Artificial Intelligence is not the future—it’s the now. And yet, for many insurance and finance companies, AI remains a buzzword echoing in boardrooms rather than a tool transforming operations. The benefits are undeniable: faster claims processing, hyper-personalized banking, fraud detection, automated underwriting—the list goes on. So why are so many firms still lagging behind?

Welcome to the messy middle of digital transformation. At Yameo, we’ve worked with countless companies in insurance and finance across Europe who are ready to embrace AI, but hit the same brick walls. Let’s unpack the 5 real AI adoption challenges—and how to get past them with confidence. 

1. Data Quality & Availability: The Foundation Is Fractured

You can’t build a smart system on dumb data. AI thrives on clean, structured, and plentiful information—but most legacy insurance and finance systems were never designed with AI in mind. Disparate databases, outdated formats, siloed departments… sound familiar? 

The Fix: Start with a data audit. Invest in data warehousing, cleaning, and integration. Implement robust data governance frameworks and consider leveraging external data sources where internal ones fall short. An AI-ready enterprise is a data-disciplined one. 

2. Legacy Infrastructure: Too Much Tech Debt, Not Enough Lift-Off

AI doesn’t play well with outdated systems. Many financial institutions still run on decades-old infrastructure that can’t support modern AI tools. The result? Innovation bottlenecks and frustrating pilot projects that never scale. 

The Fix: You don’t have to rip and replace everything. Modular modernization is key. APIs, microservices, and cloud migration enable AI adoption without shutting down operations. At Yameo, we help clients retrofit intelligence into existing systems while planning for the long game. 

3. Talent Shortage: Everyone Wants AI Experts—Nobody Can Find Them

The demand for AI-savvy professionals has outstripped supply. Insurers and financial firms compete with Big Tech for a limited talent pool. Meanwhile, internal teams often lack the skills to manage, interpret, and scale AI solutions effectively. 

The Fix: Don’t just hire—partner. Build external alliances with software development agencies who live and breathe AI. Upskill your teams through training programs, and create cross-functional squads that combine domain expertise with data science. Remember: tech without people is just potential, not performance. 

4. Regulatory & Ethical Uncertainty: The Compliance Conundrum

Insurance and finance are highly regulated industries. Throw AI into the mix and suddenly you’re dealing with algorithmic bias, explainability concerns, and constantly evolving compliance standards (hello, EU AI Act). Risk-aversion kicks in—and innovation stalls. 

The Fix: Bake ethics and compliance into your AI lifecycle. Use explainable AI models, keep human-in-the-loop oversight, and collaborate with legal teams from day one. Tools like model monitoring, audit trails, and decision transparency are not optional—they’re your license to operate. 

5. Cultural Resistance: When Fear Trumps Strategy

Let’s be real: AI threatens the status quo. Teams fear job losses, leaders hesitate on investment, and decision-makers dread being the ones who back a failed project. Cultural resistance is one of the most underestimated AI adoption challenges—and one of the deadliest. 

The Fix: Change the narrative. AI isn’t about replacing people—it’s about augmenting them. Show how automation frees up time for higher-value work. Share success stories, celebrate small wins, and create a culture of experimentation. The fastest-growing firms in finance and insurance don’t just use AI—they believe in it. 

Final Thoughts: The Winners Are Already Moving 

AI adoption in insurance and finance is no longer a nice-to-have. It’s a survival strategy. But the path to transformation isn’t a straight line—it’s a strategic maneuver through very real roadblocks. 

At Yameo, we help you navigate those AI adoption challenges with a mix of sharp technical skills, industry know-how, and a human-first approach to innovation. Whether you’re automating claims, detecting fraud, or just trying to make sense of your data, we’re here to help you build smartly—and fearlessly. 

Let’s talk. Your AI future starts now. 

If you thought 2024 was transformative for artificial intelligence, 2025 has been nothing short of revolutionary. At Yameo, we’ve been working with industry leaders since 2006, and I can tell you with certainty: we’ve never witnessed technological change at this velocity. The AI landscape has shifted so dramatically that the strategic plans many organizations drafted just 18 months ago are already obsolete.

Let me share what’s really happening in AI right now—and more importantly, what you need to prepare for in the first quarter of 2026.

