While many organizations are just getting comfortable with AI assistants that help with isolated tasks, a new approach has arrived: agentic AI systems that work autonomously on complex tasks from start to finish. The business impact of this advancement has been substantial and measurable. Organizations using agentic AI systems report an average 3-4x ROI and improvements in efficiency metrics by as much as 60%, based on case studies.

This blog post gives you an overview of what agentic AI is, provides concrete examples from the real world, and offers practical ideas for both quick wins and a longer-term boost for your business.

What Makes Agentic AI Different?

ChatGPT, Microsoft 365 Copilot, and similar generative AI assistants mark the previous frontier of AI development. These copilots require constant instructions from the user: “read this document”, “summarize key points”, “use them to draft an email to director Y”, “no, not like that!”. While this speeds up your team’s work and allows them to focus more on the bigger picture, they’re still very much spending time micromanaging the AI.

Agentic AI changes this dynamic by enabling AI systems to operate independently. When we give a task to one of these AI agents, it

1. Plans the approach, e.g., by breaking down complex goals into smaller, actionable steps,
2. Executes multiple actions across a variety of tools and data sources at its disposal,
3. Adapts to obstacles it encounters along the way,
4. Makes decisions on how to complete the task, and
5. Delivers complete results rather than just completing individual steps

For instance, Google’s Gemini Deep Research can take a single task like ‘Prepare report on recent trends in sustainable packaging industry in Europe’, and it’ll autonomously figure out concrete topics to search for, browse multiple web sites, synthesize information, and compile a structured report. All without needing any mid-process guidance from the user. What would have taken hours of research becomes a single request with results ready after a quick coffee run.

This autonomy and goal-orientation of agentic AI exists on a spectrum. It ranges from simple copilot-like agents that can handle a few steps independently to increasingly sophisticated agents that can tackle complex workflows with less and less human intervention every day. This frees your team to focus on truly high-value work that requires or benefits from human ingenuity and creativity.

Organizations are implementing agentic AI solutions across this entire spectrum, from simple assistants to autonomous systems. At Brightly, we’ve developed agents for marketing teams to automatically gather feedback, analyze market trends, and draw insights from the data. Customer service teams use agents that independently resolve complex inquiries by navigating knowledge bases, considering customer histories, and drafting personalized responses. Product development accelerates through agents that continuously process user feedback and bug reports, even generating initial specification documents without constant oversight.

When identifying potential AI agent applications in your organization, look for tasks that:

• Involve understanding or generating language-based content
• Require a series of steps that depend on previous results
• Currently consume significant time from skilled professionals

These characteristics often represent ideal starting points for agentic AI implementation. Across industries, these solutions are enabling professionals to shift from routine processing to higher-value strategic work.

Agentic AI in Action Today

In my consulting work, I use a team of AI agents to assist with tasks such as research, brainstorming, and routine coding. This human-AI collaboration has improved my productivity by 10-30%, particularly when starting new projects where the agents can manage preliminary work and drafts, allowing me to focus on the big picture.

These productivity gains aren’t isolated to consultants. Across industries, agentic AI is having an impact:

Salesforce Agentforce changes CRM workflows by automatically summarizing calls, routing inquiries, and providing real-time sentiment analysis. It’s freeing sales and customer success teams from routine tasks while improving customer experience.

Acentra Health’s MedScribe speeds up healthcare appeals processes in the U.S. MedScribe automatically converts physicians' notes—filled with medical shorthand and specialized jargon—into clear, patient-friendly documents. This automation has reportedly more than doubled processing speed while maintaining outcome quality (source).

Databricks Genie democratizes data analytics with a data analyst AI agent. It allows non-technical users to explore complex company data through conversations in plain English (or one of many other supported languages). This eliminates the need to know SQL and delivers instant insights and data visualizations. Our customers have found this particularly valuable since it increases the value of their existing data platform investments.

At Brightly, we’ve developed, e.g., Dealcheck – a document intelligence agent that accelerates sales processes by automatically analyzing contracts, RFPs, and market dialogue requests. Initially built for internal use, we've now deployed customized versions to clients, improving both response quality and speed for sales teams.

