By implementing AI, some companies have gained a competitive advantage by improving their operational efficiency and customer experience, while others have ended up in hot water. Technology companies like Intellias can assist you in choosing the right kind of AI tools – and implementing them correctly. Our Design Thinking Workshop applies an AI adoption framework from a user-centric design perspective to make sure that AI benefits your company and your users while minimizing the risk of mishaps.
Without AI, businesses risk losing competitiveness, operating inefficiently, and missing out on revenue opportunities. The right AI adoption framework helps businesses automate processes. It enables digital transformation and empowers AI-driven decision-making, which results in personalized customer interactions for long-time success.
There are plenty of cautionary tales that illustrate AI gone wrong. Air Canada faced fines when its virtual assistant provided incorrect information to a passenger. ChatGPT cited legal precedents that do not exist to a lawyer seeking case law for a suit against the Avianca airline. Tutoring company iTutor Group paid a six-figure amount to settle a lawsuit for age-based discrimination because it programmed AI software to reject applicants of a certain age. Even Meta’s highly anticipated AI assistant – the company’s answer to OpenAI’s ChatGPT – has failed to live up to the hype, making mistakes in facts and numbers, not to mention the erroneous web search results.
At the same time, 80% of CIOs foresee increased involvement with AI and machine learning initiatives in 2024, up from 55% last year, according to Foundry’s “State of the CIO 2024” report.
AI is changing the rules of the game, driving transformation through automation, forecasting, and hyper-personalization. Businesses leveraging AI gain not just efficiency but also the ability to make data-driven, forward-thinking decisions.
What gives? While AI technology seems to hold promise, the failures experienced by Air Canada and other companies showcase that implementing AI incorrectly can carry the risk of a tarnished reputation and financial damage. The solution is to thoroughly research the advantages and shortcomings of available AI products.
At Intellias, our AI/ML services are laser-focused on aligning AI implementation with business needs and customer expectations. That’s why we’ve developed our artificial intelligence adoption framework – the Design Thinking Workshop. This mature, tailor-made roadmap tackles the specific problems of businesses looking to innovate with AI.
Key takeaways:
- AI adoption framework helps businesses enhance efficiency, boost competitiveness, and increase revenue.
- AI-driven automation and forecasting reshape industries by optimizing decision-making and reducing risks.
- Incorrect AI implementation can lead to costly mistakes, as seen in high-profile failures.
- Companies need a structured approach like Intellias’s AI adoption framework to ensure AI aligns with business goals.
Overcoming AI implementation challenges
Implementing AI in business is about ensuring it serves real business needs. Many companies dive in headfirst, only to realize they’re facing roadblocks:
- Mismatch of AI solutions with real business tasks. Companies often implement AI for the sake of AI, ending up with expensive tools that don’t solve real problems.
- Ethical risks. Bias in data can lead to discrimination, regulatory issues, and a serious PR nightmare.
- Lack of a clear strategy for integration. Without a structured approach, AI initiatives become disjointed experiments rather than game-changing solutions.
Without a framework for applying AI in the enterprise, businesses risk deploying tools that generate inaccurate insights, reinforce biases, or fail to improve efficiency.
At Intellias, we use a structured AI implementation framework to tackle these issues. For example, when developing AI-driven personal finance solutions, we ensure machine learning models are trained on high-quality big data to prevent biases. Our approach to predictive analytics helps clients forecast trends accurately while avoiding compliance risks. Additionally, by integrating AI assistants into customer service, we enhance automation without sacrificing accuracy.
The purpose of an AI adoption framework is to align AI with business goals, ensuring a seamless, responsible, and effective deployment.
AI adoption frameworks: Choosing the right approach
Clients often ask us which artificial intelligence (AI) solution they should implement. There is no universal answer. Despite the rapid advancement of AI technology, it may not be the right fit for every company. Before adopting artificial intelligence, an organization should carefully assess how AI technology aligns with its goals.
To make an informed decision, an organization must choose an AI adoption framework that aligns with its specific needs—whether it’s cost optimization, increasing profitability, transforming business strategy, or gaining consumer insights.
Let’s explore the advantages, limitations, and key components of an AI adoption framework to help you determine whether AI technology is the right fit for your business.
Business transformation with AI
The framework for applying AI in an enterprise is perfect for companies seeking a structured approach to adopting artificial intelligence, including an appropriate solution for comprehensive digital transformation initiatives. It helps evaluate business readiness, define strategies, implement solutions, and continuously monitor performance.
This is a strategic model for large-scale digital change that integrates AI across the enterprise.