The 2025 AI Revolution: By the Numbers

The statistics alone tell a compelling story. The global AI market reached $757.58 billion in 2025, and is projected to grow to $3.68 trillion by 2034—a nearly fivefold expansion in under a decade. But numbers only scratch the surface of the transformation we’re witnessing.

Around 78% of companies globally now use AI in at least one business function, with sharp acceleration in customer service, cybersecurity, and content creation. What’s fascinating isn’t just adoption rates, it’s how AI has evolved from a supportive tool to an autonomous decision-maker.

The Three Breakthrough Developments That Defined 2025

1. Agentic AI: From Tool to Teammate

The biggest shift? AI agents that don’t just respond to prompts but actually set goals and execute multi-step plans autonomously. Gartner predicts that 40% of enterprise applications will leverage task-specific AI agents by 2026, compared to less than 5% in 2025.

Think about what this means for software development—our core expertise at Yameo. AI agents are now handling everything from code generation to quality assurance, allowing our teams to focus on strategic architecture and innovation rather than repetitive tasks.

2. The Reasoning Revolution

Reasoning defined 2025, as frontier labs combined reinforcement learning with verifiable reasoning to create models that can plan, reflect, self-correct, and work over increasingly long time horizons. This isn’t just incremental improvement—it’s a fundamental shift in AI capabilities.

3. Multimodal AI Becomes Standard

Remember when AI could only process text? By 2025, multimodal AI models that understand text, image, audio, and video inputs simultaneously have become the new baseline. For our clients in sectors like healthcare and insurance, this means AI can now analyze medical imaging, patient records, and doctor’s notes in one integrated process.

The Regulatory Landscape: EU Sets the Global Standard

While innovation accelerated, so did regulation. The EU AI Act entered into force on August 1, 2024, with key provisions rolling out throughout 2025, including bans on unacceptable AI practices from February 2025 and obligations for general-purpose AI models from August 2025.

As a European software house with headquarters in Utrecht, we’ve been deeply involved in helping our clients navigate these new requirements. The Act classifies AI applications by risk level—unacceptable, high, limited, and minimal—with strict requirements for high-risk systems like those used in healthcare, finance, and critical infrastructure.

The implications? Any company deploying AI in Europe needs solid governance frameworks. This isn’t optional—it’s the cost of doing business in 2026.

Market Dynamics: The AI Competition Heats Up

In 2024, U.S.-based institutions produced 40 notable AI models, significantly outpacing China’s 15 and Europe’s three, though Chinese models have rapidly closed the quality gap. What we’re seeing is a genuine global race, with massive implications for businesses trying to maintain competitive advantage.

The talent war is equally fierce. U.S. postings for AI jobs rose 25.2% year-over-year, and the median annual salary for AI roles has climbed to $156,998. For organizations like ours, this means rethinking how we build and retain technical teams.

What Q1 2026 Will Bring: Five Critical Predictions

Based on current trajectories and industry signals, here’s what I see coming in the first quarter of 2026:

1. The AI Spending Reality Check

Enterprises will delay 25% of AI spend into 2027 as ROI concerns mount, with only 15% of AI decision-makers reporting an EBITDA lift in the past 12 months. The honeymoon phase is ending. Organizations will demand concrete business outcomes, not just technological novelty.

For software development partners like Yameo, this means our clients will increasingly ask: “How will this AI investment improve our bottom line?” It’s the right question.

2. Agentic AI Goes Mainstream in Enterprise

AI agents will handle customer service ticket triage, supply chain optimization, and automated portfolio management with minimal human intervention. The technology is ready. Q1 2026 is when enterprises start deploying at scale.

3. The Rise of Physical AI (With Caveats)

While AI leaders predict minimal to moderate usage of physical AI in the next two years due to safety requirements and substantial hardware costs, more than 50% of general respondents expected moderate to significant impact. Expect pilot programs and proof-of-concepts, not widespread deployment.

4. AI Governance Becomes a C-Suite Priority

With enforcement of the EU AI Act ramping up throughout 2026, expect every board meeting to include AI governance discussions. Companies will need dedicated resources—possibly even new C-suite positions—to manage AI risk and compliance.