Measurable Benefits: The Business Case for Agentic AI

The business case for agentic AI is backed by compelling real-world reports:

• C3IT, an Indian IT consultancy, reports accelerating project documentation work by 30% and creation time for kick-off presentations by 60% (source),
• Elcome, a maritime technology provider, has cut support request response times by up to 60% with the help of agentic AI (source),
• Crediclub, a financial services company, achieved 96% monthly savings in auditing expenses with specialized agentic AI (source).

On average, organizations see 3.70 € returned for every 1 € invested in agentic AI solutions, according to industry sources, and the benefits extend beyond direct cost savings. As organizations automate repetitive tasks, they not only save time but also improve output quality and employee satisfaction, leading to further efficiency gains.

To estimate the potential ROI for an agentic AI use case, you start with a simple formula:

Annual Value =
  (Time saved per task) x (Task frequency) x (Number of employees) x (Average hourly labor cost)

For example, if agentic AI saves 30 minutes a day for 30 employees costing 50 € / hour, the annual savings are (0.5 hours) x 200 days x 30 employees x 50 € = 150 000 €. And this considers only direct time savings – the full ROI typically includes additional benefits from quality improvements and strategic redeployment of talent.

While these immediate returns are impressive, the long-term potential is even more important as AI capabilities continue to evolve at an exponential pace (source). Currently, agentic AI can handle tasks that take a human roughly an hour to complete, but estimates suggest that by 2030 agentic AI can handle tasks that would take a human a month to complete! Organizations can capture these exponential gains by adopting adaptable AI frameworks that help reap the benefits of AIs of tomorrow (source).

Start Here

Ready to explore agentic AI for your business?

You should start by identifying clear opportunities in your existing processes. We often recommend two complementary approaches:

1. Define strategic, long-term vision
   Clarify how agentic AI fits into your organization’s future. This usually includes, e.g., assessing your digital maturity and overall AI readiness, identifying core business processes that could benefit from agentic AI, developing a combined AI and data roadmap aligned with your business strategy. This groundwork aligns AI initiatives with business objectives and helps prioritize AI investments.
2. Identify quick wins
   Find low-hanging fruits that deliver immediate value while building your team’s confidence in AI. Effective quick wins typically address clearly defined and previously known pain points, impact multiple stakeholders for maximum visibility, and generate tangible, easily measurable ROI that builds momentum for broader initiatives.

Pilot projects in low-risk, high-impact areas often include, e.g., automating and improving various customer support channels, streamlining sales related pre-analysis and drafting tasks, and accelerating various documentation and reporting processes.

From Pilot to Scale

Once you’ve identified promising opportunities, successful implementation follows a clear pattern:

1. Measure before you start: Define effective metrics and KPIs to measure success and remember to establish a non-AI baseline for comparison. This provides concrete evidence of improvement needed to justify and guide broader deployment.
2. Focus first, scale later: Begin with focused solutions to specific problems, then scale to cover more use cases as organizational confidence and team’s capabilities grow.

The transition from isolated pilots to enterprise-wide capability happens naturally when you combine clear metrics demonstrating success and a strategic vision guiding expansion.

Preparing for the Rapid Evolution of Agentic AI

Agentic AI capabilities are rapidly advancing, allowing for more and more complex tasks to be automated. What handles hour-long tasks today might manage month-long projects by 2030. Organizations implementing adaptable AI frameworks now will compound their returns as these capabilities expand. We’ve already seen this with Santen, a pharmaceutical company where our collaboration on early agentic AI implementation has positioned them to automatically scale benefits as underlying AI capabilities advance (source).

According to Gartner’s estimates, this rapid evolution will have major impacts by 2028 (source):

• 15% of day-to-day work decisions will be executed by agentic AI (up from <1% today)
• 33% of enterprise software systems will include agentic AI capabilities
• 80% of common customer service tasks will be independently resolved by AI

These statistics highlight the upcoming change in how work will be performed. They emphasize the importance of early adoption of agentic AI to create advantages that late adopters will struggle to match. As we've seen throughout this blog post, from measurable ROI to transformative productivity gains, agentic AI is not just a future possibility—it's present-day reality that organizations are already leveraging to reshape how work gets done.

In our upcoming blog post, we’ll explore the technical foundations that power agentic AI systems. We’ll break them down into their core components, share key implementation considerations from our experience, and offer practical insights we’ve learned from building these solutions with our customers. Whether planning your first agentic AI project or scaling existing implementations, these principles will help you make informed decisions and avoid common pitfalls on your journey.