What’s Now and What’s Next in AI
AI is shifting from isolated tools to cross-departmental agent systems, putting new pressure on data readiness, governance, and how results will be measured.
During the recent AI Horizons conference, the conversation among attendees highlighted a significant shift. No longer were participants debating the integration of AI within organizations; instead, they were focused on the challenges that arise when embedding AI into real workflows.
Key Themes from the Conference
The discussions centered around data hygiene, agentic systems that span multiple departments, and the essential need for measurement of AI’s effectiveness and its correlation to ROI.
For communications leaders, the main takeaway was crystal clear: AI maturity has progressed beyond mere experimentation—it is now about being prepared to operate at scale.
Current Tension Points in AI Implementation
Experts at the conference discussed several critical points regarding the current state of AI:
- Implementation Readiness: The past year has seen significant advancements. Organizations are now prepared to ask the right questions and implement AI agents in their workflows.
- Data Hygiene Importance: The mantra “No data, no AI” resonated strongly as attendees acknowledged that ensuring clean data is a more significant challenge than anticipated.
- Legal Considerations: Legal teams are often unaware of ongoing agent workflows, which raises concerns about intellectual property and governance.
Building AI Maturity in Organizations
To cultivate AI maturity, organizations are encouraged to:
- Start with Objectives: Clearly define goals instead of implementing technology for technology’s sake.
- Conduct Data Audits: Understanding the state of data—what is clean versus dirty, and how it integrates—is crucial.
- Create Cross-Departmental Workflows: Linking agent workflows across departments will maximize impact rather than allowing them to remain siloed.
Upskilling for Effective AI Usage
To facilitate a proactive role in AI, communication professionals need to enhance their skills in:
- Technical Understanding: Familiarity with how AI functions and its capabilities is essential.
- Critical Thinking: Developing media literacy and understanding AI outputs is vital, especially in high-stakes industries.
Common pitfalls in upskilling include:
- Lack of Measurement Savvy: Professionals need to demonstrate how improved skills translate into better outcomes.
- Overwhelming Management Mindset: A “figure it out yourself” approach can hinder AI adoption and lead to frustration.
- Poor Governance: Even if AI policies exist, they must be operationalized to be effective.
Creating a Positive Learning Environment for AI
To make AI learning and development feel constructive rather than intimidating, experts suggest:
- Establishing Guardrails: Regulations should facilitate safe exploration, not hinder creativity.
- Meeting Users Where They Are: Tailor communication based on the audience’s familiarity and comfort level with AI.
Ultimately, the goal should not just be about efficiency; it should focus on achieving better outcomes in communication performance and strategic value.
Conclusion
In summary, AI has transitioned from experimental phases to tangible applications within organizations. The focus is now on addressing data readiness, building cross-departmental workflows, and ensuring that AI is viewed not merely as a tool but as a strategic asset that can enhance overall performance.
