AI tools have become a game-changer for the technology industry, enhancing developer productivity and software quality. Leveraging advanced machine learning models and natural language processing, these tools offer a wide range of capabilities, from code completion to generating entire blocks of code, significantly reducing the cognitive load on developers. AI-powered tools not only accelerate the coding process but also ensure higher code quality and consistency, aligning seamlessly with modern development practices. Organisations are reaping the benefits of these tools, which have transformed the software development lifecycle.
Impact on Developer Productivity
AI tools are becoming an indispensable part of software development owing to their:
- Speed and Efficiency. AI-powered tools provide real-time code suggestions, which dramatically reduces the time developers spend writing boilerplate code and debugging. For example, Tabnine can suggest complete blocks of code based on the comments or a partial code snippet, which accelerates the development process.
- Quality and Accuracy. By analysing vast datasets of code, AI tools can offer not only syntactically correct but also contextually appropriate code suggestions. This capability reduces bugs and improves the overall quality of the software.
- Learning and Collaboration. AI tools also serve as learning aids for developers by exposing them to new or better coding practices and patterns. Novice developers, in particular, can benefit from real-time feedback and examples, accelerating their professional growth. These tools can also help maintain consistency in coding standards across teams, fostering better collaboration.
Advantages of Using AI Tools in Development
- Reduced Time to Market. Faster coding and debugging directly contribute to shorter development cycles, enabling organisations to launch products faster. This reduction in time to market is crucial in today’s competitive business environment where speed often translates to a significant market advantage.
- Cost Efficiency. While there is an upfront cost in integrating these AI tools, the overall return on investment (ROI) is enhanced through the reduced need for extensive manual code reviews, decreased dependency on large development teams, and lower maintenance costs due to improved code quality.
- Scalability and Adaptability. AI tools learn and adapt over time, becoming more efficient and aligned with specific team or project needs. This adaptability ensures that the tools remain effective as the complexity of projects increases or as new technologies emerge.
Deployment Models
The choice between SaaS and on-premises deployment models involves a trade-off between control, cost, and flexibility. Organisations need to consider their specific requirements, including the level of control desired over the infrastructure, sensitivity of the data, compliance needs, and available IT resources. A thorough assessment will guide the decision, ensuring that the deployment model chosen aligns with the organisation’s operational objectives and strategic goals.
Technology teams must consider challenges such as the reliability of generated code, the potential for generating biased or insecure code, and the dependency on external APIs or services. Proper oversight, regular evaluations, and a balanced integration of AI tools with human oversight are recommended to mitigate these risks.
A Roadmap for AI Integration
The strategic integration of AI tools in software development offers a significant opportunity for companies to achieve a competitive edge. By starting with pilot projects, organisations can assess the impact and utility of AI within specific teams. Encouraging continuous training in AI advancements empowers developers to leverage these tools effectively. Regular audits ensure that AI-generated code adheres to security standards and company policies, while feedback mechanisms facilitate the refinement of tool usage and address any emerging issues.
Technology teams have the opportunity to not only boost operational efficiency but also cultivate a culture of innovation and continuous improvement in their software development practices. As AI technology matures, even more sophisticated tools are expected to emerge, further propelling developer capabilities and software development to new heights.
The AI landscape is undergoing a significant transformation, moving from traditional predictive AI use cases towards Generative AI (GenAI). Currently, most GenAI use cases promise an improvement in employee productivity, without focusing on how to leverage this into new or additional revenue generating streams. This raises concerns about the long-term return on investment (ROI) if this is not adequately addressed.
The Rise of Generative AI Over Predictive AI
Traditionally, predictive AI has been integral to business strategies, leveraging data to forecast future outcomes with remarkable accuracy. Industries across the board have used predictive models for a range of applications, from demand forecasting in retail to fraud detection in finance. However, the tide is changing with the emergence of GenAI technologies. GenAI, capable of creating content, designing products, and even coding, holds the promise to revolutionise how businesses operate, innovate, and compete.
