One of the main questions that I have faced over the past week, since I wrote the Ecosystm Insight – Welcome to the Great Bounce Forward – is “How is this different to the “New Normal”? Many have commented that the concept of the Great Bounce Forward is more descriptive and more positive than the term “New Normal” – but I believe they are different, and require different strategies and mindsets.
This is a brief summary of some of the major differences between the New Normal and the Great Bounce Forward. I look forward with excitement and some trepidation towards this future. One where business success will be dictated not only by our customer obsession, but also the ability of our business to pivot, shift, change and adapt.
I can’t tell you what will happen in the future – a green revolution? Another pandemic? A major war? A global recession? Market hypergrowth? All the people living life in peace? Imagine that…
What I can tell you is what your organisation needs to do to be able to meet all of these challenges head-on and set yourself up for success. And to me, that won’t look like the new normal. There is nothing normal about these business capabilities at all.
As technology vendors, their channel partners and organisations deal with the tech talent crunch, there are consequences for the entire technology ecosystem. Vendor solutions are less impactful than their potential; their channel partners are losing a lot of time in closing sales cycles and their customers are left with sub-optimal deployments.
Here is how vendors and their channel partners can bridge the gap:
Re-evaluating Certification Programs
The first step is to move to a case-based training model with the participant needing to respond to a situation rather than answering multiple-choice questions. We do not discount the usefulness of standard testing, but it needs to be mixed with evaluating the situational awareness and subjective understanding of the participant. This will require more detailed answers and case studies and will require a knowledgeable evaluator at the other end who will need more time than that required to assess MCQ answers. While AI tools are starting to address this, for now, this means a fairly large investment in resources.
While giving the tech talent – whether Sales or Delivery – a greater context of competitors’ strengths will give them a better chance to win deals, it has the potential to open up a host of issues that are best avoided. However, part of that knowledge can be provided as a general solution context – focusing on customer benefits in doing a certain transition and the kind of use cases that would be most appropriate for each transition. This will require, in most cases, a redesign of the curriculum.
Rethinking Partner Sales Support
A much bigger initiative by the vendors could be to set up sales support specialist groups within their own organisations that can be booked for customer meetings by channel partners. This has become easier now with most organisations being quite accustomed to video conferences and virtual meetings.
The set-up of such a “centre” will require careful planning, expertise, and investment in systems. Most vendors do have something like this for escalations and technical support, but few have a comprehensive program to assist with sales opportunities.
The resources at these centres should be highly specialised and well-paid. This will add cost. However, a group like this can pay for itself as long as more deals are converted. We do not recommend using gold-plated costs, but penny pinching, especially on the quality of resources and systems used here will be a classic case of penny wise, pound foolish. Success here will come from increasing revenue, not decreasing cost.
Clarifying Hiring Policies
Finally, vendors need to be clear about the policies of hiring staff from their channel partners. It is unlikely that this practice is going to stop. For one, if they don’t hire these talents, their competitors might. And channel partners often attract more talent if they are able to hold out the carrot, that the employee can eventually get employed by the vendor.
However, there should be clearer policies, such as only hiring those who have spent a minimum specified time with the partner; providing a longer than normal off-ramp period; seconding the talent to the channel partner for a period during which the employee gets additional training on the vendor’s solutions while working for the channel partner. Incentivising channel partners for providing successful candidates might also be a good idea.
Having bad policies, an attitude of poaching or not cooperating just leads to the channel partners underinvesting in people which harms the vendor as well as the channel. Innovative strategies here can deliver a solution where the channel partner is able to attract better talent and manage the talent while they are in their formative stages. Essentially, they are rewarded for incubating talent. The best talent eventually “graduate” to working for the vendor. This will be a true win-win for both.
Making Upskilling a Norm
The leadership of channel partner organisations need to embrace upskilling of their teams. Employees need technological capability building as well as certification.
