Below is a roadmap for digitizing planning through AI orchestration, data fabric architecture, and automated workflows
For more than 15 years, I’ve worked at the intersection of technology and business transformation, helping organizations modernize how they plan and execute large-scale investments. Today, my focus is on unifying digital planning processes to streamline workflows, improve collaboration, and reduce costs.
That focus comes from experience. Across industries, I’ve seen how major business units often operate in silos; each managing its own plans, data, and reporting systems. Historically, coordination depended on spreadsheets, slide decks, and endless email threads. The result was predictable: misalignment, duplication, and inefficiency.
Planning in this environment can feel like building a Jenga tower — one small change could send the whole structure tumbling. A new requirement, market shift, or budget update could force teams to start over, rebuilding plans from the ground up.
As business models evolve and services become more interconnected, this fragmented approach is no longer sustainable. Organizations need a unified way to plan, adapt, and execute, one that absorbs change without disruption and enables seamless coordination across the enterprise. Here are some best practices to do just that.
- Digitize planning with AI-powered orchestration
To overcome these challenges, telcos are turning to AI-powered process automation to create a unified planning environment. In this way, every investment — whether infrastructure, technology, or operations — is centralized in a single digital system built on a low-code, data fabric architecture.
This transformation delivers two critical benefits:
- Streamlined information flow: Teams can access project data in real time through a single platform, eliminating version conflicts and disconnected files.
- Faster iteration: Plans can be updated dynamically without starting from scratch, with changes automatically cascading to relevant areas.
By adopting this approach, organizations achieve shorter planning cycles, stronger cross-functional alignment, and better visibility into capital allocation, avoiding idle or misaligned resources and delivering millions of dollars in savings annually.
- Unlock AI’s potential
With the planning foundation modernized, teams can then begin exploring AI and self-service analytics to improve speed, accuracy, and collaboration across processes, empowering users to generate insights without IT intervention. Key use cases include:
- Interrogating the data fabric: Using self-serve analytics, business leaders can query real-time planning data directly. For example, they can ask how many towers are planned for 2026 and instantly surface dashboards without waiting for manual reports.
- AI agents: Digital assistants are great at handling repeatable tasks like validating submissions or converting data formats, reducing cycle times, and freeing staff for higher-value work.
- An AI composer: AI is also jump-starting application development by generating and testing modules from natural language prompts, speeding iteration and delivering upgrades faster.
- Streamline operations with targeted AI automation
AI-driven automation is already creating a measurable impact across business processes. For instance, in greenfield construction projects, property developers will soon be able to submit fiber installation plans for new condos or townhomes online, replacing the old manual forms. An AI agent pre-screens submissions, verifying timelines and documentation before routing them to staff. Only validated requests move forward, significantly reducing processing time and accelerating planning.
Similarly, AI-powered image analysis can assess physical assets, such as utility poles or equipment. Instead of sending technicians to inspect each one, teams can use AI to analyze images to determine which poles are safe and which need replacement. This reduces site visits and scales across thousands of assets.
- Address data, cost, and change management early in your AI journey
These outcomes are not without their roadblocks. First, data accuracy and hallucinations. For example, many organizations use “meta fields” to describe data, which are often just acronyms and designed for developers, not AI. To get better results, it’s critical to rework those descriptions to be clear, detailed, and unambiguous. It’s a lot of homework, but without it, AI can’t interpret the data correctly.
Enterprises also face the broader hurdle of feeding AI complete, real-time data from across systems while safeguarding privacy and access privileges. By integrating AI into processes, they can ensure the AI receives quality data, privacy controls are enforced, and data governance is optimized to meet regulations like GDPR, etc. This is where solutions such as data fabric, which provides a unified and integrated abstraction layer across an organization’s entire data environment, can help.
Second, cost. Most AI services run on a token model where every prompt or transaction consumes tokens. That means teams can’t just apply AI everywhere because it’s available; they must ensure each use case justifies the investment in terms of efficiency and business value.
Third, the pace of change. AI is evolving daily. New models and new capabilities require continuous learning.
- Governance and data protection are paramount
As organizations scale these AI-driven capabilities, ensuring they operate securely and responsibly becomes just as important as improving speed and efficiency. This brings us to another critical area: governance and data protection.
A strong governance framework ensures that new AI-powered processes undergo rigorous security reviews, validating their architecture, data flows, and storage locations. In some cases, adoption may need to pause until compliant cloud instances or regional data hosting options are available, but maintaining trust and compliance is always worth the wait.
- Looking ahead: Process mining
The next frontier in digital transformation is process mining: using AI to analyze workflows, measure the impact of automation, and identify new optimization opportunities. By capturing “before and after” snapshots, organizations can quantify time, cost, and effort savings, while continuously refining processes based on evidence.
Looking ahead, the same AI engine could even recommend process redesigns. For example, if 80% of the planning cycle loops between two milestones, AI could suggest restructuring to cut cycle time. While not there yet, the potential is significant.
From Jenga to a well-oiled machine
The journey from fragmented planning to a unified, AI-powered ecosystem is about more than technology. It’s about making work easier, faster, and smarter for our teams.
By centralizing data, automating routine tasks, and giving people the tools to generate insights on their own, organizations can reduce cycle times, improve alignment, and free staff to focus on higher-value work.
Looking ahead, AI offers exciting possibilities to keep improving how we operate, all while keeping governance and data protection front and center. This isn’t just modernization; it’s about creating a more innovative, more connected way of working for everyone.
