Machine learning (ML) and artificial intelligence (AI) continue to transform business operations worldwide—everything from intelligent automation to unlocking hidden insights for better decision-making. A recent study positions the long-term annual impact of AI between $2.6 trillion to $4.4 trillion across a series of corporate use cases. That said, for enterprises, implementing AI is neither easy nor simple. It requires proficiency in essential business, technology, and design skills to bring enterprise applications to life, from ideation to production implementation. Understanding where to begin, or more important, determining the right next steps in the AI journey is critical for success.
To kickstart AI transformations, leaders must first consider which phase on the AI journey continuum best describes their department or organization. The enterprise AI journey phases can be categorized as follows:
Early Phase: Learning, discovery, and AI readiness
This stage is highly analytical, focused on assessing AI fit and laying the groundwork for modernization. At this point, it is important to educate the C-suite and other key stakeholders on AI as well as its strengths and limitations, resourcing needs, potential risks and rewards, plus practical, high-value use cases. This early phase includes the following, setting the foundation for informed decision-making and strategic alignment:
- Market discovery
- General team education
- Risk/reward sensing
- AI readiness
- Problem statements
- Solutioning options
- Data assessment
- Model evaluation
Mid Phase: AI/ML experiments, prototypes, and evaluation
Companies in this middle stage have moved beyond early exploration and are actively engaged in proof-of-concept (PoC) and prototype development as part of their initial AI/ML implementation. Here, an organization would engage a series of experiments and conduct evaluations to identify operational challenges, address stakeholder concerns, and perform early ROI assessments. Successfully navigating this phase requires mitigation of risks, refining AI models, and ensuring alignment with long-term business objectives. This mid phase includes:
- AI/ML feasibility considerations
- Exposing known unknowns
- Risk mitigation
- Early ROI assessment
- Limited adoption testing
Mature Phase: Production implementation and continual refinement
At this stage, AI is typically fully deployed, integrated into operations, and delivering tangible ROI. Organizations in this phase focus on continual optimization, fine-tuning AI-driven processes and automation, and ongoing innovation to establish competitive advantages. The goal is not just to simply maintain AI capabilities but to refine and expand them in order to improve operations as AI evolves. The mature phase of the AI journey includes:
- Productivity building
- Continual de-risking
- ROI optimization
- Technical differentiation
- Maintenance
- Perpetual refinements
After identifying the right phase, innovation and modernization will take center stage. It will take a collective effort within the organization to seize viable opportunities with AI. Below are three strategic ways to take advantage of those opportunities and utilize AI to drive optimizations across your enterprise.
1. Improved decision-making and insights
In virtually all organizations, making the right decision is fundamental for managers and business leaders. And now, more than ever, the need to make the right decisions is putting additional pressure on decision makers. “85% of business leaders have experienced decision stress, and three-quarters have seen the daily volume of decisions they need to make increase tenfold” over the recent years.
For enterprises, poor decision-making can hinder an organization’s growth, stability, and success. It can cause damage to a company’s reputation, lower employee morale, undermine strategic objectives, and result in financial losses. “Poor decision making is estimated to cost firms on average at least 3% of profits.” Many organizations struggle to extract meaningful value from their data using traditional analytics.
Advanced AI, on the other hand, gives employees the ability to make impactful data-driven decisions. AI can unearth hidden insights, which can help with:
- Cost reductions
- Enhanced customer experience and engagement
- Decreased customer attrition
- Fraud detection
2. Intelligent process and workflow automation
Deploying intelligent automation to gain time and resources has become a priority on enterprises on their AI journey. Employees often engage in repetitive tasks that, while necessary, can create bottlenecks and are prone to human error. By automating such processes, teams can redirect their focus toward higher-value initiatives which drive business growth.
Technologies such as robotic process automation (RPA) can handle tasks such as invoicing and payment processing with increased efficiency and accuracy. The recent surge in AI adoption and intelligent process automation (IPA) however, has further amplified the capabilities of process automation, enabling more complex and adaptive workflows.
Recently, the rise of Agentic AI represents a shift from traditional automation to AI systems capable of autonomous decision-making. Unlike other AI-powered systems, which tend to follow certain rules or heavily rely on human input and oversight, Agentic AI operates autonomously with minimal human supervision. Agentic AI relies on a proactive machine intelligence approach to iterative problem solving (from perception to reasoning), performing tasks, and continuous self-learning. Agentic AI systems come with sophisticated reasoning, independent decision-making, and the ability to adapt and take self-directed actions to solve multi-step problems.