Upskilling is a slower burn process than some of the steps discussed earlier, but for partners, this is the key. Partners will need help to put together a full program that nurtures employees at different levels of experience and capability and builds them up with a step-by-step approach. This is actually the job of a full-fledged Learning and Development organisation. Very few partners today either invest in such an organisation or have the ability to work this into their HR practices.
Finding an outsourced partner who can help to design and implement such a program might be the way to go. There is also an opportunity here for multiple channel partners to coordinate and engage one agency, in effect paying to hire an outsider as a joint Chief Learning Officer for all their companies.
In an exploding market where the skill of the employee can land the extra contracts, reel in the customers, whose lifetime value can be enormous, upskilling is vital – it is not really a choice.
Conclusion
There is a need to do things differently and to invest a lot more in talent. In a growing market, channel partners are getting by as revenue is moving upwards anyway. A smart step-by-step investment approach, however, can dramatically change this growth trajectory. That will alter the fortunes of the vendors too. Maintaining the status quo and not spending more on tech talent is actually a poison pill – the market growth is just hiding that fact.
BHP – the multinational mining giant – has signed agreements with AWS and Microsoft Azure as their long-term cloud providers to support their digital transformation journey. This move is expected to accelerate BHP’s cloud journey, helping them deploy and scale their digital operations to the workforce quickly while reducing the need for on-premises infrastructure.
Ecosystm research has consistently shown that many large organisations are using the learnings from how the COVID-19 pandemic impacted their business to re-evaluate their Digital Transformation strategy – leveraging next generation cloud, machine learning and data analytics capabilities.
BHP’s Dual Cloud Strategy
BHP is set to use AWS’s analytics, machine learning, storage and compute platform to deploy digital services and improve operational performance. They will also launch an AWS Cloud Academy Program to train and upskill their employees on AWS cloud skills – joining other Australian companies supporting their digital workforce by forming cloud guilds such as National Australia Bank, Telstra and Kmart Group.
Meanwhile, BHP will use Microsoft’s Azure cloud platform to host their global applications portfolio including SAP S/4 HANA environment. This is expected to enable BHP to reduce their reliance on regional data centres and leverage Microsoft’s cloud environment, licenses and SAP applications. The deal extends their existing relationship with Microsoft where BHP is using Office 365, Dynamics 365 and HoloLens 2 platforms to support their productivity and remote operations.
Ecosystm principal Advisor, Alan Hesketh says, “This dual sourcing is likely to achieve cost benefits for BHP from a competitive negotiation stand-point, and positions BHP well to negotiate further improvements in the future. With their scale, BHP has negotiating power that most cloud service customers cannot achieve – although an effective competitive process is likely to offer tech buyers some improvements in pricing.”
Can this Strategy Work for You?
Hesketh thinks that the split between Microsoft for Operations and AWS for Analytics will provide some interesting challenges for BHP. “It is likely that high volumes of data will need to be moved between the two platforms, particularly from Operations to Analytics and AI. The trend is to run time-critical analytics directly from the operational systems using the power of in-memory databases and the scalable cloud platform.”
“As BHP states, using the cloud reduces the need to put hardware on-premises, and allows the faster deployment of digital innovations from these cloud platforms. While achieving technical and cost improvements in their Operations and Analytics domains, it may compromise the user experience (UX). The UX delivered by the two clouds is quite different – so delivering an integrated experience is likely to require an additional layer that is capable of delivering a consistent UX. BHP already has a strong network infrastructure in place, so they are likely to achieve this within their existing platforms. If there is a need to build this UX layer, it is likely to reduce the speed of deployment that BHP is targeting with the dual cloud procurement approach.”
Many businesses that have previously preferred a single cloud vendor will find that they will increasingly evaluate multiple cloud environments, in the future. The adoption of modern development environments and architectures such as containers, microservices, open-source, and DevOps will help them run their applications and processes on the most suitable cloud option.
