“Do what you like and success will follow you. Passion is the fuel behind the engine of a successful career.” – Abhishek Srivastava, the CEO of Techcanvass
Abhishek Srivastava, the founder of Techcanvass, has played quite a major role in building the company. With over 25 years of experience, he’s achieved what many young entrepreneurs aspire to. As a graduate of NIT and IIM Kozhikode, he brings both strong academic credentials and hands-on leadership, which have mostly shaped his approach at Techcanvass.
Here is what Abhishek Srivastava has spoken about his journey in AI and business analytics, as well as what his company offers to make individuals future‑ready. In this interview with Business Upside, he has expressed his views on how AI will bring changes in the business arena and what skills should be honed.
- How do you see the changes led by AI in the business landscape in the next 5 years, and what skills do you find important for professionals to remain relevant?
Answer: I’ve been at the intersection of technology, education, and workforce upskilling for quite some time, and I see companies increasingly adopting intelligent tools and methods to streamline operations. In many cases, what used to be considered futuristic is now a must‑have. Professionals are most likely realizing that they can’t wait to learn new tools; they need to pick them up now in order to stay relevant.
One striking case is Viz.ai’s stroke detection platform. It’s changing emergency care in U.S. hospitals by interpreting medical images in real time and notifying medical teams almost instantly – something that only a few years ago seemed almost science fiction. Using examples like this in our training helps learners at Techcanvass connect theoretical ideas to what’s happening in the real world.
Another example is UPS’s ORION (On‑Road Integrated Optimization and Navigation) system. Every day, many drivers rely on this platform to plan better delivery routes. It considers maps, package details, traffic patterns, and delivery windows all together. UPS estimates that ORION cuts delivery distances by about 100 million miles per year, saving fuel and cutting emissions – which is quite something. It shows how technology can bring both cost savings and environmental benefits, when used wisely.
Since the industry is moving fast, professionals need to upskill in order to keep pace. The World Economic Forum reports that nearly 40% of core job skills will be disrupted by 2030. That matches what we mostly see at Techcanvass. So we built our curriculum with an “intelligent tools-first” mindset: not just teaching theory, but preparing people for the actual roles that are forming now.
Some skills that seem most important: Analytical Thinking and Creative Thinking – because those are things machines can’t fully replicate. Afterwards, skills around understanding data (how to interpret it, how it’s collected, cleaned, used) and systems thinking matter quite a bit, especially as processes become more interconnected.
- What are some of the potential threats that businesses face while implementing AI solutions, and how can professionals overcome them?
Answer: That’s one of the trickiest parts. At Techcanvass, we try to underscore responsible practices because too often people focus on what’s possible and neglect what can go wrong. I’m often telling students that knowing risks is just as important as knowing potential.
Some of the threats include:
- High Costs and Unclear ROI: Projects involving cutting‑edge tools or intelligent systems often require big investments – in computing, in hiring experts, in licensing. If there isn’t a well‑thought‑out business case, costs can balloon without guarantee of good returns.
- Poor Data Quality: It’s the “garbage in, garbage out” situation. If your data is incomplete, inconsistent, biased, or inaccurate, then whatever you build on top of it will most likely suffer.
- Algorithmic Bias: Using historical data can unintentionally reproduce unfair outcomes – say, in hiring or lending. Without checks, what was once bias becomes amplified.
- Privacy and Security: With lots of sensitive information involved, there are real compliance and security risks. Regulations like GDPR or CCPA are forcing companies to think carefully about data handling, permissions, user consent, and so on.
- Lack of Skills: It’s not easy to find people who know both the domain (business, operations, sector) and the technical side (data, modeling, interpretation). Many organizations are still catching up in this area.
In order to solve these problems, professionals should focus on learning all the time, being conscious of ethics, having solid governance, and making sure that every project has a defined goal. You need more than just tools; you need supervision, excellent data hygiene and realistic goals.
- How do you think AI will transform the job role of business analysts, and what new skills do they need to stay updated?
Answer: Business Analysts often do a lot of the groundwork: gathering requirements, mapping processes, writing detailed specs. That remains important, but it also limits how far one can move up the ladder. I believe things will shift.
Instead of handling every task manually, analysts will increasingly delegate repetitive or data‑heavy parts to intelligent systems. This means analysts can invest their time toward higher impact work – identifying opportunities, shaping strategy, working closely with stakeholders.
Business analysts will most likely become strategic advisors. They’ll define problem statements more sharply, ensure that implemented solutions deliver concrete value and help guide decisions rather than just document them.
In order to get there, they’ll need:
Strong Foundational BA Skills: requirement gathering, stakeholder management, process analysis, problem solving – all still essential.
AI Literacy & Tools knowledge: knowing how to use tools for machine learning, natural language processing, generative tools; also knowing their limits.
Data Acumen: understanding data sources, data bias, how models are trained; not to become data scientists, but to be savvy enough to critique outputs.
Ethical & Responsible AI Awareness: awareness of privacy, fairness, transparency; asking questions about what gets automated and whether that’s fair or sustainable.
- According to you, how will data analysis change in the next few years?
Answer: At Techcanvass, we’re already seeing shifts and in many cases helping bring them about. Our DAIC program is producing professionals who are doing things just a few years ago considered too advanced.
Right now, a lot of work is spent cleaning and preparing data. That will mostly be automated in order to free analysts to focus more on interpretation and insight. Think of it as having a capable assistant handling the tedious stuff so that you can spend time being creative and strategic.
Also, querying data will probably become more natural. People will ask questions in everyday language – “Which campaign in region X did well last quarter?” rather than writing long queries. Tools will interpret the intent, pull data and show visuals. That means data access becomes democratized, available to many more people, not only specialists.
