The frenzy around AI is reaching fever pitch even as businesses strive to design strategies to harness its full potential. This year in 2025 AI will seamlessly integrate into daily life, driving decisions and outcomes invisibly. This will usher in a shift in emphasis from AI innovation to improving implementation and measurable results. As AI cease to be an exception and become ubiquitous making our lives smarter, faster and more intuitive, organizations will be compelled to better design AI strategies that deliver.
Yet AI may be the biggest gamble in the business world today, because most organizations have not figured out how to get it right. Research indicates 80% of AI projects fail. But the pressure to deliver is knocking on the door and organizations must now make concerted efforts to scale up AI deployments and amplify the value with deep integrations and seamless interconnections. But in the process, CXOs must resist the pressures to deliver quick results and focus on designing a thoughtful AI strategy that is robust and comprehensive.
Without an AI strategy at the organization level, there is bound to be chaos and wasted efforts. AI strategy for enterprises must keep in view long-term business outlook to remove the constraints of traditional business and redefine with new operating models for sustainable growth.
How to Design an AI Strategy for Business
Given that AI strategy will influence business outcomes, AI must be integrated into operations, decision-making and growth plans. This requires not just technology adoption but a cultural change in the way employees think, behave and act.
AI strategies of best-in-class organizations comprise a technology foundation and a cultural shift. It calls for aligning the business model, goals and preparing with new skill sets. At the same time, a crucial peg in the AI strategy for business is a strong data foundation. AI leverages data analytics, machine learning and automation all of which require massive amounts of data that must be systematically gathered, stored, and processed.
Steps to Implement AI Strategy in Enterprises
Working with a large number of organizations across the spectrum, NewVision has gained insights into crucial areas that ensure successful AI implementations. Here are a few things to keep in mind while designing a robust strategy for enterprise AI.
Identify Business Goals and Objectives: An AI-driven business strategy must be reinforced with an AI-scorecard that assesses the organizational readiness to adopt AI and aligns it with business goals and stakeholder requirements. It must assess things such as integration challenges; architectural issues related to data collection and processing; availability of skill sets, and capability building strategies.
Use Cases that Solve Business Problems: Identify and start with uses cases that are high impact and addresses real business issues. For example, a manufacturing organization may do well to focus on a predictive maintenance solution to ensure high performance and efficiency, while organizations having large customer interfaces—such as those in retail and travel—will benefit by focusing on automating customer service to increase efficiencies while also mining data for better customer service.
Evaluate the Data Strategy: Making a comprehensive assessment of the data strategy is crucial as data is the foundation of an AI strategy. This entails identification data silos; assessment of data sources; reviewing data quality; designing strategies for data accessibility and defining a governance policy.
Choose AI Tools and Technologies: Selecting the right tools and technologies aligned with your business goals and objectives. For instance, NLP is the most appropriate technology for sentiment analysis, just as implementing machine learning and IOT sensors is the best approach if the objective is to predict equipment failure and increase operational reliability.
Establish Ethical Framework: As AI begins to influence decision-making, embedding ethics in data collection and storage, monitoring algorithm for bias, and instituting principles for transparency will go a long way in providing a secure foundation of organizational AI strategies.
Skill Development Roadmap: A strategic approach to addressing AI skill requirement will help address the gaps in demand and supply. Identify the needs and the gaps to define what approach is most suitable. Upskilling employee to develop in-house talent can be combined with lateral hiring. Working strategically with an experienced partner with the requisite skills is also a smart approach.
Ensure Stakeholder buy-in: Clearly communicate the AI strategy, goals and objectives, the roadmap and the next steps. An AI strategy for digital transformation will clearly affect operations and operating model and therefore stakeholder buy-in is a crucial step in ensuring successful AI implementation.
Common Challenges in AI Strategy Development
As an emerging field, there are bound to be challenges and learnings while designing and implementing AI strategy for enterprises. Here are a few common pitfalls to address early in the journey for successful AI implementation.
Metrics not aligned with business objectives: What is being measured will play a crucial role to assess the impact. For instance, if the metrics to measure the effectiveness of a chatbot in customer service is pegged as the number of conversations, it will be incorrect because the responses may not be accurate. A correct measure is to access the CSAT of chatbot engagement or issue resolution to reflect the user experience.
Embrace a Modular Platform: Avoid seeing AI projects as one-off project. Instead, a long-term strategic view and a modular AI framework will help scale and amplify the value of different AI deployments. As AI-bolstered businesses evolve and grow, a flexible and modular platform will help to adapt and add new capabilities with technology advancements and requirements changes.
Absence of wholistic data strategy: Data being the foundation for robust AI strategy for enterprises, quality and quantity of data is crucial. Inadequate data and poor quality of data will affect the data models and thus decision-making. A comprehensive data strategy entails extracting data trapped in legacy systems and enriching it with external data sources. A sound data strategy supported by data fabric, data pipelines to extract, collect, store and enrich data must be thoughtfully designed and implemented.
Inadequate Skillset and Small Talent Pool: The quest for AI success can come a cropper in the absence of skilled employees. A Reuters study finds that there is a shortfall of 50% in all hiring related to AI, while the demand to upgrade in-house employees with AI skillsets is pegged at 70%. To retain the competitive edge, large global behemoths such as Amazon have instituted strategic programs that aims to make two million employees AI-ready by the beginning of 2025. A thoughtful strategy for skill development is required to address the skill gap—while AI fundamentals can be taught, applying AI for specific business outcomes is fraught with variables including business, regulatory, compliance and other contextual challenges.
Benefits of a well-planned AI strategy
Despite the hype and aura around AI, according to US census data only 5% American companies use AI in 2024, up from 3.5% in 2023, indicating that many organizations are not sure of how to harness AI potential.
A well-planned AI strategy will equip organizations with disruptive capabilities and future-proof AI investments. Given the rapid pace of technology advancements the benefits of a well-planned strategy cannot be overlooked.
This must begin with an understanding of AI technologies, an assessment of existing capabilities with a long-term view of organizational goals. AI management and orchestrating a cultural shift internally are important pillars in the AI roadmaps. There must be clarity and transparency while adopting AI tools wherein employees are encouraged to embrace AI tools—including GenAI—to boost productivity without being afraid of getting penalized.
The scale and impact of AI cannot be harnessed in isolation and therefore AI strategies of enterprises must be permeated with a wholistic understanding of the businesses, while considering external factors, including ethical considerations and political implications.
