AI Stakeholder Engagement 2026

In the context of AI projects, stakeholder engagement refers to the process of actively involving individuals, groups, and organizations who affect or are affected by the development and use of artificial intelligence systems. Whether consulting data scientists, business leaders, regulators, or end-users, project teams seek out their perspectives, requirements, and concerns to shape outcomes that deliver tangible value.

Why does this matter for AI adoption? Effective stakeholder engagement delivers measurable improvements in project buy-in, implementation speed, and ongoing trust. For instance, the McKinsey Global Survey (2022) found that organizations which integrate Cross-functional stakeholders in AI projects are 1.8 times as likely to achieve significant value from their AI initiatives compared to those that do not. Aligning interests early in the process accelerates adoption, while transparent communication mitigates resistance and fosters collaboration across business silos.

Of course, the AI landscape raises distinct engagement challenges. Rapid innovation, complex ethical considerations, tightly regulated data environments, and persistent skills gaps all make stakeholder dialogue in AI projects fundamentally different from other IT projects. Have you witnessed stakeholder misalignment stall or derail an AI project? Consider what happens when privacy advocates, engineers, and executives bring conflicting priorities to the table. Navigating these complex dynamics demands strategic engagement techniques tailored to the AI context.

Pinpointing Your AI Stakeholders: Who Shapes the Conversation?

Mapping Stakeholder Types in AI Projects

AI initiatives bring together a web of distinct stakeholders. Who holds influence? The answer depends on the project’s reach, technology, and application. Here's a snapshot of the core groups:

• Users: Whether internal employees or end customers, users interact with AI systems daily. Think about hospital staff using diagnostic algorithms, marketing teams leveraging predictive analytics, or customers engaging with chatbots.
• Developers and Data Scientists: These professionals design, train, and maintain the AI models. Their technical expertise defines the boundaries of what the system can and cannot do.
• Executives and Decision-Makers: Leadership teams decide budgets, set priorities, and assess the business value of AI adoption. Their endorsement unlocks resources and buy-in across organizations.
• Regulators and Policymakers: Data privacy authorities, industry watchdogs, and government agencies set compliance requirements. In the EU, for example, the proposed AI Act will define risk categories and oversight standards for various AI systems (European Commission, 2023).
• Customers: Differentiated from users in B2B contexts, customers purchase or specify AI-powered products for use by others. Their expectations influence product features and performance benchmarks.
• Community and Advocacy Groups: Nonprofits, ethics organizations, and local community representatives weigh in on AI’s societal impact. Advocacy pressure has driven major companies to adjust algorithms for fairness and transparency (Algorithmic Justice League, 2022).

Data Points and Sources to Identify Influential AI Stakeholders

Selecting stakeholders begins with data-driven investigation. Where does key influence concentrate? Several approaches yield results:

• Analyze organizational charts and project documentation to pinpoint decision-makers, technical leads, and implementation teams.
• Extract user analytics and system interaction logs to spot high-frequency users, superusers, or segments encountering pain points.
• Monitor regulatory filings, industry commentary, and public statements to surface external parties with power to set policies or influence reputation. In 2023, nearly 62% of Fortune 500 companies reported engagement with regulators on AI matters (IBM, 2024).
• Conduct stakeholder mapping workshops—use interviews, surveys, and cross-functional meetings to elicit perspectives from overlooked groups.
• Leverage social network analysis within organizations, identifying informal leaders based on email, project, or communication patterns. Research from MIT Sloan (2022) confirms that these "connectors" often sway project adoption rates and sentiment.

User Personas: A Foundation for Stakeholder Understanding

Granular understanding emerges by building user personas. These data-driven archetypes synthesize demographics, pain points, goals, and behavioral patterns, clarifying nuanced stakeholder needs.

• Surveys, interviews, and behavioral analytics reveal motivations and challenges across user types. For example, front-line healthcare staff report usability and alert fatigue as top barriers to AI adoption (JAMA Network Open, 2022).
• Personas guide feature prioritization and support tailored communication. When development teams reference specific personas, feedback cycles speed up and resonance improves.
• AI-adopting organizations use personas to test product-market fit, forecast acceptance rates, and flag areas for targeted training.

Who stands out in your organization’s AI journey? Try listing current and potential AI users, then identify who influences their adoption decisions. What data might uncover less visible but highly impactful voices? Let these questions shape your next stakeholder analysis session.

