Key Takeaways

• By 2026, 5% of workers will routinely use AI against their employer’s wishes to complete tasks. “The future of all work will be transformed by AI.” 

• By 2026, 20% of top data science teams will have rebranded as cognitive science or science consultancies, increasing diversity in staff skills by 800%. “Data science will change given its pervasiveness, and it will encompass more disciplines.”

• By 2026, 50% of business intelligence tools will activate their user’s metadata, offering insights and data stories with recommended contextualized journeys and actions. “D&A teams must move from dashboards to stories, using metadata to personalize the user experience.” 

• By 2027, 80% of enterprise marketers will establish a dedicated content authenticity function to combat misinformation and fake material. “AI technology is progressing much faster than people or organizations are able to absorb its implications and capabilities. Responsible AI must be a priority.”

• By 2026, more than a quarter of Fortune 500 CDAOs will have become responsible for at least one data and analytics-based product that becomes a top earner. “CDAOs can make a positive impact on the performance of the organization and strengthen their own position by joining the Chief Digital Officer in driving digital product revenue growth.”

• By 2026, environmental sustainability will be a key criterion in over 60% of data management initiatives, supported by financial governance practices. “Within the scope of a cloud ecosystem, environmental sustainability initiatives have a direct link to cost management outcomes.”

Key Takeaways

• “Regulatory compliance is the primary reason why privacy, security and/or risk are barriers to AI implementation.” 

• “Breaches and hacks against AI are plentiful and are rendered in many variations. Organizations must take compromises by insiders as well as outsiders seriously.”

• “Collective organizational management of AI privacy, security and risk results in improved AI business outcomes.”

• “When it comes to tackling new AI risks, data and analytics leaders need to get organized and set up their priorities for AI trust, risk and security management (AI TRiSM).” 

• The top 5 priorities for AI TRiSM are:

  1. AI Inventory: Capture the extent of exposure by inventorying AI used in the organization and ensure the right level of explainability. 

  2. AI Risk Awareness: Drive staff awareness across the organization by leading a formal AI risk education campaign.

  3. Privacy and Data Protection: Eliminate exposures of internal and shared AI data by adopting data protection and privacy programs.

  4. Robust ModelOps: Support model reliability, trustworthiness and security by incorporating risk management in model operations.  

  5. AI Security and Resilience: Adopt specific AI security measures against adversarial attacks to ensure resistance and resilience. 

• By 2026, organizations that operationalize AI transparency, trust and security will see their AI models achieve a 50% result improvement in terms of adoption, business goals and user acceptance. 

• “Organizational peers with vested interest in AI must work together to manage AI privacy, security and risk.”

Key Takeaways

• There are four steps to articulating D&A value:        

     1. Tying data pain points and opportunities to business goals

     2. Identifying the potential value of a data improvement

     3. Measuring the value of a D&A investment

     4. Visualizing and articulating the value to business leaders 

• “Making the case for data investments should focus on showing the improvements to the decisions that will drive strategic goals.” 

• “Creating a data-driven culture is not just the execution of a grand information architecture transformation; it is a set of minor, gradual changes to how staff make decisions.” 

• D&A teams should measure four altitudes of improvements to how their organization uses data to get value: Outcome, decisions, D&A initiatives and data measures.

• “D&A initiatives should aim to improve the inputs and results of better analysis and decision-making, including improving data, data management and data literacy.”