The appeal of GenAI lies in its versatility and creativity, offering solutions that go beyond the capabilities of predictive models. For example, in the area of content creation, GenAI can produce written content, images, and videos at scale, potentially transforming marketing, entertainment, and education sectors. However, the current enthusiasm for GenAI’s productivity enhancements overshadows a critical aspect of technology adoption: monetisation.
The Productivity Paradox
While the emphasis on productivity improvements through GenAI applications is undoubtedly beneficial, there is a notable gap in exploring use cases that directly contribute to creating new revenue streams. This productivity paradox – prioritising operational efficiency and cost reduction – may not guarantee the sustained growth and ROI necessary from AI investments.
True innovation in AI should not only aim at making existing processes more efficient but also at uncovering opportunities for monetisation. This involves leveraging GenAI to develop new products, services, or business models to access untapped markets or enhance customer value in ways that directly impact the bottom line.
The Imperative for Strategic Reorientation
Ignoring the monetisation aspect of GenAI applications poses a significant risk to the anticipated ROI from AI investments. As businesses allocate resources to AI adoption and integration, it’s also important to consider how these technologies can generate revenue, not just save costs. Without a clear path to monetisation, the investments in AI, particularly in the cutting-edge domain of GenAI, may not prove viable in the next financial year and beyond.
To mitigate this risk, companies need to adopt a dual approach. First, they must continue to explore and exploit the productivity gains offered by GenAI, which are crucial for maintaining a competitive edge and achieving operational excellence. At the same time, businesses must strategically explore and invest in GenAI-driven opportunities for monetisation. This could mean innovating in product design, personalised customer experiences, or entirely new business models that were previously unfeasible.
Conclusion
The excitement around GenAI’s potential to transform industries is well-founded, but it must be tempered with strategic planning to ensure long-term viability and ROI. Businesses that recognise and act on the opportunity to not only improve productivity but also to monetise GenAI innovations will lead the next wave of growth in their respective sectors. The challenge lies in balancing the drive for efficiency with the pursuit of new revenue streams, ensuring that investments in AI deliver sustainable returns. As the AI landscape evolves, the ability to innovate in monetisation as much as in technology will distinguish the leaders from the followers.
The challenge of AI is that it is hard to build a business case when the outcomes are inherently uncertain. Unlike a traditional process improvement procedure, there are few guarantees that AI will solve the problem it is meant to solve. Organisations that have been experimenting with AI for some time are aware of this, and have begun to formalise their Proof of Concept (PoC) process to make it easily repeatable by anyone in the organisation who has a use case for AI. PoCs can validate assumptions, demonstrate the feasibility of an idea, and rally stakeholders behind the project.
PoCs are particularly useful at a time when AI is experiencing both heightened visibility and increased scrutiny. Boards, senior management, risk, legal and cybersecurity professionals are all scrutinising AI initiatives more closely to ensure they do not put the organisation at risk of breaking laws and regulations or damaging customer or supplier relationships.
13 Steps to Building an AI PoC
Despite seeming to be lightweight and easy to implement, a good PoC is actually methodologically sound and consistent in its approach. To implement a PoC for AI initiatives, organisations need to:
- Clearly define the problem. Businesses need to understand and clearly articulate the problem they want AI to solve. Is it about improving customer service, automating manual processes, enhancing product recommendations, or predicting machinery failure?
- Set clear objectives. What will success look like for the PoC? Is it about demonstrating technical feasibility, showing business value, or both? Set tangible metrics to evaluate the success of the PoC.
- Limit the scope. PoCs should be time-bound and narrow in scope. Instead of trying to tackle a broad problem, focus on a specific use case or a subset of data.
- Choose the right data. AI is heavily dependent on data. For a PoC, select a representative dataset that’s large enough to provide meaningful results but manageable within the constraints of the PoC.
- Build a multidisciplinary team. Involve team members from IT, data science, business units, and other relevant stakeholders. Their combined perspectives will ensure both technical and business feasibility.