5. The Talent Landscape Transforms

New hybrid roles like “knowledge engineers” will emerge, blending technical expertise with domain knowledge. Junior software engineering roles will continue to face pressure as AI handles more routine coding tasks, but professionals who can manage teams of AI agents will command premium salaries.

The Ethical Dimension: Navigating Responsibility

Here’s something that keeps me up at night: AI-related incidents are rising sharply, yet standardized responsible AI evaluations remain rare among major industrial model developers.

At Yameo, we’ve always believed that excellence and constant improvement are in our DNA. That philosophy extends to how we approach AI. It’s not enough to build software that works—we must build software that works responsibly, ethically, and in compliance with emerging regulations.

What This Means for Your Business

If you’re a decision-maker trying to navigate this landscape, here’s my advice based on nearly two decades of building software for industry leaders:

Start with strategy, not technology. The businesses winning with AI aren’t those deploying the most cutting-edge models—they’re those with clear use cases tied to business outcomes.

Invest in governance early. With Q1 2026 bringing increased regulatory scrutiny, robust AI governance frameworks are no longer optional. Companies without proper guardrails will face significant legal and reputational risks.

Focus on workforce transformation. AI won’t replace your talented people, but it will change what they do. Invest in reskilling and upskilling now, before you’re forced to by competitive pressure.

Choose partners carefully. The AI landscape is moving so fast that even many established tech companies can’t keep pace. You need partners who understand both the technology and your industry—partners who’ve proven they can deliver through multiple technology cycles.

Looking Ahead: The Opportunity of a Generation

Despite the challenges—and there are many—I’m genuinely excited about where we’re headed. AI could contribute up to $15.7 trillion to the global economy by 2030, more than the current output of China and India combined.

But capturing that opportunity requires preparation. It requires solid software solutions built on thoughtful strategy. It requires partners who understand that successful AI implementation isn’t about following hype—it’s about solving real business problems.

At Yameo, we’ve been privileged to work with organizations like DEKRA, Ergo Hestia, and AON—companies that trust us to navigate complex technological transitions while keeping their business objectives front and center. That’s exactly the approach AI demands in 2026.

The first quarter of 2026 will separate organizations that experimented with AI from those that successfully integrated it into their operations. Which category will you be in?

As we delve into 2025, the AI landscape stands at a pivotal juncture. The rapid advancements in AI technologies have ushered in transformative possibilities across industries. However, these developments are accompanied by a spectrum of challenges that software houses must adeptly navigate to harness AI’s full potential responsibly and effectively.

1. Escalating Infrastructure Demands

The proliferation of AI applications has led to an unprecedented surge in computational requirements. Training sophisticated models necessitates substantial energy consumption, prompting concerns about environmental sustainability. Notably, from 2019 to 2024, Google’s greenhouse gas emissions escalated by 50%, a trend largely attributed to the energy demands of AI operations.

Strategic Response: Software houses are compelled to innovate in energy-efficient algorithms and invest in sustainable infrastructure. Collaborations with green energy providers and optimizing code to reduce computational load are becoming industry imperatives.

2. Intensifying Market Competition

The AI landscape is witnessing fierce competition, with tech giants like Google, OpenAI, Microsoft, and emerging players such as China’s DeepSeek vying for market dominance. DeepSeek’s introduction of cost-effective, partially open-source models has disrupted traditional business models, leading to aggressive price wars and challenging the sustainability of high-cost AI solutions.

Strategic Response: To remain competitive, software houses must focus on innovation, offering unique value propositions, and exploring niche markets. Embracing open-source collaborations can also foster community-driven advancements and reduce development costs.

3. Ethical and Safety Considerations

The accelerated development of AI technologies has outpaced the establishment of comprehensive ethical guidelines and safety standards. Incidents involving AI-generated deepfakes and biases in AI decision-making systems have raised public and regulatory concerns.

Strategic Response: Implementing robust ethical frameworks and bias mitigation strategies is paramount. Software houses should prioritize transparency, conduct regular audits of AI systems, and engage with policymakers to shape regulations that balance innovation with societal welfare.

4. Talent Acquisition and Skill Gaps

The demand for AI expertise has surged, leading to a competitive talent landscape. Traditional software development roles are evolving, requiring proficiency in AI and machine learning. This shift has created skill gaps, particularly in integrating AI seamlessly into existing systems.