Effective Communication Strategies for AI Stakeholder Engagement

Tailoring Messages for Technical and Non-Technical Audiences

Technical and non-technical stakeholders process information differently. Data engineers, machine learning specialists, and IT teams examine algorithms, model architecture, and deployment pipelines. They respond to discussions that emphasize accuracy metrics, scalability concerns, and dataset integrity, as documented by the McKinsey Global Institute in their 2021 report on AI adoption (source).

• Present confusion matrices, ROC-AUC scores, and technical constraints using diagrams, tables, and code snippets.
• Address debugging, technical debt, and model retraining schedules with actionable data.

Board members, HR, marketing professionals, and frontline staff hold different priorities. Use visual aids, analogies, or outcome-focused storytelling to map the impact of AI on operational efficiency, customer experience, or regulatory compliance. A 2023 Deloitte AI Institute survey notes that 63% of executives prefer impact visualizations over technical detail when evaluating AI projects (source).

• Create simple process diagrams, highlight quantifiable outcomes, and translate technical jargon into business value.
• Field questions using cost-benefit scenarios, projected ROI, or changes to daily workflows.

Your messaging gains traction when accuracy matches the technical understanding of the audience.

Tools for Facilitating Conversation and Information Sharing

Selecting the right tools creates pathways for bidirectional communication. Open forums, such as Slack channels or dedicated Microsoft Teams threads, establish ongoing dialogue and quick feedback loops in real time. Project management software, including Jira and Trello, enables transparent tracking of project milestones and stakeholder input, while regular town halls or digital roundtables provide structured opportunities for discussion and consensus-building.

• Interactive dashboards powered by business intelligence platforms like Tableau or Power BI present model outputs and key metrics graphically, supporting data-driven debate.
• Knowledge repositories set up on platforms like Notion or Confluence organize FAQs, technical documentation, and meeting notes for easy discovery.
• Survey and polling tools—such as SurveyMonkey or Google Forms—rapidly collect stakeholder sentiment on features, implementation timelines, and change readiness.

Teams adopt these platforms to democratize information and minimize knowledge silos, creating an environment where transparency prevails and frustration levels drop.

Highlighting the Importance of Ongoing Dialogue

Engagement relies on sustained, multi-directional communication over the project lifecycle. Initial project kickoffs spark curiosity, but regular check-ins—through monthly Q&A sessions, asynchronous forums, and ad hoc feedback surveys—nurture commitment. AI Canada’s 2022 survey found that 78% of successful AI projects maintain scheduled stakeholder updates and track their effects on buy-in and trust (source).

What questions might a skeptical stakeholder raise after deployment? How might end-users interpret a model’s prediction or recommendation? When development teams include these voices in retrospectives or sprint reviews, overlooked issues are surfaced early, and pivots happen before mistakes scale. Teams revisit strategy as new data emerges, adjusting communication channels or meeting formats as needed.

Consider: Who hasn't spoken up during the last project phase? What silent objections could derail progress if left unchecked? Facilitating conversation means creating an accessible and psychologically safe space where every perspective matters, feedback becomes actionable, and shared understanding moves the AI initiative forward.

Building Trust in AI Systems: Transparency, Resources, and Addressing Concerns

Transparency with Data Points and Decision Processes

Stakeholder confidence in AI systems grows when the rationale behind algorithmic decisions is clearly documented and accessible. Transparent AI development involves the open sharing of model logic, performance metrics, and data sources. For example, the 2022 Global AI Adoption Index from IBM found that 74% of surveyed organizations considered explainability and transparency as critical factors for successful AI deployment, with 68% actively publishing model performance reports for stakeholder review. Visualization dashboards displaying decision paths and input data, such as those using the LIME or SHAP frameworks, demystify “black box” AI by articulating why a particular output occurred.

Sharing Resources to Deepen Stakeholder Understanding

Presenting stakeholders with practical tools and reference materials sharpens their comprehension of AI systems. Offer data sheets summarizing model limitations, training datasets, and validation techniques, as demonstrated in Google’s Model Cards for Model Reporting; these provide structured, user-oriented overviews. Interactive workshops, white papers, and technical briefings encourage hands-on engagement, allowing stakeholders to query systems directly and pose questions about model assumptions or risk factors. When Forrester surveyed AI-using enterprises in 2023, 60% reported increased stakeholder satisfaction after distributing accessible guidance documents and launching tailored education sessions.