Pros:
- Aligns AI initiatives with strategic business objectives
- Focused on continuous improvement and ongoing optimization
Cons:
- Requires significant resources and takes a long time to implement
- Complex processes due to the framework’s comprehensive nature
AI implementation roadmap
This is a detailed plan for businesses to integrate AI into their operations. From setting up infrastructure to developing AI models, the framework addresses each phase of AI implementation. This clear, step-by-step guide for incorporating AI into operations, delivers cost optimization through practical milestones.
Pros:
- Straightforward, sequential, and easy-to-manage approach to implementing AI
- Practical steps during the entire journey
Cons:
- Lack of flexibility and adaptability
- Significant resources necessary for each step
Ethical and responsible AI deployment
As the name suggests, the AI adoption framework focuses on transparency and fairness. It prioritizes ethical principles and a commitment to responsible implementation of machine-powered solutions.
Pros:
- Unbiased and transparent AI solutions
- Accountability for AI outcomes
Cons:
- Complex implementation requiring consistency across all processes
- Slower path to innovation due to strict ethical guidelines
AI maturity model
The AI maturity model provides a clear roadmap for gradually scaling AI solutions. Ongoing optimization and continuous improvement ensure that solutions remain effective over time.
Pros:
- AI solutions can scale as the organization matures
- Clear stages of the AI journey, from awareness to transformation
Cons:
- Dependency on completing each stage can slow progress
- Potential gaps between business needs and AI solutions
Design thinking approach to AI adoption
Design thinking helps balance technological enthusiasm with real-world AI applications. Iterative prototyping and testing ensure that AI solutions are technically feasible and deeply resonate with end-users.
Pros:
- Creative problem-solving and innovative ideas
- High-quality solutions due to iterative processes and continual refinement
Cons:
- Time-consuming iterative nature
- Technical and feasibility constraints
Design thinking as the most effective approach to building AI-based projects
Design thinking offers a practical, human-centered, and iterative approach to tackling complex problems. From this perspective, all AI-based products and services must account for the needs of customers, the hurdles they overcome on their user journey, and the emotions they experience.
When it comes to AI adoption, a design-led approach is key, as it reframes the company’s challenges from a user-centric perspective. Design thinking focuses on understanding end-user needs to develop innovative solutions through collaboration and experimentation.
Many big-name companies – including Google, IBM, Airbnb, PepsiCo, and Nike – already incorporate design thinking into their operations. For example, IBM’s AI research lab applies this methodology to develop trustworthy AI models, while Airbnb uses design thinking to redefine personalization in its search algorithms.
Indra Nooyi, a former CEO of PepsiCo and an advocate of the design thinking approach, shared with Harvard Business Review that design influenced almost every major decision during her years as CEO, contributing to an 80% sales increase during her tenure.
During an Intellias Design Thinking Workshop, we help clients validate the value of customer-centric AI solutions in their business context. Our design-led artificial intelligence adoption framework encourages diverse perspectives, iterative experimentation, and stakeholder collaboration. We ensure that AI solutions seamlessly integrate into existing workflows. We also look at the bigger picture: the ethical, legal, and societal implications of incorporating AI technology. This enables business process automation, optimizes key operations, enhances revenue streams, and refines their AI-driven business model.
Our workshop unites technologists, executives, analysts, and cross-functional teams — including risk management, operations, sales, and customer-facing advisors — to explore new ideas, provide feedback, and drive customer-centricity. This ensures technology adoption lifecycle is not just innovative, but also practical, ethical, and sustainable.
The Intellias Design Thinking Workshop: competitive edge through AI
AI adoption is more than just integrating new technology — it’s about finding solutions that create real business value. The Design Thinking Workshop at Intellias relies on proven methods and approaches that include user-centered exploration, brainstorming and AI strategy creation, defining the value proposition, and, most importantly, continuous feedback and testing.
Our process is built around four key phases:
- Research and empathic analysis: We start by deeply understanding your business model, industry landscape, and pain points. Through market analysis, client surveys, and competitor research, we validate hypotheses and uncover critical insights.
- Ideation and brainstorming: We facilitate collaborative discussions to explore AI models, business challenges, and potential applications. This phase fosters creativity and helps refine AI strategies tailored to specific needs.
- Developing the business case and value proposition: Intellias translates ideas into AI solutions, forecasting investment, risks, and ROI while mapping a clear AI implementation framework.
- Hypothesis testing and feedback: AI solutions are validated through MVPs, pilot projects, and A/B testing. User insights and business performance data help fine-tune the strategy for maximum impact.