Strategic Response: Investing in continuous learning and development programs is essential. Software houses can cultivate talent by offering training in AI competencies, fostering a culture of innovation, and collaborating with academic institutions to bridge the skill gap.

5. Data Privacy and Security Challenges

AI systems thrive on data, but the collection and utilization of vast datasets have sparked concerns over privacy and security. High-profile incidents of data breaches and unauthorized use of personal information have eroded public trust.

Strategic Response: Adopting privacy-by-design principles ensures that data protection is integral to AI system development. Implementing robust encryption, anonymization techniques, and obtaining explicit user consent are critical steps in safeguarding data integrity.

6. Integration Complexities

Incorporating AI into existing software ecosystems presents technical challenges, including compatibility issues and the need for significant architectural overhauls. Ensuring that AI components align with legacy systems without disrupting operations requires meticulous planning.

Strategic Response: Employing modular design principles allows for scalable and flexible integration of AI functionalities. Engaging in thorough testing and adopting iterative development approaches can mitigate integration risks.

7. Regulatory Compliance

As governments worldwide grapple with the implications of AI, a wave of regulations is emerging to govern its development and deployment. Navigating this evolving regulatory landscape demands vigilance and adaptability from software houses.

Strategic Response: Proactive engagement with regulatory bodies and staying abreast of legislative developments are crucial. Incorporating compliance checks into the development lifecycle can preempt legal challenges and align AI products with regional and international standards.

Conclusion

The trajectory of AI in 2025 is marked by a confluence of opportunities and challenges. For software houses, the imperative lies in adopting a holistic approach that encompasses technological innovation, ethical responsibility, and strategic agility. By addressing these multifaceted challenges head-on, the software industry can harness the transformative power of AI to drive sustainable growth and societal advancement.

As we step into 2025, the tech landscape is evolving faster than ever and staying ahead of the curve is critical for businesses looking to remain competitive. At Yameo, we’ve been at the forefront of cutting-edge technology, helping our clients navigate complex challenges and leverage innovative custom software development. Based on our expertise and research, here are the top five tech trends to watch in 2025.

The Next Frontier of AI: Smarter and More Specialized

Artificial Intelligence continues to dominate the tech conversation, but 2025 will see a shift toward more specialized AI applications. Generative AI tools are becoming indispensable across industries, from content creation to advanced customer support. 

What’s new? Expect AI to integrate deeper into workflows with tools that: 

• Provide hyper-personalized customer experiences. 

• Automate repetitive tasks without compromising quality. 

• Enhance predictive analytics for smarter decision-making. 

At Yameo, we’ve witnessed firsthand how AI transforms operations. Whether you’re exploring AI-powered chatbots or predictive models, this is the year to invest. 

RCS Messaging: The Future of Customer Communication

Rich Communication Services (RCS) is taking messaging to a whole new level. Offering a more interactive and engaging alternative to SMS, RCS allows businesses to: 

• Share multimedia content. 

• Enable two-way customer communication. 

• Provide secure, branded messaging experiences. 

Why 2025? With increasing adoption across industries, RCS is set to replace traditional messaging methods. Imagine sending customers not just a notification but a dynamic, interactive message that includes videos, images, and even payment options. 

Yameo Insight: Companies in retail, banking, and healthcare are already leveraging RCS to improve customer engagement. The possibilities are endless. 

CTA: Discover RCS Messaging with Yameo [link to RCS landing page] 

Custom Software Development: A Must-Have for Business Agility 

Off-the-shelf solutions can only take you so far. In 2025, businesses will increasingly turn to custom software development to: 

• Meet unique operational needs. 

• Scale solutions alongside growth. 

• Gain a competitive edge in crowded markets. 

At Yameo, we specialize in crafting bespoke software solutions tailored to our clients’ challenges. Whether it’s an industry-specific application, workflow automation tool, or custom CRM, the key is adaptability and scalability.

Sustainability Through Tech Innovation

Sustainability is no longer optional. Companies are integrating technology to reduce waste, improve energy efficiency, and build more eco-friendly processes. From AI-powered energy management systems to blockchain for supply chain transparency, tech is enabling smarter sustainability strategies. 

