The Future of AI and Analytics: Insights from Gary Arora and Dr. Aleksandar Tomic
The landscape of AI-driven analytics is rapidly evolving, reshaping business operations, education, and the very nature of work. In a recent ODSC AIX podcast, Dr. Aleksandar Tomic, Associate Dean for Strategy, Innovation, and Technology at Boston College, and Gary Arora, Chief Architect for Cloud and AI Solutions at Deloitte, discussed the transformative impact of AI, the shifting skillsets required in data science and analytics, and the future of AI-enhanced decision-making.
These insights paint a compelling picture of how AI is augmenting human expertise rather than replacing it, as well as the challenges and opportunities that lie ahead.
You can listen to the full podcast on Spotify, Apple, and SoundCloud.
AI as a Fundamental Skill, Not a Replacement
One of the core themes of the discussion was the increasing importance of AI as a fundamental skill, akin to writing or mathematics. Gary emphasized that “working with AI is now as fundamental as writing,” arguing that universities must integrate hands-on AI labs into their curricula to prepare students for the future job market.
However, both experts agreed that AI is still just a tool — albeit a powerful one. While it is automating certain repetitive tasks, it is not replacing the fundamental need for human judgment, business acumen, and analytical thinking. Instead, professionals who can leverage AI to enhance their decision-making and business strategy will have a significant competitive advantage.
Dr. Tomic highlighted how AI is transforming education, making coding and data analysis more accessible but also raising new challenges. Historically, data analysts were required to write SQL queries or scripts in Python to extract insights. Now, with AI-powered analytics tools, users can “talk to data” using natural language queries. This shift, while empowering, demands a deeper understanding of how AI works, its limitations, and its potential biases.
The Evolution of Analytics and AI-Powered Decision-Making
The conversation explored how AI is redefining analytics workflows. Traditionally, data analytics focused on descriptive and predictive insights — what happened and what is likely to happen next. But AI is enabling a shift toward prescriptive analytics, answering the question: What should we do about it?
Gary explained that this shift inverts the traditional analytics pyramid. In the past, business users relied on data scientists to generate insights. Now, AI enables non-technical users to conduct deep analysis, effectively democratizing data-driven decision-making. However, as Gary pointed out, “having AI does not mean you have a strategy” — organizations still need professionals who understand the business context and can ensure AI is used effectively and responsibly.
One of the biggest hurdles in AI adoption is trust. Organizations are wary of fully autonomous decision-making because AI, particularly large language models, can produce errors or “hallucinations.” AI is best deployed in augmented intelligence scenarios, where it enhances human decision-making rather than replacing it outright. As Gary put it, “We are not replacing the pilot; we are building a better cockpit.”
Challenges in Implementing AI at Scale
While AI presents exciting possibilities, integrating it into enterprise environments comes with significant challenges. Gary identified three major roadblocks:
- Data Quality and Integration — AI models require high-quality, structured, and connected data to function effectively. Many organizations have fragmented legacy systems that were not designed to support AI-driven insights.
- Governance and Risk Management — AI systems can generate unreliable or biased outputs, making governance crucial. Organizations need clear frameworks to validate AI-driven decisions, particularly in highly regulated industries like finance and healthcare.
- Cost and Scalability — AI models, especially large-scale language models, are computationally expensive. If not properly managed, the costs of AI adoption can outweigh its benefits. Organizations must balance the potential ROI with the operational costs of running AI at scale.
These challenges underscore why human oversight remains essential. AI excels at pattern recognition and automation, but it lacks the contextual understanding and ethical reasoning that human experts provide.
The Role of AI in Education and Workforce Upskilling
Dr. Tomic offered valuable insights into how academia is adapting to AI’s rapid advancements. Historically, education focused on teaching students specific tools — whether Python, R, or SQL. But as AI automates more technical tasks, the focus must shift to problem-solving, critical thinking, and adaptability.
One pressing challenge for educators is how to assess learning in an AI-driven world. Many institutions have attempted to ban AI tools like ChatGPT, but as Dr. Tomic noted, this approach is neither practical nor beneficial. Instead, universities must find ways to integrate AI into learning while ensuring students develop true understanding, rather than reliance on AI-generated answers.
He likened this shift to the evolution of typing: once a specialized skill, typing is now expected in nearly every profession. Similarly, AI proficiency will soon be a baseline skill, not an optional expertise.
Furthermore, AI is reshaping career paths in analytics. Entry-level data analyst roles — historically focused on data wrangling and report generation — are being automated. As a result, domain expertise is becoming a key differentiator. Instead of merely running SQL queries, analysts must now contextualize AI-generated insights, validate findings, and apply business judgment.
The Future of Analytics Careers in an AI-Powered World
Given these shifts, what skills will be most valuable for future data professionals? Gary and Dr. Tomic outlined several key competencies:
- AI Literacy — Understanding how AI models work, their strengths, and their limitations.
- Prompt Engineering — Crafting effective queries and instructions for AI systems.
- Data Storytelling — Translating complex insights into actionable business decisions.
- Ethical AI Oversight — Ensuring AI-driven decisions are fair, transparent, and unbiased.
- Industry-Specific Knowledge — Deep expertise in a particular sector (e.g., finance, healthcare, marketing) is becoming increasingly important.
As AI continues to evolve, the ability to adapt and learn new skills will be the most valuable trait for professionals in analytics and beyond.
Final Thoughts: The Age of AI-Augmented Intelligence
AI is transforming analytics, education, and business operations at an unprecedented pace. But rather than replacing humans, it is amplifying our capabilities. The future belongs to those who can blend technical understanding, strategic thinking, and domain expertise to harness AI’s full potential.
As Dr. Tomic succinctly put it, “AI is not going anywhere. Get familiar with it, get used to it, and use your creativity to figure out how to use it in productive — and hopefully fun — ways.”
For data professionals, educators, and business leaders, the key takeaway is clear: AI is a tool, not a replacement. Success in this AI-powered era will come to those who can bridge the gap between technology and business strategy, ensuring AI-driven insights translate into real-world value.
