Aligning AI Strategy With Customer Needs

Companies keep asking me how to “get their AI strategy right.” But most of them are skipping the step that matters most: 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. If you don’t know: • Who your highest-value customers are • What keeps them loyal • Where the biggest friction lives • Which moments matter most in their journey Then AI is just going to scale whatever guess you’ve been working from. When we design AI for customer service, we don’t start with models or prompts. We start with the customer blueprint: → Segmentation by behavior, not just demographics → A map of high-impact moments → A list of “never fail” interactions → The real metrics that matter for retention and revenue Once that’s in place, AI becomes the execution layer, not the guesswork layer. An AI strategy without a customer strategy is just automation in search of a purpose.

The best AI strategy is a set of prioritized customer problems. AI transformation leaders are flooded with potential use cases. When there are infinite next steps, how do you figure out the right one? I spoke with Ashok Srivastava, Chief AI Officer at Intuit, for my course on Organizational AI at Columbia Business School. Ashok says there’s only one place to start: “Understand the customer problem you are trying to solve. Not the business problem. The customer problem.” Once you know the customer problem, gather the right data sources, identify organizational constraints, and implement the technology that can solve the problem. Case in point: Ashok, who has been driving AI at Intuit since 2017 (before agents were a buzzword!), helped transform the company by building a trusted financial intelligence operating system that now serves 100 million customers. It all started with a single problem: let customers focus on what they actually care about, not worry about accounting, payments, and bookkeeping. Customer problems come first, technology comes last.

Klarna cut 700 jobs for AI. But they are now bringing humans back, saying customers like talking to people! Last December, Swedish fintech Klarna made headlines for replacing most of its customer service team with AI chatbots, stating that the bots could do the work of 700 agents. But a few months later. They’re now hiring people again. Here’s what happened: ■ Yes, queries were closed faster, but customers felt unheard. Loyalty suffered ■ Klarna’s numbers now show that live agents delivered better ROI than bots. Not surprising because real relationships still drive real revenue. ■ Trust and loyalty were lost, affirming that quick resolutions mean nothing in the absence of trust and loyalty. And yet, a recent IBM study found that 64% of CEOs admit they invest in technology, including AI, before fully understanding its value, driven by the fear of falling behind. This can result in costly missteps and eroded customer trust. So, how can companies adopt AI in CX the right way? At X-Shift, we combine strategic clarity with extensive operational expertise to guide AI implementation that enhances customer experience. Here are six considerations we keep in mind while deploying AI: 1. Current state assessment. Understand your existing CX performance, pain points, and digital maturity 2. Use case identification. Spot high-impact areas for AI, especially where humans and machines can complement each other 3. Technology landscape preview. Recommend the right tools and platforms that align with your goals, including Agentic AI 4. Data strategy alignment. Ensure data is unified and flows across all channels to power smarter AI 5. ROI value modelling: Build multi-layered ROI models tailored to your transformation goals 6. Adoption & change management planning. Design frameworks to embed AI within your culture and workflows, ensuring sustainable change We believe that when done right, AI doesn't replace your team; it amplifies their impact, ensuring that your customers feel heard, your brand remains human, and your technology truly works for you. #CX#AI #CXStrategy #Leadership #KSA

In my recent conversation with Natalie Han, PhD, VP and CPO of Gen AI at SAP, we got into a topic I think every company is wrestling with right now. When AI can theoretically touch every part of the business, how do you decide what to build first. What stood out to me was how practical her lens is. ✦ It always starts with real customer engagement. Not whiteboard ideas, but the work of helping customers implement systems or streamline existing processes. That’s where the true signals show up. ✦ Some use cases consistently surface as high value. Text-heavy generative workflows tend to create immediate impact because they free up time in places people don’t even realize they’re losing it. ✦ Prioritization only works when it’s anchored in value. Looking at the total time spent, the impact created, and the actual effort saved keeps teams honest about what will move the needle. ✦ And none of it matters unless the technology is ready. The best opportunities sit where business need and technical feasibility meet. The conversation reminded me that AI strategy isn’t about chasing the widest set of possibilities. It’s about choosing the few that create momentum, trust, and real wins for the people using the system.

