We’ve long passed the hype stage of AI. Businesses are now scrambling to implement their agentic solutions but are running into bottlenecks that stem from a lack of a coherent strategy and robust preparation, revealing a gap in readiness.
This can keep them in a vicious cycle of throwing things at the wall to see what sticks, but never actually scaling. With no cohesive vision or plan for executing agentic AI, businesses could experience unnecessary delays and costs that potentially block their solutions from delivering real business value across the enterprise.
The potential of agentic AI is promising, but it’s not enough to implement it for its own sake. When businesses instead focus on measurable impacts that are explicitly tied to a desired business outcome, that’s when the magic happens. With intentional AI orchestration, you can create a clear path to business outcomes that align with your operating model and customers.
In our webinar, “From Pilot to Payoff: Scaling Generative AI for Business Impact”, Concentrix experts Stephanie Kozak and James Kim shared invaluable insights backed by first-hand data through Everest on how businesses can push past the hype and create a realistic, yet effective Agentic Operating Framework™ that actually works with your unique business needs.
In this blog, we dive deeper into those insights so you can set yourself up for success throughout your agentic AI journey.
Lead with Outcomes, Not FOMO
Even with increased pressure to deploy agentic AI solutions, businesses must resist acting purely out of FOMO (the fear of missing out). Take top AI leaders, for example, who say that while Moltbook, while “revolutionary”, could pose security risks if companies don’t use the right precautions.¹
The key is to prioritize use cases that drive quantifiable results. By rushing into fragmented pilots driven by external pressure, it becomes easier to overlook the risks that come with this strategy. You could also lose out on opportunities for adopting a unified, intelligent solution.
We’re not the only ones who think use cases should be the focus when deploying agentic AI. A Forrester research report had this to say:
“To ensure that AI investments are not isolated experiments but enterprise-aligned solutions, every initiative must answer two questions: ‘Does it solve a client or employee need?’ and ‘Can it be delivered at scale responsibly?’”²
Designing your solution with these questions in mind can help your deployment scale instead of stall. It can be tempting to charge ahead to keep up with other enterprises, but without a solid plan that addresses real challenges, you could risk overlooking the many ways your solution could address the unique needs of your business.
During the webinar, James reflected on how “we’re still seeing a lot of FOMO out in the marketplace. And while it’s certainly warranted, it’s resulting in siloed behavior where teams race to deploy something without a shared view of success or coordination around enterprise priorities.”
To avoid this trap, start with these action items:
- Define three to five critical business outcomes you want AI to influence (for example, reduced handle time, improved NPS, or higher conversion).
- Identify what is keeping you from those outcomes and how AI could play a part in the solution to overcome.
- Identify where redundant pilots exist across departments by focusing on enterprise-wide goals and customer experiences.
When you anchor your implementation in outcomes instead of pure hype, you create a common language that unites executives, IT, and operations around what success really looks like.
Prioritize Use Cases with Balanced Risk and Impact
It’s easy to underestimate the potential impact of external facing implementations, but if not done right, this could be a major risk for businesses. Let’s say your AI agent slips up and gives incorrect answers to customers. The reputational damage that can result may take months or even years to recover from as customers slowly rebuild their trust in your business.
Don’t know where to begin? Start by classifying each use case as either internal facing (e.g. IT support for employees) or external facing (e.g. resolving complaints directly with the customer). Then, determine the risk levels of each use case by asking yourself how your business could be impacted if your agentic solution falters.
To lower your risk, focus on internal-facing opportunities that can still produce meaningful results for your business, like tailored agents that can help your employees more quickly perform their job functions or compliance monitoring tools that identify patterns and send alerts to avoid potential penalties.
Other use cases like AI-enabled training assistance for human advisors or internal document summarization can be addressed with a lower risk profile as opposed to a fully autonomous customer-facing virtual agent. As your capabilities mature, you can eventually take on higher-risk external use cases with a more robust safety net as you develop your agentic operating model.
Governance, Observability, and “Enterprise Truth”
Agentic AI can’t drive meaningful results at scale on isolated pilots alone. It also needs a backbone of governance and observability, along with a unified “enterprise truth”, a phrase coined by Pete Clare, Managing Director of Cloud & Collaboration Engineering.
Enterprise truth in this context represents the information, data, functions, and policies that make your business unique. It should also be curated, open for AI consumption, and actively managed to achieve sustainable success in delivering AI-enabled outcomes with safety and trust.
Many organizations discover too late that their AI journey has little to show in the way of results because core business processes and data readiness strategies aren’t documented or integrated in a way that their AI agents can reliably use.
Without these foundations, organizations risk overlooking true operational readiness in favor of flashy demos. Enterprise truths become the substrate that allows AI agents to act in alignment with how your business truly operates.
Adopt an Enterprise View with an Agentic Value Map
Businesses arrive at a defining milestone in their AI journeys when they shift from disconnected deployments to a holistic, enterprise-minded view of where human and AI agents create the most value. For example, our Agentic Value Map helps leaders visualize value streams, key workflows, and where human effort, AI automation, or collaboration between the two makes the most sense.
You can begin this shift by pinpointing major value streams in your industry, like onboarding or employee retention, then listing high-value workflows within each stream and determining which ones are better suited for human-only, AI-only, or human‑plus‑AI collaboration.
Businesses can in turn address missed opportunities while revealing reuse potential. One AI agent, once built, can be deployed across several parts of the company. This reduces silos and results in a cohesive, coordinated AI journey.
Here’s a sneak peek of what an agentic value map might look like for a travel or transit company:
It’s Time to Launch Your Agentic AI Journey
Stop falling into the trap of siloes and the “one-and-done project” mentality. With the right frameworks and strategies, you can position your business for long-term success with a unified, enterprise-minded AI strategy. Stick to the steps, and you can transform chaos into momentum.
¹ Top AI leaders are begging people not to use Moltbook,” Eva Roytburg, Fortune, February 2, 2026.
² The CIO’s Guide To AI Readiness, Forrester Research, Inc., January 23, 2026. – Available to Forrester subscribers or for purchase