- Prioritise speed over perfection. Use available tools and platforms to expedite the development process. It’s more important to quickly test assumptions than to build a highly polished solution.
- Document assumptions and limitations. Clearly state any assumptions made during the PoC, as well as known limitations. This helps set expectations and can guide future work.
- Present results clearly. Once the PoC is complete, create a clear and concise report or presentation that showcases the results, methodologies, and potential implications for the business.
- Get feedback. Allow stakeholders to provide feedback on the PoC. This includes end-users, technical teams, and business leaders. Their insights will help refine the approach and guide future iterations.
- Plan for the next steps. What actions need to follow a successful PoC demonstration? This might involve a pilot project with a larger scope, integrating the AI solution into existing systems, or scaling the solution across the organisation.
- Assess costs and ROI. Evaluate the costs associated with scaling the solution and compare it with the anticipated ROI. This will be crucial for securing budget and support for further expansion.
- Continually learn and iterate. AI is an evolving field. Use the PoC as a learning experience and be prepared to continually iterate on your solutions as technologies and business needs evolve.
- Consider ethical and social implications. Ensure that the AI initiative respects privacy, reduces bias, and upholds the ethical standards of the organisation. This is critical for building trust and ensuring long-term success.
Customising AI for Your Business
The primary purpose of a PoC is to validate an idea quickly and with minimal risk. It should provide a clear path for decision-makers to either proceed with a more comprehensive implementation or to pivot and explore alternative solutions. It is important for the legal, risk and cybersecurity teams to be aware of the outcomes and support further implementation.
AI initiatives will inevitably drive significant productivity and customer experience improvements – but not every solution will be right for the business. At Ecosystm, we have come across organisations that have employed conversational AI in their contact centres to achieve entirely distinct results – so the AI experience of peers and competitors may not be relevant. A consistent PoC process that trains business and technology teams across the organisation and encourages experimentation at every possible opportunity, would be far more useful.
You know AI is the absolute next biggest thing. You know it is going to change our world!! It is the little technology trick start-ups use to disrupt industries. It enables crazy applications we have never thought of before! A few days ago, we were dazzled to learn of an AI app that promises to give one a credit rating score based on reading your face – essentially from just a photograph it can tell a prospective financier what the likelihood of your paying back the loan is!
Artificial Intelligence is real and has started becoming mainstream – chatbots using AI to answer queries are everywhere. AI is being used in stock trades, contact centre applications, bank loans processing, crop harvests, self-driving vehicles, and streaming entertainment. It is now part of boardroom discussions and strategic initiatives of CEOs. McKinsey predicts AI will add USD 13 trillion to the global economy by 2030.
Hype vs Reality
So much to like – but why then do we often find leaders shrugging their shoulders? Despite all the good news above there is also another side to AI. For all the green indicators, there are also some red flags (Figure 1). In fact, if one googles “Hype vs reality” the majority of the results returned are to do with AI!!!!
Our experience shows that broad swaths of executives are skeptical of AI. Leaders in a variety of businesses from large multinational banks, consumer packaged goods companies to appliance makers have privately expressed their disappointment at not being able to make AI work for them. They cannot bridge the gap between the AI hype and reality in their businesses.
The data available also bears this out – VentureBeat estimates that 87% of ML projects never make it into production. Ecosystm research suggests that only 7% of organisations have an AI centre of excellence (CoE) – while the remaining depend on ad-hoc implementations. There are several challenges that organisations face in procuring and implementing a successful AI solution – both technology and business (Figure 2).
Visible Patterns Emerge from Successful AI Use Cases
This brings us to an interesting dichotomy – the reality of failed implementations versus the hype surrounding AI. Digital native companies or early adopters of AI form most of the success stories. Traditional companies find it tougher to embark on a successful AI journey. There have been studies that show a staggering gap in the ROI of AI projects between early adopters versus others, and the gulf between the high performers and the rest when using AI.
