not politics. A brief pre-conversation reduces friction and surfaces objections early. It avoids wasted budget on projects that don’t land, and ensures your team is working on the right objectives. Bringing people into the fold also helps break down the common enterprise silos, and make people bought in as they’ve got a stake in the game.

Communicating the value of Data and AI Literacy

Executives reasonably ask:

“What does data literacy actually change, and where is the evidence?”

What we know from research:

• The Gartner Data Literacy Survey (2021) found that organisations with higher data literacy levels reported more consistent use of data in decision-making. This seems like a no-brainer to anyone who has invested in a data literacy programme, but leveraging credible sources helps make the point.
• McKinsey’s 2022 State of AI report shows that companies with broader AI skill distribution tend to adopt more AI use cases successfully, though the research stops short of claiming causation. And now we’re in the peak AI curve, we can see that lack of AI skills is a big bottleneck for adoption, driving ROI and innovation.

A simple statement to make is that data literacy increases an organisation’s capacity to use data and AI tools effectively, but it is not sufficient on its own. It must be aligned to real workflows, incentives, and business goals. This is why Data Literacy Academy starts by aligning our programmes directly with the corporate and data strategies of its customers.

Communicate literacy as a capability enabler, not a guaranteed outcome. And with the right measurement systems in place at the start, it becomes a valuable place to track impact, collect new use cases and engage the 80% of people outside the data team who are essential to bring on the journey if an organisation wants to claim to be data-driven. That framing is factual and aligns with what executives expect

How to use this framework to gain Executive support for a Data & AI Literacy programme

Proposing a Data & AI literacy programme to senior executives can be challenging because its impact is distributed across people, processes, and decision-making. This is not immediately visible in a single KPI. Many executives acknowledge its theoretical importance but struggle to map it to concrete operational outcomes. This is where a structured communication approach becomes essential.

Below is how to apply the framework we discussed earlier to make the case clearly, without overstating or relying on abstract claims.

1. Context: Anchor the conversation in a current business challenge

Executives respond best when the conversation starts with issues they already recognise. Instead of positioning literacy as a “skills uplift,” connect it to a specific organisational constraint.

Examples of accurate, situational contexts data leaders commonly face:

• Decisions involving Data & AI are taking longer than necessary because business teams escalate unclear or ambiguous issues to technical teams.
• AI pilot projects are slow to progress because teams lack a shared understanding of inputs, required data quality, or operational implications.
• Key workflows rely on manual judgment where data-informed decision-making could improve speed or reduce inconsistency.
• Stakeholders across functions hold different assumptions about terminology, capability boundaries, or risks, which slows alignment.

This context grounds the conversation in observable, measurable organisational friction.

2. Consequence: Clarify the operational impact of maintaining the status quo

Executives evaluate investment choices based on trade-offs. Consequence gives them the information necessary to assign weight.

Examples of grounded, non-speculative consequences:

• When teams lack shared understanding of data concepts, cross-functional initiatives take longer to scope, leading to delays in project start dates or misaligned resource allocation.
• Misinterpretation of risk (e.g., privacy, model limitations, data lineage) can result in overly cautious or overly optimistic decisions, either of which affects throughput or compliance overhead.
• If frontline or operational teams lack confidence using data tools, reliance on specialists increases, which lengthens cycle times for decisions that could otherwise be self-serve.
• When AI-related discussions lack common terminology, decision-making meetings become repetitive, and progress slows due to re-clarification.

Each consequence can be illustrated using ranges or past examples, avoiding claims of guaranteed ROI while still highlighting clear operational implications.

3. Options: Present structured pathways forward

A literacy programme is not a single monolithic action, and executives prefer to evaluate alternatives.

Here is a grounded set of realistic options:

Option A: Limited cohort-specific literacy uplift

• Focused on teams directly involved in near-term Data & AI initiatives
• Lower time investment, faster implementation
• Improves decision-quality within targeted workflows
• Trade-off: Broader organisational alignment remains limited

Our live learning cohorts are designed in a highly targeted way, so impacts are tied to specific goals of said cohort.

Option B: Enterprise-wide baseline literacy

• Establishes shared terminology and expectations across functions
• Reduces miscommunication in cross-functional decisions
• Improves readiness for upcoming automation or AI adoption
• Trade-off: Often not as custom as a hands-on customised curriculum

Our OnDemand platform caters to upskilling thousands of learners in a self-service pathway. These pathways are customised to suit organisations’ requirements and the level of the learners. OnDemand also enables the capture of thousands of use cases from the learners, which benefits leaders to understand where skills and product gaps exist.

4. Recommendation: Make a reasoned, defensible proposal

Executives expect their functional leaders to integrate technical understanding with business relevance.

A grounded recommendation might look like: “Based on the pace of upcoming AI-related decisions and the cross-functional nature of our initiatives, I recommend combining Option A and B. It provides enough customised alignment, while giving us the scope to scale the programme fast. We can begin with a subset of teams and expand based on observed impact.”

Or, in a different organisational maturity: “Given current constraints on time and resource availability, Option B offers the clearest near-term benefit. I recommend piloting with the business teams who foundational skills fast, while assessing success before unlocking more customised cohorts in a live learning setting.”

A recommendation is not a prediction of success, but it is a structured argument that matches organisational constraints with feasible actions.

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