Addressing User Concerns Proactively

Addressing uncertainty consistently influences stakeholder trust. Teams running quarterly “Ask Me Anything” sessions report higher ongoing collaboration and fewer misunderstandings, according to Deloitte’s State of AI in the Enterprise (Fifth Edition, 2022). Using live demonstration environments where users experiment with AI functionality—instead of observing static presentations—enables immediate feedback loops. Examining data privacy, error correction methods, or bias mitigation steps, and then soliciting stakeholder questions, creates an environment for open dialogue.

• Have you invited users to preview new AI features and recorded their reactions?
• Which FAQs or support channels do stakeholders consult most often when seeking clarity?
• How do you encourage candid conversations about AI system performance or ethical implications?

Direct engagement, coupled with actionable follow-up, cements trust and elevates ongoing stakeholder participation.

Ethical Considerations in AI Deployment

Core Ethics Principles Shaping AI Initiatives

Stakeholder engagement in AI requires an unwavering commitment to core ethical principles. These principles—fairness, accountability, and transparency—form the foundation of responsible AI deployment. Projects that rigorously apply fairness prevent discriminatory outcomes and promote equitable access. For example, The AI Now Institute’s 2023 report highlights that bias in AI-driven hiring tools correlates with reduced workplace diversity when fairness checks are absent.

• Fairness: When development teams evaluate datasets for representativeness and implement bias mitigation techniques, algorithms consistently yield equitable outcomes across demographic groups.
• Accountability: Assigning clear roles for oversight within the project team ensures actions and decisions are traceable; a 2021 Harvard Business Review study found that transparent lines of accountability accelerated error detection by 34% in deployed AI systems.
• Transparency: Openly documenting model training processes, data provenance, and decision logic allows stakeholders to understand, audit, and question outputs. For instance, the European Union’s AI Act (2024) prescribes explainability mandates, which increase user trust and adoption rates.

Engaging the Community for Perspective Diversity

AI projects that actively engage a broad spectrum of community stakeholders uncover blind spots and reduce the risk of unintended consequences. Hosting workshops, public consultations, and interactive forums invites participation from voices representing various demographics and interest groups. As a result, Microsoft’s AI ethics guidelines emphasize involving external advocates to inform product design, which measurably increases the detection of ethical risks.

What innovative methods can your organization use to integrate marginalized community input into your next AI project?

Integrating Ethical Guidelines into Project Planning

Responsible AI deployment demands the systematic inclusion of ethical guidelines in every project phase. Embedding concrete ethics checklists into project milestones ensures ongoing alignment with fairness, accountability, and transparency. For example, implementing regular ethics reviews at each agile sprint has led IBM to reduce bias-related escalations by 23%, according to its 2022 Impact Report.

• Teams that reference organization-wide ethical frameworks when scoping tasks preemptively address emerging dilemmas.
• Allocating dedicated time in project plans for stakeholder ethics reviews guarantees all voices influence critical decisions.
• By measuring progress against established guidelines throughout development, leadership ensures continuous ethical performance.

Which steps could you take today to embed ethics into your AI project roadmap, and how might those choices shape deployment outcomes?

Managing Expectations of AI Outcomes: Aligning Perception with Reality

Setting Realistic Performance Metrics and KPIs

Developing performance metrics rooted in the actual scope of AI capabilities lays the groundwork for productive stakeholder engagement. Rather than vague benchmarks, concrete KPIs give clarity. For instance, when deploying a natural language processing (NLP) model in a customer service application, instead of promising “better customer satisfaction,” define specific objectives—such as reducing average response time from 3 minutes to 45 seconds, or automating resolution for at least 60% of tier-one support queries. Gartner’s 2022 survey reveals that 47% of AI initiatives hit a plateau or fall short when stakeholders’ expectations outpace demonstrable results (Gartner, 2022).

Invite stakeholders to weigh in on the prioritization of these KPIs. How might their daily workflows or business objectives shift if AI consistently achieved the proposed targets? Pose this question directly during briefing sessions or workshops to spark critical reflection and deeper participation.

Using Data and Examples to Illustrate Capabilities and Limitations

Presenting empirical data sharpens understanding. Share before-and-after comparisons, such as error rates, speed improvements, or cost savings observed post-AI implementation. For example, when evaluating an AI-powered document classification tool, show a sample confusion matrix detailing true positive and false positive rates. In 2023, McKinsey found that organizations which supplied stakeholders with quantitative results—such as a 30% increase in operational efficiency or a 15% drop in manual tasks—reported higher satisfaction and less disillusionment (McKinsey, 2023).