Yameo’s perspective? Investing in sustainable tech isn’t just good for the planet—it’s good for business.

Cybersecurity: Protecting the Digital Frontier

With the rise of AI, RCS, and custom software comes the need for robust cybersecurity measures. Threats are becoming more sophisticated, and businesses must prioritize: 

• Proactive threat detection. 

• Securing sensitive customer data. 

• Ensuring compliance with evolving regulations. 

Yameo Tip: Partnering with experts to build secure systems from the ground up can save your business from costly breaches.

Ready to Lead in 2025?

The year ahead is full of opportunities for businesses ready to embrace innovation. Whether it’s leveraging AI, adopting RCS messaging, or investing in custom software, the future belongs to those who act now. 

Let’s shape the future together. 

Artificial Intelligence (AI) has become one of the hottest topics right now. Everyone is talking about it, how it can be used, and its potential impact on society. Of course, there are still uncertainties surrounding AI when it comes to ethics, regulations, and limitations. AI has already started to change the way we live and work, and its influence is probably going to grow in the coming years. One area where AI is already making an impact is in the field of software development and writing code. Here’s our take on that as a software agency with 17 years of experience.

AI in software development

Artificial Intelligence is a very powerful technology at the moment. It has already found numerous applications in industries such as healthcare, finance, and transportation. Even everyday tasks like writing emails or finding new recipes can be optimized with tools like ChatGPT. In software development, AI can most importantly help streamline and optimize the process of writing code. With the help of AI-powered tools, developers can automate repetitive tasks, such as writing boilerplate code or performing code reviews, saving valuable time, and increasing productivity. Furthermore, AI can provide insights into code quality, performance, and optimization, enabling developers to write smarter, more efficient code. However, does that mean that AI is essential to stay competitive in the market?

Potential risks of leveraging AI in writing code

Before we explore how exactly AI can be used in software development, we definitely should explore the potential risks of working with it. While AI can provide numerous benefits to software development, it is not without its risks and limitations. When it comes to the AI generated results, we can treat it similarly to Google Translate. Yes, the results are often very good and fast, but you always have to double-check and make sure it’s 100% right. The more complex task you have, the more careful you must be with trusting the AI results. However, it is also crucial to consider the potential risks of leveraging AI in terms of data privacy and intellectual property rights.

Examples of risks

First, let’s look at the topic of data privacy. As the generated code is based on analyzed data, it is essential to ensure that there are no potential leaks or breaches in the system. Additionally, there may be legal implications to using AI-generated code, and it is important to take these into account to avoid costly legal disputes in the future. Another potential risk is the introduction of bias into the code, as AI algorithms are only as unbiased as the data they are trained on. This can lead to unintentional discrimination or inequity in software products. What’s more, the quality of the code produced by AI-powered tools may not always meet the standards required for the project, leading to potential errors and inefficiencies. Ethical concerns related to AI in software development include issues transparency and accountability. There is a need to ensure that the use of AI in software development is transparent and explainable to avoid confusion or mistrust among users.

Leveraging AI to write code & other use cases

Now that we have addressed the potential risks that come along with leveraging AI in software development, let’s explore how exactly you can use it as a developer. We’ve already mentioned that it can help streamline and optimize the process of writing code. Let’s have a look at possible use cases.

AI Tools

Of course, one of the most popular tools you can use to generate code is ChatGPT. Created by OpenAI, the AI chatbot ChatGPT may immensely speed up your process of writing code. However, as mentioned before, you must be aware of its potential risks, as the generated code might include bugs, security vulnerabilities, etc. Another tool you can use is Copilot (and soon Copilot X.) It uses deep learning algorithms to analyze code patterns and suggest code snippets based on the context. Copilot works by integrating with the code editor and providing suggestions as developers write, enabling them to complete tasks quickly and with fewer errors. It can be used for writing functions, classes, and entire code blocks, making it a valuable asset for developers looking to streamline their workflow. It is important to note that new software solutions based on AI are constantly coming out, and may bring some powerful options for you and your use cases in the future.

Will AI replace developers?

No. While AI can automate many tasks, it is unlikely that it will replace developers entirely. Rather, AI can complement the work of developers and allow them to focus on more complex tasks that require human expertise, such as problem-solving and innovation. Therefore, it is crucial to balance the use of AI with human expertise in software development.