While this strategy may well work for BHP, Hesketh adds, “Tech buyers considering a hybrid approach to cloud deployment need to have robust enterprise and technology architectures in place to make sure the users get the experience they need to support their roles.”
Last week I wrote about the need to remove hype from reality when it comes to AI. But what will ensure that your AI projects succeed?
It is quite obvious that success is determined by human aspects rather than technological factors. We have identified four key organisational actions that enable successful AI implementation at scale (Figure 1).
#1 Establish a Data Culture
The traditional focus for companies has been on ensuring access to good, clean data sets and the proper use of that data. Ecosystm research shows that only 28% of organisations focused on customer service, also focus on creating a data-driven organisational culture. But our experience has shown that culture is more critical than having the data. Does the organisation have a culture of using data to drive decisions? Does every level of the organisation understand and use data insights to do their day-to-day jobs? Is decision-making data-driven and decentralised, needing to be escalated only when there is ambiguity or need for strategic clarity? Do business teams push for new data sources when they are not able to get the insights they need?
Without this kind of culture, it may be possible to implement individual pieces of automation in a specific area or process, applying brute force to see it through. In order to transform the business and truly extract the power of AI, we advise organisations to build a culture of data-driven decision-making first. That organisational mindset, will make you capable implementing AI at scale. Focusing on changing the organisational culture will deliver greater returns than trying to implement piecemeal AI projects – even in the short to mid-term.
#2 Ingrain a Digital-First Mindset
Assuming a firm has passed the data culture hurdle, it needs to consider whether it has adopted a digital-first mindset. AI is one of many technologies that impact businesses, along with AR/VR, IoT, 5G, cloud and Blockchain to name a few. Today’s environment requires firms to be capable of utilising a variety of these technologies – often together – and possessing a workforce capable of using these digital tools.
A workforce with the digital-first mindset looks for a digital solution to problems wherever appropriate. They have a good understanding of digital technologies relevant to their space and understand key digital methodologies – such as Customer 360 to deliver a truly superior customer experience or Agile methodologies to successfully manage AI at scale.
AI needs business managers at the operational levels to work with IT or AI tech teams to pinpoint processes that are right for AI. They need to make an estimation based on historical data of what specific problems require an AI solution. This is enabled by the digital-first mindset.
#3 Demystify AI
The next step is to get business leaders, functional leaders, and business operational teams – not just those who work with AI – to acquire a basic understanding of AI.
They do not need to learn the intricacies of programming or how to create neural networks or anything nearly as technical in nature. However, all levels from the leadership down should have a solid understanding of what AI can do, the basics of how it works, how the process of training data results in improved outcomes and so on. They need to understand the continuous learning nature of AI solutions, getting better over time. While AI tools may recommend an answer, human insight is often needed to make a correct decision off this recommendation.
#4 Drive Implementation Bottom-Up
AI projects need alignment, objectives, strategy – and leadership and executive buy-in. But a very important aspect of an AI-driven organisation that is able to build scalable AI, is letting projects run bottom up.
As an example, a reputed Life Sciences company embarked on a multi-year AI project to improve productivity. They wanted to use NLP, Discovery, Cognitive Assist and ML to augment clinical proficiency of doctors and expected significant benefits in drug discovery and clinical trials by leveraging the immense dataset that was built over the last 20 years.
The company ran this like any other transformation project, with a central program management team taking the lead with the help of an AI Centre of Competency. These two teams developed a compelling business case, and identified initial pilots aligned with the long-term objectives of the program. However, after 18 months, they had very few tangible outcomes. Everyone including doctors, research scientists, technicians, and administrators, who participated in the program had their own interpretation of what AI was not able to do.
Discussion revealed that the doctors and researchers felt that they were training AI to replace themselves. Seeing a tool trying to mimic the same access and understanding of numerous documents baffled them at best. They were not ready to work with AI programs step-by-step to help AI tools learn and discover new insights.