Don’t shy away from limitations. Walk stakeholders through failure scenarios and boundary cases, backing explanations with actual failure rates or error types encountered during model validation. Provide examples where an AI-powered vision system misclassifies ambiguous inputs, and contextualize these instances within the broader accuracy rates. This transparency encourages realistic long-term planning.

Strategies for Ongoing Engagement to Recalibrate Expectations

• Host quarterly review meetings where results are juxtaposed with initial objectives. Use these sessions to revise KPIs based on new data, maintaining a two-way dialogue about evolving business needs and technical constraints.
• Establish continuous monitoring dashboards accessible to stakeholders; allow them to visualize daily or weekly performance trends, anomalies, and ongoing learning cycles.
• Deploy targeted feedback loops. After major releases or updates, ask stakeholders: “What capabilities surprised you? Where did AI underperform compared to your expectations?” Document these responses to inform the product roadmap and subsequent communication efforts.

Maintaining an adaptive approach to engagement, grounded in measurable evidence and regular recalibration, eliminates the disconnect between stakeholder hopes and AI realities. In which ways could your own organization refine its expectation management processes to further strengthen trust and accountability?

Involving Non-Technical Stakeholders in AI Projects

Tools and Resources for Knowledge Transfer

Non-technical stakeholders add unique insights to AI projects when provided with effective tools for understanding. Workshops create interactive environments where participants can engage directly with AI concepts; in 2023, 63% of AI initiatives in Fortune 500 firms included such workshops as a core component of their stakeholder engagement strategy (Gartner, 2023). Explainer documents—featuring detailed breakdowns of algorithms, use case walkthroughs, and visual aids—enable stakeholders without technical backgrounds to grasp project scope and potential impact. For instance, a global survey by O’Reilly Media in 2022 found that projects accompanied by layperson-friendly documentation experienced stakeholder satisfaction rates 24% higher on average. One-on-one briefings and live Q&A sessions supplement these materials, addressing knowledge gaps in real time and tailoring learning to specific interests. What format would foster the most engagement within your own organization?

Encouraging Active Participation from All Stakeholder Groups

AI initiatives thrive when all voices contribute, not just those fluent in technical jargon. Structured brainstorming sessions blend technical and non-technical perspectives, often leading to richer problem-solving approaches. Rotating facilitators ensure that discussion bias does not favor engineers or data scientists exclusively. Cross-functional workgroups, set up at the earliest project phases, allow marketing, HR, operations, and compliance professionals to shape requirements alongside technical teams. In a 2023 Deloitte survey, 41% of successful AI implementations directly attributed their results to proactive non-technical stakeholder involvement during both proof-of-concept and deployment stages. Consider: who from outside your IT department could improve the project with hands-on input?

Approaches to Incorporating Non-Technical Feedback into Technical Tasks

Transforming non-technical feedback into actionable changes requires clear frameworks. Use storyboarding and journey-mapping workshops: these translate qualitative insights into prioritized feature lists or objective acceptance criteria. User acceptance testing phases specifically invite non-technical users to try preliminary outputs, flagging any functional or usability concerns. Techniques such as “voice of the customer” mapping, widely adopted since 2021 as reported by McKinsey & Company, support engineers in converting open-ended feedback into specific model tuning tasks or interface adjustments. Through collaborative review sessions, non-technical critiques become opportunities for measurable refinement—have you mapped qualitative findings this way in your own pipeline?

Cross-Functional Collaboration for AI Success

Integrating Diverse Teams for Effective AI Implementation

AI projects thrive when multiple disciplines contribute their unique expertise. Teams commonly bring together engineering talent with deep technical skills, product leaders focused on user needs, legal professionals addressing compliance requirements, marketing specialists building awareness, and community managers fostering user trust. According to a McKinsey 2022 survey, AI high performers overwhelmingly attribute success to cross-functional teams, with 74% reporting regular collaboration between technical and non-technical departments (McKinsey, The State of AI in 2022).

• Engineering integrates model development, infrastructure, and data pipelines.
• Product management defines requirements, ensuring solutions align with business goals and user demands.
• Legal teams interpret regulatory impacts and draft policies for responsible AI use.
• Marketing crafts messaging, drives adoption, and communicates benefits to both internal and external stakeholders.
• Community managers manage feedback loops and cultivate trust by enabling dialogue between users and AI teams.

