Digital transformation is the process of upgrading the existing technical architecture of an enterprise by adopting new software solutions or using the existing ones more efficiently. Today, with the rise of artificial intelligence (AI) technologies, digital transformation is more important than ever.
Enterprises across industries are integrating AI-powered tools and software into their workflows and operations to unlock higher levels of productivity, reduce costs, and deliver greater value to their customers.
However, achieving these benefits through an AI-oriented digital transformation requires a calculated approach. Organizations should carefully analyze the available solutions and closely monitor performance metrics during the implementation phase.
In this article, let’s look at four things enterprises need to focus on to facilitate a smoother AI transformation within their organizations.
1. Strategic alignment with business objectives
Any kind of workflow upgrade, whether it is changing the individual action items or the tools used to complete them, should be closely linked to the core goals of the enterprise. The core goals can include completing projects faster, delivering better goods/services, and optimizing overhead costs.
One of the major obstacles in this process is the disconnect between the technical and business teams.
Technical teams are usually more focused on the capabilities of the tools available and the capabilities they bring. On the other hand, business teams are focused on aspects related to client satisfaction and revenue.
This gets more challenging as the business environments can rapidly change and AI is evolving at a faster-than-ever pace.
To align business objectives and AI upgrades strategically, enterprises need to build cross-functional teams to set measurable goals. While bringing about structural changes, enterprises need to closely track performance metrics to gauge the efficacy of AI transformation efforts and proactively iterate their approach as needed.
2. Data management and governance
Custom AI models and workflows run on private data, generally collected from the users and other stakeholders. While giving operational workflows an AI upgrade, it is essential to set up robust, future-proof frameworks that manage this data properly.
Data governance refers to that framework. It is a set of standardized principles and practices within an organization that determines how data is collected, stored, processed, and archived throughout the lifecycle.
This management and governance framework helps enterprises create data pipelines that bring reliable information from different sources, store them securely, analyze them for insights through custom AI models, and archive or delete them.
Apart from getting value from real-world data, data governance and management help enterprises decrease the likelihood of security breaches and data misuse while remaining compliant with regulations such as GDPR and CCPA.
As you can imagine, each of the processes is multilayered which requires cross-departmental collaboration and, in some cases, partnering with certified AI consulting firms.
Remember that an internal regulatory framework often develops over time as enterprise teams progressively learn more about their AI requirements. This can be offloaded to the AI consulting partner to speed up the workflows.
3. Workforce adaptation and change management
AI-augmented digital transformation modifies how teams complete different action items on a day-to-day basis. Consequently, enterprises have to take steps to ensure that the employees are sufficiently prepared to embrace the changes.
Workforce adaptation and change management initiatives educate and train team members to get a grip on the enhanced workflows and new tools. It also collects feedback from the enterprise employees to learn about their personal preferences and adjust the pace of change.
Enterprises, before augmenting their organizational operations with AI tools, need to closely analyze the skillsets of their current workforce. This makes it simpler to shape the change management initiatives where the skill gaps are properly addressed via training.
An ill-formed or poorly executed workforce education program can backfire and disrupt the regular flow of work by distracting the employees and overwhelming them with educational resources about advanced AI tools.
It is crucial, therefore, to begin by opening an internal discussion where the needs of the enterprise teams are heard. This is essential for building comprehensive support systems that help the employees level up adequately to reap the benefits of digital transformation.
4. Measuring ROI and long-term sustainability
Transforming an enterprise by integrating AI tools is much more than adopting the latest model you can afford and ‘automating’ as many action items as possible. Rather, it’s about methodically speeding up various operations to reach the business goals.
Moreover, implementing AI to enhance enterprise processes can get costly. From hiring skilled professionals to training employees while handling minor disruptions in work, digital transformation can be time-consuming and expensive.
This necessitates a system that constantly monitors the performance of newly installed tailored AI systems and tracks their impact over time. There are many things that enterprises need to keep their eyes on:
- Tangible improvements in terms of operational efficiency (time to completion, quality of work, etc.)
- Cost incurred by the organization in adopting the AI systems and keeping them running
- The ease of transition from the old workflow to the new one from the team’s perspective
- Scalability and robustness of the AI upgrades to retain a competitive edge in the future
Enterprises, right from the start of their AI transformation journey, should focus on identifying the essential key performance indicators (KPIs) and create dashboards to track them over time. Plus, it is crucial to foster a transparent culture of continuous learning to reap all the benefits that AI promises.
Looking forward: What enterprises should do next
Successful AI integration requires strategic alignment with business objectives, robust data management and governance, effective workforce adaptation, and a clear focus on measuring ROI and sustainability.
While each of these considerations is crucial, addressing them all simultaneously can be overwhelming for many organizations. Mistakes and oversights can disrupt work and can quickly rack up expenses with nothing to show.
This is where partnering with an experienced AI consulting agency can make a significant difference.
GrowExx, a leading AI consulting company, has a proven track record of delivering custom AI solutions to enterprises that drive results. From strategic planning to providing continuous support after implementation, organizations from various industries have successfully digitally transformed themselves with GrowExx.
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