At this point, we suggested approaching the project bottom-up – wherein the participating teams would decide specific projects to take up. This developed a culture where teams collaborated as well as competed with each other, to find new ways to use AI. Employees were shown a roadmap of how their jobs would be enhanced by offloading routine decisions to AI. They were shown that AI tools augment the employees’ cognitive capabilities and made them more effective.
The team working on critical trials found these tools extremely useful and were able to collaborate with other organisations specialising in similar trials. They created the metadata and used ML algorithms to discover new insights. Working bottom-up led to a very successful AI deployment.
We have seen time and again that while leadership may set the strategy and objectives, it is best to let the teams work bottom-up to come up with the projects to implement.
#5 Invest in Upskilling
The four “keys” are important to build an AI-powered, future-proof enterprise. They are all human related – and when they come together to work as a winning formula is when organisations invest in upskilling. Upskilling is the common glue and each factor requires specific kinds of upskilling (Figure 2).
Upskilling needs vary by organisational level and the key being addressed. The bottom line is that upskilling is a universal requirement for driving AI at scale, successfully. And many organisations are realising it fast – Bosch and DBS Bank are some of the notable examples.
How much is your organisation invested in upskilling for AI implementation at scale? Share your stories in the comment box below.
Written with contributions from Ravi Pattamatta and Ratnesh Prasad
The National Australia Bank (NAB) and Microsoft announced a strategic partnership last week, to develop and architect a multicloud environment to be used by both NAB and its New Zealand counterpart, Bank of New Zealand (BNZ).
The five-year partnership will involve Microsoft and NAB sharing development costs and investments to migrate around 1,000 out of 2,600 applications from the NAB and BNZ stacks, on Microsoft Azure. By 2023, NAB aims to run 80% of its application on the cloud, build a robust cloud foundation, and enable customers to access applications and services on the cloud.
The partnership aims to support NAB’s commitment to continuous improvement and innovation, leveraging the Microsoft global engineering team. It also involves setting up of the NAB Cloud Guild program, where Microsoft will train 5,000 NAB and BNZ technologists to equip them on cloud and allied technology skills.
NAB and Microsoft have previously collaborated to improve the experience for NAB customers, through cloud-based applications. NAB’s cloud-based AI powered ATM was the result of a proof-of-concept (PoC) developed on Microsoft Azure’s cognitive services, in 2018. It involved general ATM security captures along with facial biometrics to enable customers to withdraw cash without a card or a phone.
Besides the partnership with Microsoft, NAB also uses Google Cloud for multicloud workloads as well as AWS for its AI competencies and resources across platforms. In February, NAB launched an AI-based voice service to boost the bank’s contact centre experience along with AWS.
Ecosystm Comments
Ecosystm Principal Advisor, Tim Sheedy says, “If ever there was a sign that multicloud is the predominant approach for businesses, this is it. NAB is a big AWS client – in Australia and New Zealand. They lead the way for businesses in training thousands of employees on AWS technologies through their Cloud Guild. But now Azure is also developing a strong foothold in NAB – the public cloud services market is not a one-horse race!”
“Many businesses that have standardised on – or preferred – a single cloud vendor will find that they will likely use multiple cloud environments, in the future. The key to enabling this will be the adoption of modern development environments and architectures. Containers, microservices, open-source, DevOps and other technologies and capabilities will help them run their applications, data and processes across the best cloud for them at the time – not just the one that they have used in the past.”
Sheedy thinks, “NAB’s competitive advantage will not come from whether they are using AWS or Azure – it will come from the significant time and effort they are investing in giving their employees the skills they need to take advantage of these environments to drive change at pace. Too many businesses are increasing their cloud usage without making the necessary investments to upskill their employees – if you know you are planning to spend more on the cloud, then start now in reskilling and upskilling your staff. There is already a real shortage of cloud skills and it is only going to get worse.”
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