By implementing an AI adoption framework, we ensure that companies make informed decisions, mitigate risks, and accelerate their AI-driven transformation. Let’s take a closer look at how each phase unfolds in practice.
Empathic exploration
Before crafting a strategy for adopting any technology (and especially AI), we need to gain an in-depth understanding of the client’s challenges and aspirations. Our process begins with a comprehensive pre-discovery phase. During this phase, we immerse ourselves in our client’s world by thoroughly researching the client’s business and industry. We identify and discuss needs and productivity bottlenecks, pain points, and obstacles to growth.
Recently, we worked with a client to determine whether AI was suitable for their needs. Part of our work involved assessing whether AI could drive growth and expansion; another part was ensuring that AI implementation was both secure and ethical. Throughout this process, we emphasized the responsible and ethical use of data and AI.
We created a discovery roadmap tailored to the client’s unique market dynamics. Client surveys validated assumptions and revealed blind spots. By aligning ourselves with the client to see their perspective, we were able to design AI solutions that integrated new technologies. Pre-discovery laid the groundwork for innovation that propelled the client’s business model ahead of the competition.
Ideation and brainstorming
As part of a Design Thinking Workshop, we analyze a client’s business model, processes, and data stances, define core operational problems, and uncover technology-powered opportunities. We then facilitate a semi-formal and relaxed brainstorming session, creating a trustworthy environment and encouraging unconventional thinking to generate ideas.
These sessions are crucial for exploring fresh perspectives and out-of-the-box ideas that could provide powerful future solutions.
After seeing impressive natural language processing models, one of our clients initially focused solely on generative AI. However, through our design thinking process, the client realized that while generative AI is powerful, it’s just one tool among many in the analytics toolkit. The company recognized opportunities to apply various techniques including regression modeling, neural networks, and optimization algorithms to best address its business challenges.
The key was determining which specific analytics technique best fit each problem. While deep technical expertise wasn’t required, we enabled our client to have more focused conversations with technical experts, asking the right questions to develop an analytics strategy tailored to their business goals.
This stage consolidates research findings to pinpoint user needs and articulate the problem statement, ensuring precision and focus in addressing challenges head-on. Adopting a user-centric approach allows our clients to synchronize their efforts with user requirements, minimizing the risk of developing solutions that miss the mark.
Business case and value proposition
The Design Thinking Workshop by Intellias is a dynamic platform where participants engage in solution discovery, ideate, and visualize potential roadmaps to align technological innovation with human-centric outcomes.
In the next stage of our workshop, we flesh out selected ideas into detailed concepts. This phase marks the transition to tangible proposed solutions, articulated through business cases where hypotheses begin to take form. Stakeholders explore visual representations and workflows of the most promising concepts. We highlight the strengths and limitations of proposed solutions; participants prioritize ideas. Validation scenarios are crafted to test and refine these solutions, ensuring they meet user needs and expectations.
We then draft a solution to create a compelling business case and define the value proposition, emphasizing the benefits of AI adoption framework and return on investment for stakeholders. Additionally, we outline a roadmap detailing possible features, balancing creativity with actionable outcomes to address real-world challenges.
Feedback and refinement
This stage involves revisiting the original challenges and success criteria to ensure the future solution meets set goals. We collect, synthesize, and prioritize feedback to review the solution and assess how it meets the original and possibly revised goals.
We also compare success criteria checklists, including mandated challenges, user needs, wants, and feedback. If the lists don’t line up, we refine further. This comprehensive approach assesses the solution through multiple contexts and perspectives, identifying opportunities for change and improvement.
Report and conclusions: investing in reality
In the final step of our design thinking for AI workshop, Intellias provides stakeholders with documented findings, highlighting both the limitations and advantages of proposed solutions. Through gap analysis and estimation, we identify areas ripe for innovation and development, with a focus on helping our clients use their strengths to the fullest.
Post-workshop deliverables include coherent and practical conclusions and recommendations. From clearly defined user personas to intelligible summaries of proposed solutions, we equip clients with a clear understanding of their future investment perspectives. Our retrospective sessions ensure that our solutions are not only innovative but also finely tuned to our clients’ needs.
Effective design thinking practices
Design thinking workshops offer numerous advantages for responsible AI adoption and implementation. By applying the following core design thinking principles in workshops, companies can create AI solutions that resonate with users.
A user-centric approach
Prioritizing user empathy distinguishes leaders from laggards. A design thinking workshop aligns your team to put the customer first through every stage of AI development and rollout. Forrester reports that companies applying design thinking see higher customer satisfaction and increased revenue growth. Design thinking is a proven path to developing intuitive, user-friendly AI products and services that delight customers.