For enterprises, this means AI can go beyond simple task automation to proactively manage workflows and adapt dynamically to new conditions. For instance, in IT security, AI-driven security agents can detect and neutralize threats without human intervention, reducing response times and minimizing risks. For retailers, AI agents can be predict supply chain disruptions and autonomously adjust procurement and logistics strategies. In banking, AI-powered financial agents analyze risk factors, and detect fraud without manual human oversight.
3. Team collaboration and communication
In today’s fast-paced business environment, optimizing team collaboration and communication is essential for enhancing productivity. As organizations continue to generate vast amounts of data, efficient management and access to this information becomes a critical driver of success. Next-generation communication tools and sophisticated knowledge management systems are central to improving how teams collaborate and share information.
For modern enterprises, information has become a powerful asset, often described as the new gold. The ability to make this information easily accessible, searchable, and shareable within an organization is fundamental to improving team performance. Companies often rely on complex knowledge management systems that house vast amounts of diverse data—structured, unstructured, or semi-structured data. These systems support collaboration by allowing employees to retrieve essential information quickly, fostering smoother communication and collaboration across teams. However, without the proper tools to navigate this wealth of information, teams struggle to find what they need, when they need it.
Advanced AI can help streamline access to diverse data types stored across various platforms, creating a unified search experience that spans everything from PDFs to spreadsheets, images, audio files, and even videos. This means that no matter where information resides within an organization, employees can quickly find relevant data and insights.
Natural language processing (NLP), a branch of AI focused on the interaction between computers and human language, aims to enable machines to read, understand, interpret, and generate text in a way that is meaningful. Today, the popularity of LLMs (large language models) are transforming the way machines interact with language. An LLM (a key technology used within the field of NLP), is a type of general-purpose language model pre-trained on massive datasets to learn the patterns of language. This training process often requires significant computational resources and optimization of billions of parameters. Once trained, LLMs can be used to perform a variety of tasks, such as generating text, translating languages, and answering questions. LLMs can also be used to improve written communication by summarizing documents, refining grammar, and enhancing tone. These functions boost clarity and help ensure that communication within teams is precise and professional, reducing misunderstandings and promoting more effective collaboration.
Furthermore, AI’s ability to analyze sentiments and emotions in digital communication plays a crucial role in enhancing team dynamics and external customer relations. By using sentiment analysis, AI tools can detect emotional cues in conversations, such as frustration, dissatisfaction, or enthusiasm. This is particularly useful for identifying and addressing potential issues within teams or with customers before they escalate. For example, AI can flag conversations where negative sentiment is high, allowing managers to intervene and de-escalate tense situations. Similarly, by understanding emotional triggers, organizations can tailor their communication strategies to boost employee morale, strengthen brand loyalty, and improve customer satisfaction.
Strategic considerations for AI implementations
After identifying key areas where AI can add value, consider the following questions:
- Which use cases can quickly provide value?
- Is there sufficient managerial or executive support for the intended use case(s)?
- Are ROI expectations realistic among stakeholders?
- Are changes required to the compute infrastructure for this purpose? This involves hardware and IT capacity planning.
- Do we have access to the right data for the intended use case(s)? In the exploration phase, consider how the 5 Vs of data can accelerate discovery and unlock hidden value.
- What are common missteps to avoid in implementing AI?
- Do we have the required skills internally or externally via vendors to make this a reality? It takes 18 separate skills to bring an AI solution to life from ideation to production level implementation.
Continuous learning and expert collaboration
AI is a vast and ever-evolving field. Familiarizing yourself with key AI terms and concepts is essential. Understanding the distinctions between traditional data analytics and the latest in AI including agentic AI, analytical AI, generative AI, and hyperautomation can prove highly valuable in assessing potential opportunities along your AI journey.
Embarking on this new path requires a strategic approach, focusing on specific business challenges and leveraging AI’s transformative potential to drive innovation and efficiency within your organization. On this journey, you may benefit by partnering with specialized AI firms and experts to accelerate the overall learning process for your team. These professionals can provide support in uncovering the narratives within your business, its data, and processes, and help you avoid common missteps.
It’s also important to ensure that any implementation of AI is compliant with ethical standards and the rapidly-evolving regulatory landscape. AI promises to reshape our experiences in ways both subtle and profound. Ideally, AI is developed and deployed responsibly, ethically, and in a manner that benefits humanity. Building trustworthy AI involves addressing various concerns such as algorithmic biases, data privacy, transparency, accountability, and the potential societal impacts of AI. At present, multiple corporate and governmental initiatives are underway to create ethical guidelines, codes of conduct, and regulatory frameworks that promote fairness, accountability, and transparency in AI implementations.
For additional information, read our recent guide related to creating an effective corporate AI policy and our blog about the costly problems caused by legacy IT systems.
ABOUT ENTEFY
Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.
Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.
To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.