Reframing Organizations: Artistry, Choice, and Leadership offers a unique available at: ronaldweinland.info%20materials/Quality%ronaldweinland.info Reframing Organisations. Bolman & Deal identify four distinctive 'frames' from which people view their world -. Structural, Human Resources,. Political, and. Reframing Organizations: Artistry, Choice, and Leadership (3rd ed.) by Lee G. Bolman. Read online, or download in secure PDF format.
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Reframing Organizations provides time-tested guidance for more effective organizational leadership. Rooted in decades of social science. Fourth Edition Reframing Organizations Artistry, Choice, and Leadership LEE G. BOLMAN Available online: ronaldweinland.info Access. Reframing Organizations is written for present and future leaders and managers —those who envision themselves actively engaged in the struggles to tame and.
Explore the Archive Executive Summary Artificial intelligence seems to be on the brink of a boom. Yet companies are struggling to scale up their AI efforts. Most have run only ad hoc projects or applied AI in just a single business process. In surveys of thousands of executives and work with hundreds of clients, McKinsey has identified how firms can capture the full AI opportunity. The key is to understand the organizational and cultural barriers AI initiatives face and work to lower them. That means shifting workers away from traditional mindsets, like relying on top-down decision making, which often run counter to those needed for AI.
The hub. A small handful of responsibilities are always best handled by a hub and led by the chief analytics or chief data officer. These include data governance, AI recruiting and training strategy, and work with third-party providers of data and AI services and software.
Hubs should nurture AI talent, create communities where AI experts can share best practices, and lay out processes for AI development across the organization. Our research shows that companies that have implemented AI on a large scale are three times as likely as their peers to have a hub and 2.
Hubs should also be responsible for systems and standards related to AI. In contrast, when a European bank found that conflicting data-management strategies were hindering its development of new AI tools, it took a slower approach, making a plan to unify its data architecture and management over the next four years as it built various business cases for its AI transformation.
The spokes. Among them are tasks related to adoption, including end-user training, workflow redesign, incentive programs, performance management, and impact tracking. A few tasks are always owned by the hub, and the spokes always own execution. The gray area. Much of the work in successful AI transformations falls into a gray area in terms of responsibility. Deciding where responsibility should lie within an organization is not an exact science, but it should be influenced by three factors: The maturity of AI capabilities.
When a company is early in its AI journey, it often makes sense for analytics executives, data scientists, data engineers, user interface designers, visualization specialists who graphically interpret analytics findings, and the like to sit within a hub and be deployed as needed to the spokes.
But as time passes and processes become standardized, these experts can reside within the spokes just as or more effectively. Business model complexity. The greater the number of business functions, lines of business, or geographies AI tools will support, the greater the need to build guilds of AI experts of, say, data scientists or designers.
Companies with complex businesses often consolidate these guilds in the hub and then assign them out as needed to business units, functions, or geographies. The pace and level of technical innovation required. When they need to innovate rapidly, some companies put more gray-area strategy and capability building in the hub, so they can monitor industry and technology changes better and quickly deploy AI resources to head off competitive challenges.
Both faced competitive pressures that required rapid innovation.
However, their analytics maturity and business complexity differed. The institution that placed its analytics teams within its hub had a much more complex business model and relatively low AI maturity.
Its existing AI expertise was primarily in risk management. By concentrating its data scientists, engineers, and many other gray-area experts within the hub, the company ensured that all business units and functions could rapidly access essential know-how when needed.
The second financial institution had a much simpler business model that involved specializing in fewer financial services.
This bank also had substantial AI experience and expertise. So it was able to decentralize its AI talent, embedding many of its gray-area analytics, strategy, and technology experts within the business-unit spokes. As these examples suggest, some art is involved in deciding where responsibilities should live.
Every organization has distinctive capabilities and competitive pressures, and the three key factors must be considered in totality, rather than individually.
For example, an organization might have high business complexity and need very rapid innovation suggesting it should shift more responsibilities to the hub but also have very mature AI capabilities suggesting it should move them to the spokes.
Its leaders would have to weigh the relative importance of all three factors to determine where, on balance, talent would most effectively be deployed. Talent levels an element of AI maturity often have an outsize influence on the decision. Does the organization have enough data experts that, if it moved them permanently to the spokes, it could still fill the needs of all business units, functions, and geographies?
If not, it would probably be better to house them in the hub and share them throughout the organization.
Oversight and execution. While the distribution of AI and analytics responsibilities varies from one organization to the next, those that scale up AI have two things in common: A governing coalition of business, IT, and analytics leaders.
Fully integrating AI is a long journey. Creating a joint task force to oversee it will ensure that the three functions collaborate and share accountability, regardless of how roles and responsibilities are divided.
This group, which is often convened by the chief analytics officer, can also be instrumental in building momentum for AI initiatives, especially early on.
Assignment-based execution teams. Organizations that scale up AI are twice as likely to set up interdisciplinary teams within the spokes. Such teams bring a diversity of perspectives together and solicit input from frontline staff as they build, deploy, and monitor new AI capabilities. The teams are usually assembled at the outset of each initiative and draw skills from both the hub and the spokes. These teams address implementation issues early and extract value faster.
Some art is involved in deciding where AI responsibilities and roles should live. For example, at the Asian Pacific retailer that was using AI to optimize store space and inventory placement, an interdisciplinary execution team helped break down walls between merchandisers who determined how items would be displayed in stores and downloaders who chose the range of products.
Previously, each group had worked independently, with the downloaders altering the AI recommendations as they saw fit. That led to a mismatch between inventory downloadd and space available. By inviting both groups to collaborate on the further development of the AI tool, the team created a more effective model that provided a range of weighted options to the downloaders, who could then choose the best ones with input from the merchandisers. Educating Everyone To ensure the adoption of AI, companies need to educate everyone, from the top leaders down.
To this end some are launching internal AI academies, which typically incorporate classroom work online or in person , workshops, on-the-job training, and even site visits to experienced industry peers. Most academies initially hire external faculty to write the curricula and deliver training, but they also usually put in place processes to build in-house capabilities. Every academy is different, but most offer four broad types of instruction: Leadership.
Most academies strive to give senior executives and business-unit leaders a high-level understanding of how AI works and ways to identify and prioritize AI opportunities.
Here the focus is on constantly sharpening the hard and soft skills of data scientists, engineers, architects, and other employees who are responsible for data analytics, data governance, and building the AI solutions. Google Scholar Kauffman, S. Humanity in a creative universe.
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Biology and Philosophy, 29 1 , — The nature of hierarchical controls in living matter. Widely published on the intersection of leadership and organizations, Bolman is a sought-after consultant for corporations, public agencies, and educational institutions in the United States and around the world.
Having served on the faculty of some of the top U. The Power of Reframing Pages: Opportunities and Perils Pages: Change and Leadership in Action Pages: Artistry, Choice, and Leadership Pages: The Best of Organizational Studies Pages: Your password has been changed. Please check your email for instructions on resetting your password. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username.
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