Developers won’t be replaced by AI, however, they may be replaced by other developers who know how to effectively utilize AI. 

Yameo’s take

Although we see how useful AI is for simple, repetitive tasks, we definitely won’t be relying on it entirely. The responsible use of Artificial Intelligence in software development requires a thorough understanding of its capabilities, limitations, and potential risks. While many tools are emerging that can help developers write code faster and more efficiently, there is still a need for human expertise to balance the limitations of AI. It is crucial to select and test AI-powered tools carefully and ensure that they meet the required standards. While we are excited about the potential of AI to improve our work, we recognize that it is not yet at a stage where we can trust it completely.

Customer sentiment analysis – what is it?

Simply put, customer sentiment analysis is a process by which you can determine how your customers really feel about your products & services by analysing their behaviour and language when they discuss your products on the internet. It is largely driven by automated artificial intelligence keyword processing or NLP algorithms based on machine-learning and big data analytics. But more on that later on. 

To give a simple example, you have customers who have reviewed your services on a review website, discussed it on Facebook with friends, searched on google for similar services, and been to your website (and that of a competitor) multiple times. Customer Sentiment Analysis (CSA) helps you to learn what your customer is really thinking of your service. It offers companies more insights into how to effectively manage and target their existing and new customers. 

So, what are some of the benefits of Customer Sentiment Analysis? 

Well, to start off there are some big benefits for your Marketing & Sales departments, namely: 

• You can build and structure your marketing strategies more accurately and hence maximise your ROI. 
• Taking into account real customer sentiments means you can better manage customer relationships, and segment customers into groups based on their opinions and feelings towards your company, and its services. 
• Better prepare your customer service employees who have real-time data on the opinions and sentiments of customers towards your brand, products and services. 
• Improve engagement outcomes with your customers 

The different forms of Customer Sentiment Analysis 

Manual Processing 

As the name says this is a manual process carried out by human beings – not so A.I. you may think. However, this is still a necessary process (for now) as A.I. is not always able to detect and determine aspects like sarcasm or ambiguous textual content. This is where human intervention is required.  

Keyword Processing 

Well-defined and A.I. enabled algorithms can lead to real-time Customer Sentiment Analysis, which enables your customer service agents to see in real-time what keywords are used by your customers. This information point helps to provide a complete overview for your employees when dealing with a customer. 

Natural Language Processing (NLP) 

Data mining techniques and analytics for both audio and text can eliminate the shortcomings of the previous two methods. NLP also accounts for the emotions being expressed by your customer and factors this into the final overview. 

What would such a situation look like for your company? 

1. Your customer calls your call center 

2. A.I. analyses the call in real-time: An AI bot is quietly conferenced into the call to analyze the caller’s sentiments and emotions. 

3. Business rules trigger actions: Your business rules can prompt the bot to alert a supervisor if the bot’s real-time analysis of the caller’s sentiments indicates a possible customer satisfaction issue. 

4. Experts provide real-time support: The supervisor can coach the agent in real-time via a whisper or text chat, and together they can provide the customer with the best possible service. 

5. The bot broadens its understanding: The AI bot gets better at decoding sentiment and emotions as it monitors the effect of the supervisor’s suggestions. With ever-improving insight into caller sentiments and emotions, the AI gets even better at deciding when to alert a supervisor. 

So, what are the challenges? 

Although this form of A.I. is not commonly adopted yet, it is already making big strides. And with every new innovation, there are some challenges that have been identified. 

Fake opinions 

Sometimes users express fake opinions which distorts the value of the information that CSA generates.  

Limits of Manual Processing 

Using human beings for processing is time and resource-consuming, which does not always have a positive effect on the cost-benefit analysis of CSA. 

Language barriers 

With over 7,000 languages worldwide, CSA is currently most effective in the big common languages like English, Spanish, Mandarin, and French. This means the technology is not yet viable for every language. 

Poor strategy planning 

Effective implementation is often a factor why CSA does not meet its full potential. The increase in data, especially sentiment data from social media, means a huge influx of data at a fast pace which not every organisation is ready to handle. 

Is Customer Sentiment Analysis viable for your business? 

