AI For Business Conference

AI For Business Conference - 2025
In May of 2025 we hosted a conference at Beaver Country Day School In Chestnut Hill, MA. This conference brought together students, faculty, and world renowned AI experts to share:
Agenda
How AI works? - A talk by Tom Davenport, President’s Distinguished Professor of Technology and Management at Babson College
Current state of AI? Hype vs Reality - A talk by Tom Davenport
How are companies using AI today? - A talk by Pavan Pant, a Serial Entrepreneur  at Flagship Pioneering 
How can students build a carrier in AI? - A talk by Venkat Srinivasan, Managing Partner at Innopark, an AI venture capital fund 
How are investors and businesses creating return on AI? - A talk by Venkat Srinivasan
How AI works? 
Davenport notes that generative AI refers to systems that create new content or artifacts — for example, generating text, images, code or other media — rather than just analyzing past data.
He contrasts it with what he calls “analytical AI” (traditional machine-learning / predictive models) which mostly analyze and predict, rather than generate.
Current state of AI - Hype vs Reality
It opens new business possibilities: capturing knowledge, generating creative content, supporting knowledge-workers, speeding up workflows. For instance, companies using Gen AI to disseminate institutional knowledge.
But they also stresses the caveats: data must be reasonably good, human validation is still needed (especially where context, judgement or domain-expertise matter).
They argue process and measurement matter: you need to apply disciplined experimentation, define processes, measure what you get (versus what you had) to see whether Gen AI is delivering value.
They warn against treating Gen AI as a panacea: many organisations are excited but may not yet have changed culture or fundamentals to capture real value.
How are businesses using AI today?
Conversational & voice bots — building human conversational voice agents as a key application area (e.g. automate service desks).
Domain-specific knowledge systems — productized language intelligence for professional domains (e.g. legal intelligence or HR knowledge automation).
Education automation — automated exam/paper grading and assessment tools (e.g. QuickGrade).
Vision + embedded AI — AI/vision/CCTV integrations for operational use (e.g. campus security, embedded edge vision).
Productized Gen-AI  — framing Gen-AI as part of deployed product stacks (enterprise features), not just research demos; emphasis on deployment/readiness and solving business problems.
How can students build career in AI?
Work at the Intersection of AI & Business
In his talk, Srinivasan emphasizes that AI isn’t just a technical field — it’s deeply linked with business strategy, emerging markets, and disruptive innovation. For students, this means opportunities to develop both technical literacy and business acumen. 
You can become the bridge between AI technology and business value.
Students can engage in roles that ask: How do I apply AI to a business problem, market need or emerging market context?
Insight: Not just “learn coding” but “learn what value AI unlocks for business”.
Emerging Markets & Global Opportunity
Because Srinivasan links AI with emerging‐market strategy, students get the chance to apply AI in global and emerging-economy settings. 
Projects might involve working in Africa, Asia, Latin America (via the “Srinivasan Family Awards” for students) — grants for students to apply their work globally.
This opens paths into global innovation, social impact, and tech deployment in non-traditional settings.
Entrepreneurial & Venture-Backed AI Roles
Srinivasan leads a VC firm (Innospark) focusing on AI disruption. For students, this suggests roles in:
Startups or venture-backed AI companies where innovation is rapid.
Internships, co-ops or research roles evaluating AI technologies for investment (for example, the Technologist in Residence program at Innospark).
Opportunity to think about value creation via AI (not just building models) — what will make the business succeed, scale, disrupt.
Experiential/Project-Based Learning & Real-World AI Work
At Northeastern, programs aligned with this mindset include student awards (Srinivasan Family Awards) and seminars where business + AI meet. For students:
You can get hands‐on learning: grants, projects, competitions.
Example: students awarded to do research/projects in emerging markets (Kenya, Ghana, etc) via the Srinivasan awards.
Building portfolios of real outcomes (not just class assignments) is important.
Multi-disciplinary Skills: Technical + Domain + Ethical
In his talk and the surrounding program context, there’s recognition that AI’s promise comes with “perils” — ethical, governance, bias, global inequality. 
Students benefit from combining: AI foundations (ML, data, algorithms), domain knowledge (business, markets, policy), and responsible/ethical frameworks.
This combination is increasingly valuable in employers’ eyes (and in emerging markets especially).