Igor Rikalo is President at o9 Solutions.
For more than 60 years, industry experts, futurists and even science fiction authors have made predictions about the future of the workplace—and society at large. Some of the predictions from decades ago seem outlandish today, but others, like Arthur C. Clark’s foretelling of instantaneous, wireless global communication, were spot on. As AI technologies continue to evolve, I believe that many more of these predictions will shift from visionary ideas into realities that we’ll experience firsthand.
We’re already seeing this take shape in the modern workplace. In Deloitte’s 2026 State of AI in the Enterprise, a few interesting statistics stood out to me:
• 25% of business and IT leaders surveyed have moved 40% or more of their AI pilots and experiments into production so far, but 54% expect to do so within the next three to six months.
• 37% of respondents are using AI at the surface level, with little or no change to their underlying business.
• 30% of respondents are redesigning key processes within their business.
• 34% of respondents are using AI to truly transform their businesses.
So, how can IT and business leaders position their organizations to leverage AI to redesign processes and ultimately reach a point where AI is a catalyst for further transformation? While we are still in the early years of integrating AI into the workplace, from my perspective in supporting global companies across all phases of their digital transformation journeys, the following factors will be key to building a transformative, AI-enabled enterprise.
Understand Your ‘Why’
One of the foundational steps in implementing AI within a business is understanding the broader purpose or initiatives your business wants to achieve and determining the specific reasons why your business should leverage AI to accomplish them.
The following questions are some initial ways to help define why you want to apply AI within your organization and where it may make sense to do so:
• What are the pain points that AI can help solve for?
• Where are there inefficiencies that AI can help address?
• What are the business risks that you want to spot sooner?
• Where are new opportunities created or accelerated by implementing AI?
This approach helps clarify the specific goals your organization wants to accomplish, determine which data is most relevant and establish a road map that will guide not only AI implementations and pilots but also the broader strategies your company will embark on as your digital transformation continues to scale and mature.
Prioritize Data Reliability
Another essential step in building a more AI-enabled enterprise is to determine which types of data should be prioritized. The reliability and quality of the data used can play a significant role in an AI rollout’s success, as high-quality inputs lead to high-quality outputs and minimize the risk of inaccurate AI results.
However, in the early stages of implementing AI across an organization’s processes, the focus shouldn’t be on finding the “perfect” data, but data that is good enough to get started. This means determining which structured (e.g., spreadsheet data for inventory levels, product information, SKU numbers, etc.) and unstructured (e.g., emails, meeting notes, contract details, invoices, etc.) data are accurate and reliable enough to produce consistent and trusted results for the business processes to which they’re applied. The data must also be easily traced back to show how results were generated.
As a company’s AI usage matures, the data, models, platforms and workflows will continuously evolve and improve.
Link AI To Your Organization’s Existing Frameworks
Another component of building the AI-enabled enterprise is connecting enterprise platforms that embed AI capabilities into a business’s operational frameworks, processes and workflows. This allows functions like enterprise resource planning and supply chain management to reduce information and data silos that would impede cross-functional planning and slow down decision-making processes. Instead, AI-embedded platforms allow teams to coordinate and collaborate more seamlessly by using high-quality data integrated from across multiple departments, serving as a single source of truth where teams are empowered to make cross-functional business decisions quickly and accurately.
Cultivate Talent
Putting time and effort into ensuring your workforce is ready and confident in using AI in their daily work responsibilities is also a critical aspect of creating an AI-enabled workplace. Deloitte found that 53% of business leaders surveyed are focused on raising employees’ AI fluency. The leaders who are already thinking beyond achieving AI fluency are starting to prepare their employees for more strategic roles and responsibilities.
As a company’s digital transformation and AI-adoption continue to mature, it may begin to focus on refining its talent acquisition strategies to ensure that top performers have extensive knowledge of AI and can apply the latest innovations to their role. Companies may, over time, begin to re-envision career paths for their employees as they become more advanced in their use of AI.
Embrace Innovation And Continuous Learning
As your organization continues to mature in its digital transformation, look for AI-enabled tools and platforms that maintain an innovative edge, as they will allow you to continue evolving and scaling your AI deployment. A technology currently gaining traction is neurosymbolic AI, a framework that connects the strengths of deep learning neural networks with the rules and context of symbolic AI.
Forward-thinking technology companies serving clients in highly regulated industries (finance, healthcare/life sciences, telecom, energy, etc.) are already incorporating neurosymbolic AI capabilities into their platforms. As such, these companies can leverage AI frameworks and digital operating models built on their organizations’ unique knowledge and context, increasing the accuracy and trust of AI-based results and embedding continuous learning capabilities into the organization’s operational and planning workflows and decision-making at scale.
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