Yes, it is. Every business wants to be better prepared to engage with their customers and truly know what they are thinking to best anticipate and manage outcomes. Yameo is a tech partner to Vonage, one of the leading providers of Customer Sentiment Analysis solutions. Using APIs we can help you get started, or if you are not yet ready, we have a dedicated team ready to brainstorm with you and define your idea. Check out our Discovery Workshops on how our experts can support you.

First, let us quickly summarize ‘what is Artificial Intelligence?’. Simply put, it is computer systems that use algorithm-based learning to perform tasks that would otherwise require human intelligence. And its impact on software development is going to be huge! What is even more important, it is not going to happen 10 years from now but is expected in the near future. Here is a look at 5 ways Artificial Intelligence in software development will impact the future.

1. Speed and Scale of Development 

More often than not, the goal of technology is to save time. Time-based performance indicators along the software development process, such as speed, scale, and testing, greatly improve with the implementation of AI machine-based learning. 

For example, before artificial intelligence programmers, software developers had to hire quality assurance analysts to manually test the functionality of their new software. This type of manual testing took more time and required more resources, often involving a chance of error. Since artificial intelligence can conduct all these tests automatically and more thoroughly the entire software development process can be shortened, while also producing more accurate results. 

2. Error Management 

The main reason for downtime in the software development process is error management. This is especially true if you run a software-as-a-service (SaaS) or other cloud-based software platforms. And when an error occurs in software, the amount of downtime it takes to correct that error costs money. 

The process of manually correcting software errors is often time-consuming. However, with an A.I.-powered programming assistant, previous software can be automatically analysed and errors are identified and overcome. By identifying possible errors early in the development process, A.I. is able to provide optimal cost-effective error management. 

3. Strategic Decision-Making 

Another feature of A.I. is its ability to make strategic decisions. A.I. can be programmed to analyse the success and failures of previous software to help with assessing new software, hence reducing risk through better analytics. These analytics allow developers to make smarter and more strategic decisions.  

As this technology continues to grow, computer power and data storage will increase exponentially, enabling further human intelligence to be supplemented with machine precision.  

4. Precision 

Not meeting your deadline is one of the worst outcomes for a software project. In the world of software development, customers expect timelines to be met and cost accurately estimated. 

Recently, developers can train A.I. to generate accurate forecasts for them. Inputting data from past projects, an A.I.-powered machine can determine accurately the time, effort, and budget required to develop each new software project. 

5. Real-Time Feedback 

Most video conferencing solutions implement A.I.-enabled tools to gather real-time feedback from their users. Often this feedback is analyzed to improve customer experience. It can also provide developers with real-time statistics regarding which elements need improvement. By training machine learning algorithms to analyze how each user interacts with a platform, software developers can use A.I. to create the most effective software experiences for their customers.

I’m pretty confident everyone has heard of Artificial Intelligence at this point. Human intelligence and discernment are used by computers to carry out tasks and activities where normally you need a human. In customer-facing processes, we are probably most familiar with A.I. in the form of chatbots – either on a website, in a mobile app, or in other front-end solutions. Yet there are already hundreds of examples of solutions using A.I. – you’ve probably already used an A.I.-based solution today. Let’s look at a few Artificial Intelligence in customer-facing processes you may know.

• Google Maps: Analyses the speed of movement of traffic. It also takes in user-reported incidents, such as traffic accidents or road construction, to predict how long it will take you to reach your destination and uses that information to suggest the fastest route.
• Your Roomba: uses A.I. to scan the size of the room it is in, identify obstacles, and determine the most efficient route to sweep the space.
• Facebook, Snapchat: have all utilized A.I. in their facial recognition technology. Snapchat uses A.I. to recognize users’ faces and apply face filters to users’ photos. On Facebook, facial recognition is used to identify faces in photos and invite users to tag themselves or their Facebook friends.

So with A.I. becoming so readily used in our everyday customer-facing processes – how about for Insurance, Financial Services and Healthcare? These are the 3 big industries focused on customer services.

Insurance

According to a McKinsey article from March of last year, there are 4 big trends to watch for in insurance:

1. A huge growth of data from connected devices

With consumer devices becoming more technologically advanced they expect more data to be used from everyday items like watches, your car, smartphones, home assistants, fitness trackers, and door cameras. This trend will continue as currently, other less technologically advanced items like clothing, eyewear, and home appliances push to close the gap. The expected result will be a huge amount of data helping insurers satisfy their customers with new products, personalised pricing, and better real-time service delivery. What do you think?