Beyond that, the role will shift from being a “data cruncher” to being a “data storyteller”. Someone who not only looks at numbers, but asks what they mean, why they matter, and helps guide decisions based on those stories. Human judgment, context, nuance – these will still be quite important.
- Can you share personal experiences using AI in business and Data Analysis?
Answer: At my company, Techcanvass – an ed‑tech and consulting firm – we have an in‑house product development team and lately we’ve been building intelligent tools as part of our product lineup. I wear multiple hats, so beyond CEO duties, I serve as Product Owner. That means I help steer the roadmap, write specs, work with developers, etc.
In many tasks, I now use tools to help me: drafting acceptance criteria, modelling processes, creating spec documents. We recently found a tool (by Google) that helps mock up application designs and honestly, the results have been quite impressive. It speeds things up.
Using these tools has helped me save almost half my time and has reduced the communication gaps within the team. Less back‑and‑forth; clearer expectations. It’s made a real difference.
- Can you explain the significance of data quality and data governance in the context of AI and Data Analysis?
Answer: Data quality and governance are pretty much non‑negotiable if you want results you can trust and value you can count on.
Data is the fuel behind everything. Whether you’re working with large language systems or more traditional machine learning, you need accurate, consistent, complete and biased‑aware data. If the foundation is shaky, everything built on it can collapse.
Data governance is the framework that ensures that foundation stays solid. It defines who owns the data, who can access it, how it’s protected and how changes are audited. Without good governance, issues like misuse, leaks, or unintentional bias are much more likely.
You can think of data quality as the state of your data; governance is the blueprint that ensures quality is maintained over time. One‑time cleaning helps, but only governance ensures things stay right as systems evolve.
- Do you think data visualization impacts the way businesses communicate insights and make decisions?
Answer: Yes, visualization matters quite a bit in how businesses tell stories with their data and make decisions.
When you have lots of data coming in from different sources, it can be overwhelming. Good visuals – dashboards, infographics, charts – help people see patterns, spot when something is off, and act quickly. For instance, a retailer heading into the holiday season might use a dashboard to see which stores are low on hot‑selling items and realign inventory; that prevents lost sales.
Tools like Power BI, Looker, etc., make this mostly easier and faster to share across teams. It turns what might be vague hunches into clear visuals, so organizations can be more proactive rather than reactive.
- What certifications do you recommend for professionals in AI and Data Analysis to stay ahead?
Answer: It depends mostly on where someone is starting from. If you’re just getting into this field, free introductory courses (Coursera, EdX, etc.) are good in order to build your base. Andrew Ng’s courses, for example, are quite respected.
Here are recommendations by role:
For Business Analysts:
ECBA (Entry Certificate in Business Analysis) – ideal for beginners.
CCBA (Certification of Capability in Business Analysis) – suited if you’ve got 2‑3 years of experience.
CBAP (Certified Business Analysis Professional) – more for senior analysts working on complex stuff.
PMI‑PBA (Professional in Business Analysis) – useful if you also deal with project management.
IIBA‑CBDA (Certification in Business Data Analytics) – especially if you want to focus on analytics in a BA context.
AI Certification for Business Analysts (AIBA) – a course by Techcanvass meant to help BAs become capable of handling intelligent tools and related challenges.
For Data Analysts:
Google Data Analytics Professional Certificate – beginner friendly.
IBM Data Analyst Professional Certificate – includes hands-on training in tools like Python, SQL, visualization.
Microsoft Certified: Power BI Data Analyst Associate – emphasizes modeling and dashboarding.
Certified Analytics Professional (CAP) – more senior, covering a broad analytics lifecycle.
Data Analytics and AI Certification (DAIC) – a 10‑week program to build analytics from scratch and learn application development as well.
It’s also important to align certifications with what you want to do. Certifications are mostly useful when matched with a portfolio of real‑world projects that demonstrate you can apply what you’ve learned.
- Can you share any success stories or real‑world applications of skills gained through your certifications?
Answer: Sure. We recently worked with a mid‑career professional in financial services who wanted to shift into a Business Analyst role. She had domain experience but was missing some core BA skills. Through targeted upskilling and ECBA certification, she made that transition within six months into a fintech startup. Now she’s translating business needs into technical specs quite smoothly.
Also, our AI Certification for Business Analysts and for Data Analysts are structured in order to double productivity. We focus on tools, techniques and hands‑on application, not just theory. Since we design programs around actual industry expectations, people see results more quickly.
In order to understand better and learn more, you can visit Techcanvass.
- Do you think the demand for AI in business will increase in the future? If yes, how will it impact?
Answer: Yes, I absolutely believe the demand will rise significantly. Businesses are seeing what’s possible now, so investment is already increasing. But many are still figuring things out; only a few are quite mature in how they deploy intelligent technologies.
A McKinsey report from 2025 says that 92% of organizations plan to increase investments over the next few years, yet very few consider themselves fully ready. And a PwC estimate suggests a potential contribution to the global economy of up to $15.7 trillion by 2030. That gives a sense of the scale.
What does this mean in practice? First, many repetitive, data‑intensive tasks will be automated. Things like fraud detection, basic auditing, recruitment screening, customer query handling – those are mostly going to be handled more by systems. That frees people to focus on creative, strategic, or interpersonal areas, where human judgment matters.
Second, strategy itself will change. Companies will use predictive analytics, personalization, real‑time data, etc., in order to anticipate market shifts rather than just react. In places like logistics hubs, supply chains, retail, etc., this could reshape how things operate. Firms that adapt will probably gain much more: better customer insights, more efficient operations, and a clearer competitive edge.
