How do you know if AI is the right solution for business transformation?
Businesses perceived AI as experimentation, but slowly they started realizing the hidden value. AI has the potential to transform every business function and industry. AI is not only about cost savings but improving revenue opportunities and customer experience. As the AI maturity curve changes, many organizations have moved from the proof of concept to operationalizing AI at scale. A few organizations proliferated AI across functions with best practices and attributed cost savings.
AI is changing the strategic thinking of business leaders. As per McKinsey’s research report, 55% of businesses leverage AI in at least one business function, and 27% of companies witnessed cost savings equivalent to 5% of earnings before interest and taxes. Around 70% of the companies also accelerated their AI strategy during the Covid crisis.
The critical factors in determining if AI is the right solution are the long-term business value, availability of relevant datasets, strategic direction, cultural transformation, and skillset availability. Let me navigate how to ensure the criterion aligns with your AI plans.
The challenges with AI adoption
It is not out of the blue that businesses and vendors are thinking about AI adoption in every function and business workflow. But not all of them succeed. The failed AI projects lead to many costs and efforts and hamper customer, employee, and stakeholder experience and satisfaction. Though it looks simple on the cover, let us look at the challenges with AI adoption
- A few organizations perceive AI as a trend to adapt. Most of them fail to find a scalable use case that can impact future business outcomes.
- Most PoCs fail to consider the value generated through any AI implementation. It is critical to decide what value looks like over the near term, short term, and long term while implementing scalable solutions.
- Organizations often face challenges finding the right vendor, comprehensive tools, and relevant technical acumen to fit their AI scope and vision.
- AI teams fail to articulate the solution for business value and transformation. With the advances in technology, AI solutions have more transparency – the models, datasets, and how it works. When a model contradicts business leaders’ thinking, they prefer to understand the reasons in-depth. Data scientists often find this challenging.
- AI technical skills are already scarce, and hiring the needed architects and data scientists or upskilling the existing workforce needs a strategic direction from business leaders.
Why do businesses invest in AI?
Covid crisis has accelerated the adoption of new technologies like data, AI, automation, and the cloud. But the driving forces for AI are slightly different from others.
- Businesses realized the value of AI and analytics – Organizations now rely on data to make strategic decisions and create new business models. They prefer to lay their foundations for being data-driven. New algorithms and tools no longer remain distant. Businesses are looking for insights-powered solutions to optimize their operations, improve customer satisfaction, and create new business models.
- AI and analytics help improve operations – Earlier organizations didn’t rely much on technology to improve their processes. With advancements like RPA and AI, repeated workflows are automated based on process insights. Intelligent automation is garnering attention in every industry.
- New products, services, and business models are evolving – The recent advancements in AI and analytics continue to understand the demand, consumer preferences, shopping mode, and touchpoints. With these changes, organizations can rethink their products, services, and business models.
- Enhanced supply chain visibility – Though it is still early to decide on the maturity of AI-driven supply chains, organizations are looking to improve their products’ visibility from source to destination.
How do you determine if AI is the right solution?
As growth and competition pressure mount in the rapid digital world, businesses need to consider many cues before investing in a solution. Let us look at the six-pointers that need consideration before determining if AI is the right solution.
- User needs – AI is not a simple tool that businesses can start leveraging without any approval from the stakeholders. For any AI project, the availability of data is a crucial step. Once you identify the data, it is essential to understand if AI can solve the real-life challenge or if a minimal human effort can solve the problem. Considering other factors like data governance and ethical data also impacts AI implementation. Understanding the user needs to determine if AI can solve the business challenge or humans can do it stress-free.
- Integration with existing systems – At times, it is time-consuming and costly to integrate AI models and algorithms into existing workflows. A seamless connection between systems lets you start quickly with your AI projects. At the same time, organizations may not need any custom development that leads to productivity issues in AI implementation.
- Analysis of the current state – Before considering an investment in any AI solution, organizations need to access their data state. The data must be accurate, consistent, relevant, and timely for any AI model. The data quality impacts the outcome of the AI solution and the trustworthiness of the technical use.
- Build vs. Buy – Organizations need to decide if they can build or buy the required AI solution considering AI projects’ needs, scalability, and business value. If you want to buy the solution, you need to look at the maturity and implementation challenges of the existing products. Organizations need to ensure the right technical team and strategic direction if they decide on building the AI solution.
- Governance and responsibilities – Before implementing any AI project, a proper allocation of duties between data teams and strategic leaders is critical. Without causing any miscommunication, organizations need to ensure the governance framework among all the stakeholders. A clear accountability framework for the deployed models provides the right fit for AI solutions and faster time to insights.
- AI skills assessment – Any AI project requires multiple skillsets ranging from data scientists to domain experts to data engineering teams. Ensuring the right balance between the teams and the skillsets is essential for the successful implementation of any AI project. Each team’s roles, responsibilities, and milestones also need to be planned well before starting an AI project.
Many AI projects fail because they lack the strategic direction from idea to implementation. Also, it is tough to update everything from scratch and continuously in today’s complex tech environment. You can always choose a vendor with AI implementation expertise and industry knowledge to overcome all these challenges. Are you looking for a faster time to value your AI projects? Get in touch with our experts to transform your business with AI in no time.
Khalil Sheikh is the Executive Vice President of Solutions and strategy at Saxon. Under his leadership, Saxon is building ROI driven Data Science AI/ML and recommendation engines for its fortune 1000 customers. Khalil has extensive experience in the Software & IT services industry and in turning around businesses through actionable business intelligence leveraging AI. With more than 28 years of experience across the industry verticals, he has led several successful Data Science & digital transformation journeys for ISV’s and enterprises. He led CTO/CIO forums in the valley and is known for fostering creativity, collaboration, and diversity.