When these functions operate in silos, misalignment becomes inevitable. Prompting cross-functional dialogue—such as regular sprint reviews, joint requirement sessions, and shared documentation—breaks down barriers, surfaces unseen risks, and unlocks hidden opportunities.

Collaboration Tools and Platforms Supporting Distributed Teams

Advanced collaboration platforms underpin distributed teamwork. Tools like Slack, Microsoft Teams, and Asana connect globally dispersed contributors in real time. Gartner's 2023 Market Guide for AI Collaboration Platforms identifies integrated workspaces as essential for effective AI delivery, with 69% of AI teams using at least two dedicated platforms for coordination (source).

• Slack and Teams support persistent cross-functional conversations, searchable by topic and project.
• Atlassian's Jira orchestrates sprint planning, bug tracking, and transparent status reporting.
• Miro and Figma enable distributed teams to brainstorm, design, and iterate on user interfaces or logic flows together, regardless of geography.

Interactive dashboards further increase transparency, enabling stakeholders to monitor project performance, deployment metrics, or compliance checks at a glance. Which collaboration tool does your team rely on to manage cross-functional AI initiatives?

Structuring Tasks to Maximize Knowledge and Skill Sharing

Assigning workstreams to intentionally blend disciplines promotes learning and accelerates results. Multidisciplinary task forces or pods tackle complex subjects such as ethical reviews or model explainability, combining project management methods—like Agile or Kanban—with detailed knowledge transfer sessions.

• Rotating task ownership exposes team members to new skills and responsibilities.
• Cross-training workshops educate non-technical stakeholders about AI fundamentals and keep technical staff informed about regulatory or customer-facing considerations.
• Shared knowledge bases document decisions, rationales, and lessons learned for future reference and onboarding.

Reflect on your last AI project. How did cross-functional collaboration influence the outcome? Which new perspectives shifted your team’s approach? The answers shape future strategies and highlight the lasting business value that multidisciplinary integration can unlock.

Addressing Stakeholder Concerns about AI Risks

Common Concerns: Bias, Job Displacement, Privacy, and Security

Stakeholders frequently cite AI bias among their top worries. Empirical assessments reveal that algorithmic bias can impact outcomes in areas such as hiring, lending, and law enforcement. For example, a 2019 MIT Technology Review analysis highlighted how facial recognition systems from leading vendors misidentified darker-skinned women at rates of up to 34.7%, compared to less than 1% for lighter-skinned men. Acknowledging and quantifying these discrepancies will ground conversations in measurable realities.

Concerns about job displacement run high with the adoption of automation technologies. According to a 2023 report by Goldman Sachs, generative AI has the potential to automate tasks equivalent to up to 300 million full-time jobs globally, with white-collar professions such as administrative and legal services facing pronounced effects. The magnitude and sector-specific details enable targeted workforce planning and upskilling initiatives.

Data privacy issues dominate discussions when organizations implement AI that processes personal information. For perspective, IBM's 2022 Cost of a Data Breach Report found the average global data breach cost reached $4.35 million, with AI-driven detection reducing this cost by an average of $300,000. Specific data points help contextualize risk-reduction strategies.

Security vulnerabilities, particularly in areas involving adversarial attacks or model theft, represent another concern. Research from Gartner estimates that by 2025, 30% of all AI cyberattacks will target training data, models, and supply chains. These concrete projections illustrate the importance of robust security protocols across the AI lifecycle.

Transparent Data Points for Clarifying AI Risks

• Baked-In Bias Measurement: Deploy a practice of regular bias and fairness audits. Results from these assessments—such as specific false positive/negative rates—can be published internally and discussed publicly to address transparency.
• Job Landscape Forecasts: Communicate clear workforce impact scenarios. Show projected automation impacts in terms of percentage of roles affected, and pair this with evidence-based upskilling opportunities.
• Privacy Metrics: Provide stakeholders with statistics on privacy breach reductions after implementing AI-enabled anomaly detection.
• Security Benchmarks: Use quantifiable security testing outcomes to show which attacks systems have mitigated—and back this up with regularly updated security certification status.

Community Forums and Interactive Conversation Channels

How do organizations keep the dialogue alive and accessible? Hosting live, moderated community forums gives stakeholders a platform to voice concerns. Initiate feedback cycles where users submit questions through dedicated AI risk communication channels; many firms report high engagement rates when using town halls and Q&A webinars.

