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As we embrace the transformative potential of autonomous technology, AI agents are increasingly redefining business operations and value creation. However, with a multitude of vendors promoting various “AI agents,” discerning their true capabilities and optimal applications becomes crucial.
Understanding AI’s role goes beyond merely compiling a list of tasks suitable for automation and then assessing the performance of AI agents against these benchmarks. For instance, while a jet can outpace a car, it may not be the appropriate choice for a regular trip to the grocery store.
Why Attempting to Replace Work with AI Agents is Misguided
Every organization aims to create value for its stakeholders, including customers, partners, and employees.
This value represents only a portion of the total potential value that the organization could generate — known as total addressable value creation — which is the full range of value that stakeholders would accept.
When employees leave their workdays with extensive task lists to be tackled the following day, alongside items that could be deprioritized but would still yield value if completed, it creates a situation where value creation is suboptimal, often resulting in missed opportunities.
A practical starting point for utilizing AI agents involves assessing current operations and the value they already generate. This approach simplifies initial estimations, allowing organizations to evaluate existing value and identify chances to enhance efficiency or reliability in value delivery.
While this method is valuable during the transformation process, many organizations falter when they focus solely on existing value generation without exploring new possibilities. Such a limited perspective constrains investments and leaves a substantial amount of potential value untapped.
Recognizing that humans and machines have distinct strengths and weaknesses, organizations that focus on collaborative innovation with their technology and industry partners are likely to outperform those that exclusively emphasize achieving higher automation levels without increasing overall value output.
Deciphering AI Agent Functions via the SPAR Framework
We developed the SPAR framework—comprising sensing, planning, acting, and reflecting—to clarify how AI agents function. This framework aligns with human goal achievement and provides a clear understanding of AI agent operations.
Sensing: AI agents, akin to humans using senses, gather information from their surroundings. They monitor triggers, collect relevant data, and keep track of their environment.
Planning: After gathering environmental signals, AI agents carefully assess their options before acting. They synthesize the information based on their objectives, akin to human decision-making processes.
Acting: What distinguishes AI agents from basic analytical tools is their ability to perform tasks. They can coordinate various tools and systems, execute actions, and adjust in real time to maintain alignment with their goals.
Reflecting: This advanced capability enables AI agents to learn from past experiences. They can evaluate their effectiveness, analyze results, and refine their strategies based on successful methods, fostering an ongoing cycle of improvement.
The synergy among these four functions creates a powerful framework where AI agents can approach complex objectives with increasing sophistication. This exploratory capacity stands in contrast to existing processes that have been repeatedly optimized through digital transformation, which may yield only minor short-term enhancements. Instead, innovating value creation methods and developing new market opportunities can lead to significant and sustained growth.
Five Steps to Develop Your AI Agent Strategy
Many business leaders and technologists often adhere to a conventional method when implementing AI, contributing to an 87% failure rate:
Create a problem list;
or
Assess available data;
Identify potential use cases;
Evaluate use cases for ROI, feasibility, cost, and timelines;
Select a limited number of use cases and proceed with execution.
Even though this approach might seem reasonable and is commonly perceived as best practice, statistics indicate its shortcomings. A new strategy is necessary.
Begin by mapping out the total potential value creation that your organization could offer to its customers and partners, taking into account your core competencies as well as market regulations and geopolitical factors.
Evaluate your organization’s current value creation efforts.
Identify the top five most valuable and opportunity-rich initiatives for your organization to generate new value.
Analyze these opportunities for ROI, feasibility, cost, and timelines to develop viable AI agent solutions (repeat the analysis as necessary).
Finally, choose a selection of these value propositions and commit to executing them.
Generating New Value Through AI
Transitioning into an age defined by autonomous transformation, where systems consistently create value, is not a rapid endeavor; it requires a systematic approach that develops organizational capabilities in tandem with technological advancements. By methodically identifying value and scaling ambitions, organizations can effectively position themselves for success in the evolving landscape shaped by AI agents.
Brian Evergreen is the author of Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence.
Pascal Bornet is the author of Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work and Life.
Evergreen and Bornet are co-teaching a new online course on AI agents with Cassie Kozyrkov: Agentic Artificial Intelligence for Leaders.
Source
venturebeat.com