If we look back to figure 2 and analyse the challenges, we will see certain common themes – many of which are now commonplace wisdom, if not trite. Leadership alignment around AI strategy is the most common one. Getting clean data, aligning strategy with execution, and building the capabilities to use AI are all touted as critical requirements for successful execution. These themes all point to the insight that it is the human element that is more critical – not the technology.
As practitioners we have come across numerous examples of AI projects which go off-track because of human issues. Let’s take the example of an organisation that had enhancing call centre capabilities and capacity using RPA tools, as a key business mandate. There was strong leadership support and enthusiasm. It was clear that a large number of basic level tickets raised by the centre could be resolved using digital agents. This would result in substantial gains in customer experience, through faster ticket resolution and higher employee productivity – it was estimated to be above 30%. However, after two months of launching the pilot only a very small percentage of cases were identified for migration to digital agents.
Very soon, it became clear that these tools were being perceived as a replacement for human skills, rather than to augment their capabilities. The most vocal proponent of the initiative – the head of the customer experience team – became its critic, as he felt that the small savings were not worth the risk of higher agent turnover rates due to perceived job insecurity.
This was turned around by a three-day workshop focused on demonstrating how the job responsibility of agents could be enhanced as portions of their job got automated. The processes were redesigned to isolate parts which could be fully automated and to club non-automated components together driving more responsibility and discretion for agents. Once enhanced responsibility of the call centre staff was identified, managers felt more comfortable and were willing to support the initiative. In the end, the goals set at the start of the project were all met.
In my next blog I will share with you what we consider the winning formula for a successful AI deployment. In the meantime, share with us your AI stories – both of your challenges and successes.
Written with contributions from Ravi Pattamatta and Ratnesh Prasad
Organisations across all industries must leverage technology to transform and be part of the digital economy. This has significantly accelerated in the last couple of months as they are forced to transform to survive in these difficult times. Digital Transformation (DX) is no longer a hype, but organisations will continue to struggle to align their transformation priorities to deliver real business impact. From experience, the needs of the Sales & Marketing teams are often lost in the midst of competing for organisational priorities with process optimisation emerging as the first choice.
Why is it necessary for Sales & Marketing to transform?
Organisations’ Business Priorities
The primary reason is that organisations are responding to market pressure and putting customer experience (CX) ahead of even revenue growth as their key business priority (Figure 1).
It stands to reason that if organisations have the improvement of CX and revenue growth as their top priorities, they should involve functions that have higher customer interactions in their DX initiatives. Unfortunately, this is not often the case, even though organisations are inherently aware that their customer base is evolving. Sales & Marketing is all about meeting customer needs, solving their problems and giving them a great experience. In today’s competitive world, an organisation will fail without a customer focus.
The Evolving Consumer
Customers are challenging organisations to change the way they deliver a great experience. Take the growth of smartphone use as an example. Almost any activity will have some element of the smartphone being involved – researching a product, using an app, looking at information posted on a social media platform, engaging with friends to get opinions. This is driving organisations to respond with a ‘Mobile First’ policy and a digital nimbleness which they must equip their Sales & Marketing teams to handle.
Social and digital platforms are also forcing marketers to evolve the way they grab their customers’ attention. Customer service has tremendously benefited from the drive to go digital. It can impact every Sales & Marketing operation, beginning from first consumer touchpoint, all the way through the customer journey, post-purchase engagement and even in predicting issues to prevent them. This requires a huge degree of automation in the Sales & Marketing processes.
Emerging Ecosystems
As the digital world becomes a reality for consumers, the traditional supplier-vendor-channel model might soon become redundant. This is the age of ‘influencers’ – bloggers, customers, vendors, and paid endorsements – and an ever-evolving ecosystem. Engagement with the digital community can be a game-changer for many organisations. Such strong digital community ecosystems are hard to include in the traditional marketing model.
What should Sales & Marketing do to transform successfully?
Ecosystm Principal Advisor, Niloy Mukherjee, has some real-world advice for organisations that are looking to transform their Sales & Marketing practices.