Take Apple: its user-centric design is why millions love the company’s products. Customers are at the heart of every design decision Apple makes, and it pays off: almost a third of all phones on the market are iPhones.
Similarly, the Design Thinking Workshop at Intellias is geared towards designing solutions that address the needs and preferences of clearly defined user personas. Placing users at the center of the process, we guide workshop participants to validate assumptions, solve user pain points, and implement and improve AI solutions based on real-world feedback.
A continuous innovation cycle
The design thinking framework relies on techniques that include brainstorming and mind mapping. They empower teams to generate innovative ideas, along with giving them the freedom to experiment. This approach leads to more than just successful tech rollouts; teams become flexible and adaptable to evolving user needs and market dynamics.
The result? A continuous innovation cycle. Design thinking methods such as co-creation, feedback loops, prototyping, and robust testing clearly demonstrate to our clients whether proposed AI solutions resonate with users and deliver the anticipated experience.
A culture of collaboration
Interdisciplinary design thinking workshops streamline collaboration and, as a result, development cycles. They unite diverse teams, fostering interoperability and efficient workflows. Cross-functional teams can combine their skills to generate effective solutions for complex challenges – which may include bold experiments, multiple iterations, and viewing failures as growth opportunities.
Creative problem-solving and a culture of collaboration are essential to design thinking. Our workshops foster a collaborative environment that unites teams around project goals – and that collaboration extends beyond the workshop sessions.
A clear focus
Design thinking prepares companies to become more flexible and ready for the future. The design thinking framework challenges traditional assumptions through its iterative fail fast, fail forward approach and creates a process of continuous feedback. It prioritizes real-world testing, user feedback, and data-driven decisions and avoids the sunk cost fallacy. This iterative cycle guarantees that solutions evolve based on user insights.
For example, IBM fosters an agile, evidence-driven approach to innovation that can be scaled across teams, departments, and multinational organizations through user-centered prototyping and iterative evolution. Understanding the end-user is the focal point of the solution-oriented process.
The result is a clear picture of what to focus on and a roadmap for the next steps. Through immersive design thinking workshops, our clients have gained insights into delivering user-centric solutions that surpass competitors. This focused approach leverages user data to make informed decisions and innovate while continuously refining AI solutions based on user feedback and market changes.
Why do companies choose Intellias for AI solutions?
Looking for AI solutions, companies need a partner who understands how to make AI work in the real world. That’s why businesses choose Intellias. With a deep background in AI/ML, we’ve helped companies across industries turn AI potential into tangible results. Our AI implementation framework ensures that every AI solution is not just innovative but also strategically aligned with business goals.
What sets Intellias apart?
- Flexible, tailored AI strategies. We don’t believe in one-size-fits-all AI. Instead, we develop solutions that fit seamlessly into existing workflows and scale as business needs evolve.
- AI that drives real impact. From predictive analytics in retail to intelligent automation in finance, our AI solutions are built to deliver measurable business outcomes.
- We provide end-to-end support. We guide companies through the entire AI journey, from defining strategy and selecting the right models to implementation and continuous optimization.
Intellias prioritizes a strategic approach to AI implementation. We help companies in different domains drive efficiency, innovation, and competitive advantage with our AI solutions:
IntelliAssistant
IntelliAssistant is a robust AI-powered platform, built to supercharge productivity and take the busywork off teams. Whether it’s automating admin tasks, speeding up coding, or delivering real-time insights, this intelligent assistant helps businesses work smarter, not harder. With a plug-and-play infrastructure, it seamlessly integrates into corporate systems, adapting to unique business needs. Secure, compliant, and built for cross-functional teams — from IT and sales to marketing and HR — IntelliAssistant turns AI into a true game-changer for your business.
IntelliCopilot
IntelliCopilot is an AI-powered partner to boost productivity, cut costs, and accelerate software development. Built and refined through Intellias’s most demanding projects, it delivers real impact — driving a 25% increase in engineering productivity and making information retrieval 79% faster. Unlike standard AI copilots, IntelliCopilot is fully customizable to fit specific business workflows while ensuring top-tier security, compliance, and data protection.
Helping you build AI/ML capabilities
The question most businesses face is not whether to use AI but rather how to implement AI products while creating the best user experience without the risk of error. Success demands a deliberate approach in selecting the right technologies and implementing them correctly.
Technology companies can look past the hype and guide organizations through the multiplying AI possibilities. Intellias Design Thinking Workshop is your navigator to implementing artificial intelligence solutions that benefit your customers and grow your business.
Figuring out where to start? Contact our experts today, and let’s do AI the right way.
