In his book “Homo Deus,” Yuval Noah Harari contends that “organizations are algorithms” – processing information through various divisions, departments, and hierarchies to deliver decisions, with their employees and teams working as information-processing agents. As businesses leverage AI and digital to transform into intelligent enterprises it is important that they also reexamine their structures and operating models.
The reason for this is that an enterprise’s “intelligence quotient” depends on its ability to optimally organize and use its information-processing agents (people). Additionally, the infusion of AI will transform how individuals and teams work, process information, and make decisions.
Elevating employee roles and responsibilities
Human knowledge gathered over decades is being encapsulated into numerous mathematical algorithms leading to predictive, prescriptive, image recognition, language-understanding, and even content-generating applications. As these applications start augmenting and, in some situations, automating organizational decision processes, the roles of employees and management will be revolutionized.
For example, a chemical waste-management company initially depended on experts with years of experience to decide whether to recycle a waste product or not. The company used machine learning algorithms to capture the experts’ knowledge, enabling the company to make faster and more consistent decisions, ultimately freeing employees to perform more value-adding activities.
As AI takes up the bulk of information-processing responsibilities, employees’ role will elevate from executor to creator. New jobs requiring human-level intelligence will surface, with employees reviewing the decisions recommended by these algorithms and providing feedback to improve the company’s knowledge base and enhance its enterprise IQ.
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Breaking silos with data democratization
The success of AI is leading to more companies creating a centralized knowledge hub. When AI algorithms can analyze this centralized data, digital copies of processes like supply chain and manufacturing systems, can be created. These digital twins provide an integrated view of cross-functional processes and dependencies, leading to optimized decisions through simulations and more. This leads to significant cost savings and revenue growth.
For example, an organization with a digital supply chain twin can easily find alternative, cost-effective suppliers for raw materials during supply disruptions. The digital twin can quickly simulate different demand scenarios and identify the optimal inventory levels to minimize lost sales and inventory cost. In manufacturing, the plant operator can make decisions on the optimal machine configuration for handling production quality issues.
With digital twins enabled by data democratization, different units of a process or division will evolve into a cross-functional team. This evolution will resolve the traditional cross-departmental delays and the conflicting priorities of different teams. The fusion of teams, goals, and data access will lead to faster, better decisions and accelerate the journey towards becoming an intelligent enterprise.
Marrying AI with newer operating models for developing intelligent organizations
Markets are continually disrupted by new ideas, with digital and AI driving many of them. Traditional organizations are falling behind as newer companies adopt structures and operating models that are more relevant in the era of digital, AI, and the millennial workforce – rather than focusing on building scale in teams and driving efficiency through SOPs and centralized functions. To adapt and succeed, traditional companies must follow suit and focus on re-optimizing their “information processing architecture” and adopting AI technologies to be more aligned to the needs of an AI-enabled digital organization.
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The real recipe for intelligent enterprises
However, merely adopting digital and AI technologies isn’t enough to qualify as an intelligent enterprise. Adapting the following critical levers enables enterprises to drive change and adjust to new ways of working in the digital and AI era.
- AI CoE: AI infusion is foundational to becoming an intelligent enterprise. Establishing a center of excellence for AI is the leading strategy adopted by organizations early in their intelligent enterprise journey. It acts as a catalyst in driving data-driven intelligence into every process.
- Knowledge workers: As AI becomes mainstream, companies will need to shift their organizational talent strategy. An AI-infused decision system will require human creators, problem solvers, thinkers and independent workers. The focus needs to be more on developing specialized knowledge-workers and niche talent, instead of efficient executors.
- Multidisciplinary teams: Data democratization and AI are breaking internal silos, and multidisciplinary teams are becoming more effective in innovation and decision making. These changes lead to the creation of project-specific, purpose-built, and multidisciplinary teams focused on problem-solving and innovation.
- Distributed decisions: Purpose-built teams with talented knowledge workers enabled by AI technologies shouldn’t be constrained by a top-down, bureaucratic, and centralized decision-making process. Instead, organizations should adopt a distributed decision-making system to empower all multidisciplinary teams.
- New measurements: Productivity measures will need to evolve in an intelligent enterprise. Companies will need to measure the extent of AI adoption throughout the organization. Assessing the new knowledge-workers in this way should focus on improved collaboration and innovation and move away from task execution.
Since the 1980’s, the Fortune 500 has seen a complete metamorphosis. Anecdotally, organizations that have embraced technology as a competitive enabler have enjoyed longer tenures on the list as opposed to those that haven’t. In an era where organizations themselves are embodiments of algorithms, it stands to reason that those that become Intelligent Enterprises better insulate themselves against the next wave of corporate obsolesce.
However, to achieve the vision of an Intelligent Enterprise faster, businesses must focus on their information processing agents: employees, teams, and the organization. A 360-degree approach, incorporating talent transformation, team restructuring and a new measurement framework, with AI at its helm – will elevate an enterprise’s IQ, leading to optimized performance for the future.
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Vivek Karmakar, Consulting Partner, Data Science, Wipro