Art Reframing Organizations Pdf


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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: Access. Reframing Organizations is written for present and future leaders and managers —those who envision themselves actively engaged in the struggles to tame and.

Reframing Organizations Pdf

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Reframing Organizations: Artistry, Choice, and Leadership offers a unique available at: 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.

Metaorganisms as the new frontier. Zoology, 4 , — The process dynamics of normative function. The Monist, 85 1 , 3— A symbiotic view of life: We have never been individuals. The Quarterly Review of Biology, 87 4 , — New York: Oxford University Press. Google Scholar Kauffman, S. Humanity in a creative universe.

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Building the AI-Powered Organization

Journal of Theoretical Biology, 1 , — Biological organisation as closure of constraints. Journal of Theoretical Biology, , — Biological autonomy: A philosophical and theoretical enquiry.

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The problem of the emergence of functional diversity in prebiotic evolution. Biology and Philosophy, 24 5 , — What makes biological organisation teleological? Synthese, 4 , — An organizational account of biological functions. The British Journal for the Philosophy of Science, 60 4 , — Function in ecology: An organizational approach. Biology and Philosophy, 29 1 , — The nature of hierarchical controls in living matter. Rosen Ed. Volume 1 - Subcellular systems pp.

For example, to achieve a view of customers detailed enough to allow AI to do microsegmentation, a company might need to set up a number of sales and marketing initiatives. Some, such as targeted offers, might deliver value in a few months, while it might take 12 to 18 months for the entire suite of capabilities to achieve full impact.

Part one produced an AI tool that gave store managers recommendations for a few incremental items that would sell well in their outlets. Companies with good scaling practices spent half their analytics budgets on adoption.

One consolidated its AI and analytics teams in a central hub, with all analytics staff reporting to the chief data and analytics officer and being deployed to business units as needed. The second decentralized nearly all its analytics talent, having teams reside in and report to the business units.

Both firms developed AI on a scale at the top of their industry; the second organization grew from 30 to profitable AI initiatives in just two years. The hub.

Setting Up for Success

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.

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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 buyers who chose the range of products.

Previously, each group had worked independently, with the buyers altering the AI recommendations as they saw fit. That led to a mismatch between inventory purchased 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 buyers, 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.

Analytics translators often come from the business staff and need fundamental technical training—for instance, in how to apply analytical approaches to business problems and develop AI use cases.

What makes programs go off track? Companies set themselves up to fail when: They lack a clear understanding of advanced analytics, staffing up with data scientists, engineers, and other key players without realizing how advanced and traditional analytics differ. They have no strategy beyond a few use cases, tackling AI in an ad hoc way without considering the big-picture opportunities and threats AI presents in their industry. They isolate analytics from the business, rigidly centralizing it or locking it in poorly coordinated silos, rather than organizing it in ways that allow analytics and business experts to work closely together.

Reframing Organizations (3rd ed.)

They squander time and money on enterprisewide data cleaning instead of aligning data consolidation and cleanup with their most valuable use cases. They fail to focus on ethical, social, and regulatory implications, leaving themselves vulnerable to potential missteps when it comes to data acquisition and use, algorithmic bias, and other risks, and exposing themselves to social and legal consequences.

End user. Frontline workers may need only a general introduction to new AI tools, followed by on-the-job training and coaching in how to use them. Strategic decision makers, such as marketers and finance staff, may require higher-level training sessions that incorporate real business scenarios in which new tools improve decisions about, say, product launches.

Reinforcing the Change Most AI transformations take 18 to 36 months to complete, with some taking as long as five years.

To prevent them from losing momentum, leaders need to do four things: Walk the talk. Role modeling is essential.

[PDF] Reframing Organizations: Artistry, Choice and Leadership Full Colection

For starters, leaders can demonstrate their commitment to AI by attending academy training. But they also must actively encourage new ways of working. When that happens, leaders should highlight what was learned from the pilots. That will help encourage appropriate risk taking. They ask questions and reinforce the value of diverse perspectives.

At every meeting she goes to, she invites attendees to share their experience and opinions—and offers hers last. Make businesses accountable. Sometimes organizations assign different owners at different points in the development life cycle for instance, for proof of value, deployment, and scaling.Firms struggle to move from the pilots to companywide programs—and from a focus on discrete business problems, such as improved customer segmentation, to big business challenges, like optimizing the entire customer journey.

Most firms have run only ad hoc pilots or are applying AI in just a single business process. Biological regulation: Controlling the system from within. The teams are usually assembled at the outset of each initiative and draw skills from both the hub and the spokes. Companies set themselves up to fail when: They lack a clear understanding of advanced analytics, staffing up with data scientists, engineers, and other key players without realizing how advanced and traditional analytics differ.

Frontline workers may need only a general introduction to new AI tools, followed by on-the-job training and coaching in how to use them.

But they also must actively encourage new ways of working.