The rapid development of artificial intelligence (AI) is outpacing the awareness of many companies, yet the potential these AI tools hold is enormous. The nexus of AI and emotional intelligence (EQ) is emerging as a revolutionary game-changer. Here’s why this intersection is crucial and how you can leverage it: 🔍 AI can handle data analysis and repetitive tasks, allowing humans to focus on empathetic, creative, and strategic work. This synergy enhances both productivity and the quality of interactions. Imagine a retail company struggling with high customer churn due to poor customer service experiences. By integrating AI tools like IBM Watson's Tone Analyzer into their customer service process, they could identify emotional triggers and tailor responses accordingly. This proactive approach could transform dissatisfied customers into loyal advocates. Practical Application: AI-driven sentiment analysis tools can help businesses understand customer emotions in real-time, tailoring responses to improve customer satisfaction. For example, using AI chatbots for initial customer service interactions can free up human agents to handle more complex, emotionally charged issues. Strategy Tip: Integrate AI tools that provide real-time sentiment analysis into your customer service processes. This allows your team to quickly identify and address customer emotions, leading to more personalized and effective interactions. By integrating AI with EQ, businesses can create a more responsive and human-centric experience, driving both loyalty and innovation. Embracing the combination of AI and EQ is not just a trend but a strategic move towards future-proofing your business. We’d love to hear from you: How is your organization leveraging AI to enhance emotional intelligence? Share your thoughts and experiences in the comments below! #AI #EmotionalIntelligence #CustomerExperience #Innovation #ImpactLab

Microsoft state for every $1 spent on AI, our customers expect to realize a 3.5 times return. When we speak to customers, working within their hardcore industries, we find they are grappling with their plans for enterprise scale #IndustrialAI adoption. We’ve tuned into their challenges of how to align Business, IT and now AI strategies; at the same time demonstrating value from AI back to the business and achieving those fabled returns. We analyzed the BCG concept of the AI Jagged Frontier™ - where some tasks are easily done by AI, whilst others sit outside the current capabilities of AI and are difficult to derive value from – and realized this directed our customers towards our ready to use Industrial AI use cases that IFS.ai provides. Use cases that sit within the frontier, where the value is easier to achieve and has a more direct impact on the business. It also cautions customers from spending huge effort trying to adopt use cases that sit outside the frontier and run the risk of project failure and value destruction. We’ve been able to pair our IFS.ai product approach with our proven IFS Success Framework, so customers can use the same tooling to identify, realize and demonstrate the value and return from their AI investments. A word of caution though, to gain access to those returns through AI, the foundation must be laid: 1) A focus on value: understand what’s inside your AI frontier, select the use cases that deliver the greatest value for the lowest effort and measure the outcomes 2) Get data ready: review your data strategy, hygiene, architecture, policies and governance. This isn’t a one-off task – data fuels your AI – and must be treated as a key resource 3) Support people: have enough people dedicated to capturing and demonstrating AI value, understand where resistance will come from and manage the change 4) Streamline operations: think about the role of AI decision making in your operations, be clear on how to handle exceptions, understand how the interface between the AI and the human comes together in your operation 5) Update your Cyber Security approach: make sure it’s fit for the AI revolution, understand the impact on your business if your data is compromised. The great news for our customers is that they are already on the journey. We’ve designed IFS.ai, IFS Success and our partner offers to support customer through every step. #IFSUnleashed

Instead of thinking “AI first,” focus on where #AI delivers the use cases with the most value. J&J is moving “from the thousand flowers to a really prioritized focus on GenAI” after finding that only 10% to 15% of use cases were driving about 80% of the value. It seems odd to have to say point out that we should focus on where AI best delivers, but with each new tech hype cycle, we need to be reminded that being tech first is never the solution. Focus on what customers and the business most needs, then figure out what tech best delivers. At J&J, employees had been pursuing nearly 900 individual use cases, but the company found it got the most significant value by using generative AI for drug discovery, supply chains, and internal chatbots. The internet was promised as a cheap and easy “24/7 storefront,” but only added to competitive pressures. Social media was promoted as “free advertising,” but created new challenges to managing reputation and new channels to be maintained. Like past tech, AI is not a plug-and-play solution to the complexities your organization faces. AI will best enhance the business of organizations who know their customers, have a sound and disciplined strategy, and are prepared to evaluate where AI best fits and where the costs, risks, and capabilities make AI an unwise an investment (for now). https://lnkd.in/gYK4e9gU