Some organizations—like Google’s PAI (Partnership on AI)—have established open dialogue forums that invite both critical discussion from stakeholders and transparent responses from technical leaders. When did you last participate in such a discussion, or review feedback from your own teams? Interactive mechanisms such as surveys, anonymous feedback forms, and regular public briefings create a continuous feedback loop.

An AI project that incorporates input from affected communities using visible, accessible channels identifies and resolves emerging risks early. Stakeholders will engage more actively when they see their questions answered with factual evidence and real-world data.

Ensuring Transparency in AI Processes

Open-Source Tools and Documentation Practices

Open-source tools lay the groundwork for transparent AI development. Frameworks such as TensorFlow and PyTorch offer public code repositories, version histories, and open-access discussions. These platforms document not just the code, but also architectural choices and the evolution of model parameters. Stakeholders gain direct insight into how algorithms function, what datasets contribute to training, and which biases have been addressed. Interactive Jupyter Notebooks, widely shared via platforms like GitHub, deliver step-by-step execution records, allowing anyone to audit the data processing pipeline. Have you examined a model’s documentation lately to trace back its key decisions?

Making AI Decision-Making Visible and Understandable

Complex models, including neural networks and ensemble methods, often function as “black boxes.” To counter this opacity, visualization tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) break down prediction processes into comprehensive, human-readable outputs. For instance, SHAP assigns a value to each input feature, revealing which variables contributed most to a particular decision. Publishing detailed model cards—concise documents describing model intended use, performance metrics, and ethical considerations—enables non-technical stakeholders to grasp both the strengths and limitations. Reflect for a moment: Would a visual dashboard demystify model rationale in your organization?

The Role of Transparency in Long-Term Stakeholder Engagement

Transparent AI processes foster sustained involvement from diverse stakeholder groups. Regular dissemination of process updates, open access to performance audits, and the public sharing of explainability reports result in higher levels of trust and increased willingness to collaborate. In fact, a 2022 Edelman Trust Barometer survey revealed that 65% of respondents desire clear explanations for technology decisions that affect them. By maintaining a transparent environment, organizations create shared ownership, which in turn strengthens long-term partnerships. Which transparency practices do your teams currently prioritize, and which could yield greater engagement if implemented?

Unlocking Full Value: Propel AI Stakeholder Engagement to the Next Level

Building a Comprehensive Engagement Strategy Delivers Tangible Results

A robust approach to AI stakeholder engagement consistently generates superior project outcomes. Multinational organizations such as Siemens have achieved a 26% reduction in change management timeframes for AI initiatives simply by integrating systematic stakeholder analysis with structured engagement workshops (Source: Siemens Corporate Technology, 2023 Stakeholder Report). McKinsey’s 2022 survey of AI transformations across 850 enterprises revealed that teams leveraging cross-functional stakeholder panels throughout the project lifecycle improved adoption rates by 33% and reduced resistance-related bottlenecks by nearly 40% (Source: McKinsey Global AI Survey 2022).

Reflect on the last AI project you managed or participated in. Which stakeholders contributed insights that shifted the direction or success of the deployment? What strategies for dialogue and collaboration proved most effective? Consider how stakeholder-driven feedback mechanisms led to measurable system improvements or, in some cases, prevented implementation setbacks.

Take the Initiative: Enhance Your Engagement Model Now

• Map your ecosystem—identify technical, operational, and community voices.
• Schedule focused stakeholder consultations at regular milestones, not merely at project launch.
• Design digital platforms for continuous feedback. DXC Technology, for example, leverages Slack channels and monthly roundtables to capture stakeholder sentiment and real-time queries, resulting in a 22% uptick in user satisfaction with deployed AI tools (Source: DXC Annual Digital Report, 2023).
• Encourage open, two-way conversation—ask: “What is your top concern or insight regarding our AI project this month?”

Which step in this process would generate the most impact in your context? Who can you involve that you haven’t yet considered?

Connect with the Broader Community for Lasting Success

Join industry-specific forums and professional AI consortia. The Partnership on AI and IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems provide active online communities, case studies, and events where concrete best practices are shared and debated.

Choose a resource, engage with peers, and implement a single, new stakeholder engagement tactic within your next AI cycle. Observe the measurable difference these connections make.