2. Increased prevalence of physical robotics

By 2030, a much larger proportion of standard vehicles will have autonomous features, such as self-driving capabilities. Carriers will need to understand how the increasing presence of robotics in everyday life and across industries will shift risk pools, change customer expectations, and enable new products and channels.

3. Open-source & data ecosystems

As data becomes omnipresent, open-source protocols will emerge to ensure data can be shared and used across industries. Various public and private entities will come together to create ecosystems in order to share data for multiple use cases under a common regulatory and cybersecurity framework. For example, wearable data could be ported directly to insurance carriers, and connected-home and auto data could be made available through Amazon, Apple, Google, and a variety of consumer device manufacturers.

4. Advances in cognitive tech

Cognitive technologies, which are based on the human brain’s ability to learn through decomposition and inference, will become the standard approach for processing the large and complex data streams that will be generated by insurance products tied to an individual’s behavior and activities. With the increased commercialisation of these types of technologies, carriers will have access to models that are constantly learning and adapting to the world around them—enabling new product categories and engagement techniques while responding to shifts in underlying risks or behaviors in real time.

Financial Services

In a World of Finance article from February of this year, the main focus for A.I. in this industry is on better service offerings and minimising risks such as cyber security and fraud.

Better service offering 

Although financial services are predicted to always need a real human presence, A.I. is forecasted to play a pivotal role in the systems that customers interact with. Think along the lines of A.I. understanding consumer behaviours and recommending on-point and relevant services. Financial service organisations already use AI to detect fraud, predict cash-flow events, create invoices, fine-tune credit scores, conduct cost and benefit analysis, as well as for account creation and goal setup. And the forecast is that this will continue and expand.

Minimising risks – cyber security & fraud 

Where A.I. can help understand consumer behaviours and offer better service as a result, this can also help with fraud detection. A.I. is expected to become more aware of fraudulent practices and identify them much quicker and more reliably than a human can. Elements such as cyber security can be greatly improved as A.I. is able to quickly identify loopholes and weak points in banking systems.  

Healthcare

One of the biggest challenges facing healthcare is supply-demand. There is a huge demand for doctors and medical staff worldwide whilst the supply of newly trained professionals is not keeping up. A recent Economist article researched how A.I. will shape the healthcare industry moving forward. 

Currently, experts are saying that A.I. in healthcare is in the 1.0 phase – meaning A.I. helps with automating mundane tasks. A.I. 2.0 is where the industry is heading when large-scale data can be analysed and combined with medical knowledge to create new products and services which help with the care, prevention, and curing of diseases. The perspective is that A.I. will become a game-changer in healthcare and by 2027 the A.I. market will have grown by 8x since 2020. Healthcare providers will not be able to function by 2040 without some form of Artificial Intelligence in customer-facing processes.

So with A.I. becoming a more readily used technology, when should you start with A.I.?

The answer to be honest in my opinion is now. Where previously trends like ‘digitalisation’ have taken decades to kick-off and become ‘mainstream’, this is not the case with A.I.. Artificial Intelligence is already a common technology in many everyday services and products we use. You may already have A.I. elements on your omnichannel in a chatbot or voicebot. We have also seen a rise in A.I. requirements in the projects we do for customers, from picture analysis and automatic damage assessment for insurance to visual identification of customers for our kiosk banking platform. The key here is to know where you can effectively use A.I. to have immediate gains for both you and your customers. Considering a Zendesk study where it is identified that 50% of EMEA customers said they would switch to a competitor after a poor customer service experience it is becoming vital that organisations adapt to become customer-centric in their use of new technologies like Artificial Intelligence. 

Where to start with A.I.? 

I’d like to say the first place to start is with Yameo! We have dedicated teams ready to brainstorm, learn your processes and identify where Artificial Intelligence in customer-facing processes can be of value. However, the first place would be with the persons responsible for innovation, strategy, customer service and operational success. After I would say a good step is to brainstorm with a partner who understands healthcare, insurance, or financial service customer processes.