Align with your Basic Strategy
Evaluate your key business goals and work out the changes that are required to achieve those. These changes might be so small and incremental that it may not even appear to be a ‘transformation’. Keeping an eye on the goals, will ensure that you do not invest in areas that do not necessarily need changing. It will also ensure that you simply do not replace an existing process with a new one, without first working out how that change will impact your organisation.
Think beyond features – to the Benefits
Technology investments often end up being the shiny new toy. Decision-makers in organisations may get attracted to snazzy devices and application features – and lose sight of evaluating the true benefits of the technology. For instance, the sales rep selling in-store would have a very good idea of what sells and how much stock to carry. There may be no incremental benefit in equipping the rep with an app that provides real-time sales analytics and inventory data. So, the app gets relegated to being just a feature with no real benefits. On the other hand, a field sales rep might find it extremely useful, especially in sectors that are prone to unpredictable spikes in demand.
Be ready to Invest in the change
You have evaluated the changes that your organisation needs, you have identified the technologies that can truly benefit your organisation – you must be ready to invest in that change. This is not only about financial investments – you have to invest time and in people. This requires your organisation to think of the RoI, again not only in terms of finances but also in terms of effort. Be aware that your biggest challenge in implementing the required change might be people – so invest in making them less resistant and more welcoming of the change.
There will be distractions galore in your transformation journey – emerging tech areas, solutions that seem to be working for your competitors and so on. Mukherjee proposes a simple thumb rule, “If it fits the strategy and looks feasible go with it; if it is outside the agreed strategy then think long and hard, and then turn it down – or change the strategy!”
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CEOs have an active role to play in an organisation’s transformation needs and journey – including in the technology decisions. Last month we spoke about why CEOs should get involved in their organisation’s IoT investments. Now, we look at Cloud – which has been a part of the CIO’s purview so far. Under the current circumstances, most organisations are actively pushing to go digital and the internal discussions will often revolve around empowering remote employees and digital customers. All the technologies that are being evaluated by organisations today have Cloud as their pillar. Increasingly, we see organisations adopt the hybrid and the multi-cloud. And organisations may not have the capabilities – technological and skills – to support the complexity of their Cloud environment. While a CEO does not have to understand the technology fully, it is important to understand the business impact of the technology.
Why should a CEO get involved in and have visibility into an organisation’s Cloud investments? There are a few important reasons.
#1 Cloud is not a cost-saving measure – it will enable you to transform
Organisations have matured in their Cloud adoption and no longer evaluate the benefits of Cloud only in terms of shifting CapEx to OpEx. If we look at the benefits of Cloud adoption, reduction of IT costs is not even in the top 3 benefits that organisations are seeking from Cloud anymore. Operational efficiency and collaboration emerge as key benefits (Figure 1) – while some companies still move to the Cloud for the savings, they stay there for other benefits.
This requires organisations to think of Cloud as a technology empowering their infrastructure and services. Cloud acts as an enabler for ease of doing business, real-time data access for productivity increase, and process automation. This impacts the entire organisation. It also involves prioritising the needs of certain functions over others – definitely not what a CIO should have to do.
If we look at just Cloud storage as an example, organisations can no longer have individual functions and their associated shadow IT teams having their own Cloud storage (and collaboration). This often turns out to be more expensive and there is a lack of consolidated view and management. While organisations forge ahead with the dream of having real-time information sharing across functions, a CIO has to consider the entire organisation’s technological and business needs – a CEO is the best person to guide the CIO in translating the organisation’s vision into IT priorities.
#2 In fact Cloud adoption may not cut costs at all!
Organisations are also re-evaluating the cost benefits of Cloud. Investing in a Cloud infrastructure with a short-term view on the investments involved has led to instances of Cloud solutions being brought back in-house because of rising costs. While security, data privacy and integration remain the key challenges of Cloud adoption (Figure 2), over a third of the organisations find Cloud more expensive than traditional licensing or owning the hardware.
