TL;DR: AI-powered business rhythms are set to fundamentally transform how businesses orchestrate and manage productivity.

The challenge of efficiently converting strategy to execution is a decades-old problem. However, recent developments in AI have raised the ceiling for what's possible, positioning us on the cusp of several unlocks for business operations (BizOps) teams and their organizations.

Understanding Operational Rhythms

Operational rhythms are the backbone of organizational productivity. They enable the efficient orchestration of key business activities, rituals, cadences, and events, fostering alignment, agility, and accountability around an organization's critical priorities.

Operational rhythms are the consistent regular activities every organization undertakes such as weekly or monthly reviews, cross-functional meetings, quarterly planning sessions, and OKR check-ins. These rhythms provide a predictable cadence, helping teams and individuals establish clear time-bound expectations for their work.

The Ceiling Has Been Raised With AI

Large Language Models (LLMs) have removed several historically painful workflows for BizOps teams and created new opportunities for impact and value that were previously unattainable.

Here's how;

Traversing Data Synthesis

Organizations use dozens of SaaS tools, and LLMs can now analyze data across these multiple tools, summarizing progress, identifying anomalies or bottlenecks, and providing key insights or recommendations. It's not just about which KPIs and metrics have changed; understanding the context and nuances behind our results is crucial for making informed decisions moving forward. For instance, why did we win a new customer? Why did that customer choose to expand? Why did they (really) churn? This context often exists within unstructured data, which has traditionally been time-consuming and complex to source and synthesize manually.

Automation & Agents
AI-driven workflows can enhance business rhythms by scheduling meetings, sending reminders, summarizing discussions, and recording action items. Agentic workflows can persist and continue to follow up on action items before the next review. Owners and stakeholders can automate processes such as report generation, sharing progress updates, and aggregating context from various enterprise productivity tools. This automation streamlines business execution and significantly improves agility. Moreover, why shouldn't an AI agent handle simple tasks in Asana or manage a Jira ticket?

Shifting to ‘Verification’ vs. ‘Creation’

In most SaaS platforms today, users create dashboards and reports, which are then refreshed upon access. However, there is often a lack of trust or alignment in the measurement and calculation of the data presented, and there is limited context or commentary attached. We are shifting towards a model where 'execution observability’ will occur in a more continuous fashion. Dashboards and reports will remain 'always on' and include data aggregated from both unstructured and structured tools. This data can be matched or associated with corresponding KPIs, or goals. Citation of source data is readily available for further inspection. This approach enables anyone in the organization to explore any team, metric, or initiative to understand progress, performance, and status in the correct context, all in a self-serve manner.

Next Best Actions & Recommendations

By pairing current performance with the context of an organization’s future objectives and goals, LLMs can recommend ways to achieve desired outcomes more quickly. AI can provide tailored, personalized next-best actions for individuals, teams, and the organization as a whole. 'Memory' can be stored in a knowledge base to understand firmographic data, historical performance data, hierarchy and role data, contextual data, and desired outcome data to provide tailored recommendations to individuals, teams, and organizations.

Resourcing, Prioritization, and Scenario Mapping
AI can optimize resource allocation for specific strategic initiatives by evaluating the likely outcomes of success or failure. Additionally, AI can simulate various scenarios, enabling decision-makers to explore different strategies and assess their potential impacts.

Tailored Preferences

Why does the 'Monthly Sales Update' present the same information for everyone, despite different needs? For instance, Marketing may want to focus on lead conversion rates for the month, while Customer Success is more interested in upcoming deals to prepare for implementation. Previously, addressing these needs might have required a 60-90 minute meeting, with only 5-10 minutes disproportionally dedicated to each specific need. With AI, this constraint is eliminated, allowing content to be uniquely tailored to different audiences for consumption, asynchronously.

Wrapping Up

As AI technology continues to evolve, its integration into operational rhythms will become increasingly essential for businesses aiming to stay competitive and responsive in a dynamic market environment. The trick will be incorporating these use cases into extremely user-friendly software with the right set of workflows.

Reach out to Brev for more support or direct Q&A, we’re always here to provide guidance and support.

Brev is a SaaS company HQ'd in San Francisco