• This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1)
• Also, responses suggest that companies are now using AI in more parts of the business.
• . Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
• espondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives
• Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
• The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6)
• For analytical AI, respondents most often report seeing cost benefits in service operations—
• they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

• Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

   
• just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
• We have found three archetypes for implementing gen AI solutions: takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch
• their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function
• Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI.
• gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
• For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI
• Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—
• Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value

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• In many organizations, HR functions are divided into distinct areas like recruitment, benefits, payroll and employee relations. This traditional North-South siloed approach means each functional area deploys its own strategy and tools to achieve specific goals
• it often leads to a disjointed experience for employees
• East-West thinking encourages collaboration across these functional silos within HR. This approach emphasizes creating a cohesive employee experience by integrating strategies and tools across all HR functions. For example, when an employee has a query, an East-West-aligned HR function ensures that whether the question pertains to payroll, benefits or career development, the response is seamless and consistent.
• In the context of chatbot implementation, this might mean developing highly specialized bots for different functions without considering the holistic employee experience.
• Generative AI plays a pivotal role here. By designing chatbots that can understand and respond to natural language queries, we can create an interaction experience that feels more human and less robotic.
• First, it allows for more personalized interactions. A generative AI-powered bot can remember past interactions, anticipate future needs,
• generative AI enables the creation of more sophisticated and context-aware responses.
• Good data ensures that chatbots can provide accurate, relevant and timely information. It also enables these bots to learn and improve over time
• the old-school intranets and portals—often referred to as link farms—are becoming obsolete.
• This new approach prioritizes the needs and experiences of employees, ensuring that technology serves as an enabler rather than a barrier.
• An integrated experience architecture involves breaking down both east-west and north-south silos. Cross-functional collaboration is key. HR, IT and other departments must work together to ensure that chatbot solutions are aligned and interconnected

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• work was fundamentally broken and to stay ahead of curve small changes won’t be sufficient.
• Slack found that AI use by desk workers is up 23% since January and 60% since September, and they’re reaping the rewards.

• Their work-life balance was 18% higher, they were 29% more likely to feel highly passionate about work and were 23% better at managing stress.

   

• Ultimately, they were 24% more satisfied at work.

   
• two in three workers have still not used AI at work.

• This is because of trust issues – only 7% see AI as being completely trustworthy for work – and because only 15% think they’ve had enough education and training to confidently use AI effectively at work.

   
• 37% of desk workers told Slack that their employer has no AI policy, but when workers understand how and when to use AI, they are 6x more likely to have experimented with AI at work.
• work was fundamentally broken and to stay ahead of curve small changes won’t be sufficient.
• desk workers estimate they spend a third of their day on low value work,
• The ideal situation is that AI minimizes these busy work tasks, and that enables people to focus on more meaningful work,
• hen Slack asked workers what they would prioritize with the time they got back from AI, ‘more admin’ topped the list, while innovation, learning and skills fell to the bottom.

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• Traditional operations typically rely on manual processes and basic automation, where workflows are mapped and executed with limited real-time data insights. This often leads to inefficiencies and reactive problem-solving.
• An ecosystem that combines various digital accelerators, such as RPA, process mining, chatbots and advanced analytics enhances a model of intelligent operations.
• a digital operations accelerator is considered any type of technology that adds capability without being part of the operational systems
• the use of digital acceleration technology in intelligent operational models not only facilitates the incorporation of feedback at scale and speed but can also transform work methods.
• By integrating digital accelerators, companies can achieve significant improvements, such as reducing operational costs, decreasing process cycle times and enhance overall productivity.
• the outcome of process execution is always impacted by the bias between how the process was mapped and how it is actually executed, as well as its variance
• digital accelerators enable the transparent and objective description of how processes are executed.
• Identifying errors in process execution in traditional operational models is inherently reactive.
• In the context of intelligent operations, not only is it possible to transparently visualize how processes are executed in the real world, but also to simulate future scenarios
• It becomes normal to combine manual work by humans with automated tasks produced by robots, as well as virtual assistants that support front-office operations in client interactions, improving the experiences of both clients and employees.

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