Organisations find that the cost considerations do not stop after the adoption or migration. As businesses use Cloud to scale, there are several aspects that require constant re-evaluation and often further investments – cybersecurity measures, continuous data protection (CDP), disaster recovery management, rightsizing capacity, software and database licenses and day-to-day maintenance, to name a few. In addition to this, the cost of finding and recruiting a team of professionals to manage and maintain the Cloud environment also adds up to the OpEx.
If the CIO is talking about a Cloud migration for cost benefits only, the CEO and the CFO need to step in to evaluate that all factors have been taken into consideration. Moreover, the CIO may not have full visibility of how and where the organisation is looking to scale up or down. It is the CEO’s responsibility to share that vision with the CIO to guide Cloud investments.
#3 Cloud will increasingly be part of all tech adoption considerations
In this disruptive world, CEOs should explore possibilities and understand the technical capabilities which can give organisations an edge over their competitors. It is then up to the CIOs to implement that vision with this larger context in mind. As organisations look to leverage emerging technologies, organisations will adopt Cloud to optimise their resources and workloads.
AI is changing the way organisations need to store, process and analyse the data to derive useful insights and decision-making practices. This is pushing the adoption of Cloud, even in the most conservative organisations. Cloud is no longer only required for infrastructure and back-up – but actually improving business processes, by enabling real-time data and systems access. Similarly, IoT devices will grow exponentially. Today, data is already going into the Cloud and data centres on a real-time basis from sensors and automated devices. However, as these devices become bi-directional, decisions will need to be made in real-time as well. Edge Computing will be essential in this intelligent and automated world. Cloud platform vendors are building on their edge solutions and tech buyers are increasingly getting interested in the Edge allowing better decision-making through machine learning and AI.
In view of the recent global crisis, we will see a sharp uptake of Cloud solutions across tech areas. IaaS will remain the key area of focus in the near future, especially Desktop-as-as-Service. Organisations will also look to evaluate more SaaS solutions, in order to empower a mobile and remote workforce. This will allow the workforce of the future to stay connected, informed and make more decisions. More than ever, CEOs have to drive business growth with innovative products and services – not understanding the capabilities and challenges of Cloud adoption and the advancements in the technology can be a serious handicap for CEOs.
#4 Your IT Team may be more complacent about Cloud security than you think
Another domain that requires the CEO’s attention is cybersecurity. The Cloud is used for computing operations and to store data including, intellectual property rights, financial information, employee details and other sensitive data. Cybersecurity breaches have immense financial and reputational implications and IT Teams cannot solely be responsible for it. Cybersecurity has become a Board-level conversation and many organisations are employing a Chief Information Security Officer (CISO) who reports directly into the CEO. Cybersecurity is an aspect of an organisation’s risk management program.
Evaluating the security features of the Cloud offerings, therefore, becomes an important aspect of an IT decision-maker’s job. While security remains a key concern when it comes to Cloud adoption, Cloud is often regarded as a more secure option than on-premise. Cloud providers have dedicated security focus, constantly upgrade their security capabilities in response to newer threats and evolve their partner ecosystem. There is also better traceability with the Cloud as every virtual activity can be tracked, monitored, and logged. Ecosystm research finds that more than 40% of IT decision-makers think the Public Cloud has enough security measures and does not need complementing (Figure 3).
However, the Cloud is as secure as an organisation makes it. The perception that there is no need to supplement Public Cloud security features can have disastrous outcomes. It is important to supplement the Cloud provider’s security with event-driven security measures within an organisation’s applications and cloud interface.
It is the job of the CEO – through the CISO – to evaluate how cyber ready the IT Team really is. Do they know enough about shared responsibility? Do they have full cognizance of the SLAs of their Cloud providers? Do they have sufficient internal cybersecurity skills? Do they understand that data breaches can have cost and reputational impacts? As cybersecurity breaches begin to have more financial implications than ever and can derail an organisation, a CEO should have visibility of the risks of the organisation’s Cloud adoption.
Cloud is no longer just a technological decision – it is a business decision and takes into account the organisation’s vision. A full visibility of the Cloud roadmap – including the pitfalls, the risks and the immense potential – will empower a CEO immensely.
