Eight separate studies reveal that individuals who seek advice using technology like ChatGPT are often perceived as less warm or approachable. This perception may impact decisions related to hiring, feedback, and the overall acceptance of AI tools in the workplace.

Otto Dettmer/Ikon Images

Imagine you're an operations manager at a global retailer who has invested heavily in data science. Your team produces highly accurate demand forecasts, yet when leadership asks how much inventory to hold and where, the answers are vague. This disconnect between data predictions and actionable business decisions illustrates a common issue: data models often don’t address the real business problems.

Studies show 60-85% of data science projects fail—not due to poor algorithms, but because data scientists and managers often speak different languages. Data scientists focus on prediction accuracy, while managers need clear guidance on decisions that impact costs, profits, and market share. This gap leaves many valuable insights unused.

Decision science offers a solution by modeling the actual business decisions managers face, aligning analytics directly with business objectives. Instead of just predicting outcomes, decision science translates data into actionable strategies, ensuring analytics deliver measurable value.

Data science excels in prediction and machine learning, but decision science emphasizes problem framing, business knowledge, and decision modeling. Combining these disciplines strengthens problem definition and encourages collaboration between analysts and business leaders.

For example, forecasting product demand is useful, but managers must decide production quantities and inventory levels based on profit contribution, costs, and capacity constraints. Decision modeling incorporates these factors and provides recommendations or scenarios that balance risk and return.

Decision science also helps avoid the “convenient data” trap, encouraging teams to gather the data that truly addresses the decision problem, not just what’s easiest to access. This approach fosters metrics aligned with business goals, like financial impact, instead of generic accuracy scores.

To maximize ROI from analytics, organizations should start projects with a clear decision-focused brief, develop enterprise digital twins to simulate business scenarios, insist on actionable recommendations—not just predictions—and build teams blending data science with decision science expertise. Leadership that bridges technical insights with business decisions is essential.

Ultimately, organizations must shift from being data-driven to decision-driven, ensuring analytics translate into better business choices and stronger outcomes.

Anthropic mentioned that it has reduced hiring for junior technical roles as AI agents are now actively used throughout the company, impacting staffing needs.

From 2023 to 2025, the percentage of S&P 500 companies identifying AI as a risk surged from 12% to 83%. Yet, fewer than 3% of directors possess AI expertise, and only 23% of executives believe their boards are highly fluent in AI. As AI integrates deeply across business functions, boards face a critical knowledge and oversight gap that must be addressed. While recruiting AI experts is important, it’s not enough since AI touches all areas of business and requires distributed oversight among all directors, not just specialists. Moreover, expertise in AI doesn't guarantee effective oversight; true oversight means actively managing AI risks and accountability.

Boards must focus on key AI oversight areas such as governance accountability, data transparency, explanation and disclosure, fairness, risk management, human judgment, and autonomy controls. Good oversight means measuring what directors actually do, not just what they know, ensuring every director meets a minimum competency floor across all oversight areas. Depth should be spread across the entire board with at least two directors proficient in each AI domain to avoid blind spots. Ultimately, possessing AI expertise is valuable, but it is the board’s actions in diligent AI oversight that safeguard the company’s future.

For directors wanting to assess their AI oversight capabilities, participation in ongoing research and scorecards is encouraged to benchmark and improve their effectiveness.

Despite concerns that AI could reduce tech jobs, data reveals a steady increase in tech job openings. This growth is driven by rising AI-related spending, which boosts demand for hardware and sustains strong hiring in software development roles.

A senior executive at Amazon Web Services revealed that the company is highly selective when recruiting forward-deployed engineers, emphasizing that such talent is exceptionally rare and difficult to find.

Jay Clayton, currently the director of national intelligence and leader of the US's 18 intelligence agencies, has been appointed by Donald Trump as the new White House AI czar. During his confirmation, Clayton described AI as both a 'gamechanger' and a potential 'threat.' Clayton has a background as the top federal prosecutor in Manhattan and previously led the Securities and Exchange Commission under Trump's first term.

One common but costly misconception about AI is the belief that technology can fix a business without first improving its underlying processes. While AI can speed up operations, automate routine tasks, and quickly identify issues, it cannot remedy fundamentally flawed or fragmented workflows. True business transformation requires streamlining processes before AI can truly add value.

Forget the endless search for talent. Focus on cultivating and developing your own workforce.

California, a hub for numerous AI firms, is pioneering new workplace laws to protect employees from potential AI-related job threats. These regulations prevent employers from solely relying on AI for firing decisions, using AI to monitor workers' emotional states, or collecting neural data from employees. Additionally, companies must inform workers if AI contributed to layoffs, and AI surveillance is banned in workplace bathrooms. Signed by Governor Gavin Newsom, these measures aim to foster safer, fairer work environments amidst growing calls for AI regulation in the U.S.