For the larger mass of professionals, the words “artificial intelligence,” or AI, often conjure up images of robots, the sorts of robots that might someday take their jobs. But at the enterprise level, AI means something different. It has enormous power and potential: it can disrupt, innovate, enhance, and in many cases totally transform a business. Forrester Research predicts a 300% increase in AI investment in 2017 from last year, and IDC estimates that the AI market will surge from about $8 billion in 2016 to more than $47 billion in 2020. There’s solid proof that the investment can pay off—if CEO’s can adopt the right strategy. Organizations that deploy AI strategically enjoy advantages ranging from cost reductions and higher productivity to top-line benefits such as increasing revenue and profits, richer customer experiences, and working-capital optimization. The survey shows that the companies winning at AI are also more likely to enjoy broader business success.

So How to make your Enterprise AI Ready?

just one quarter of organizations say they are getting significant impact from it. But these leading businesses have taken clear, practical steps to get the results they want. Here are five of their key strategies:

• Core AI Resource Assimilation using Funding or Acquisition
• Gain senior management support
• Focus on process, not function
• Reskill your teams and foster a learning culture
• Shift from system-of-record to system-of-intelligence apps, platforms
• Encourage innovation

Core AI Resource Assimilation using Funding or Acquisition

As per insights from Forbes and Cowen & Company, 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.

Through 2020, organization using cognitive ergonomics and system design in new AI projects will achieve long term success four times more often than others    

 – Gartner

With $1.7 billion invested in AI startups in Q1 2017 alone, and the exponential efficiencies created by this sort of technology, this evolution will happen quicker than many business leaders are prepared for. If you aren’t sure where to start, don’t worry – you’re not alone. The good news is that you still have options:

• You can acquire, or invest in, an innovative technology company applying AI/ML in your market, and gain access to new product and AI/ML talent.
• You can seek to invest as a limited partner in a few early stage AI focused VC firms, gaining immediate access and exposure to vetted early stage innovation, a community of experts and market trends.
• You can set out to build an AI-focused division to optimize your internal processes using AI, and map out how AI can be integrated into your future products. But recruiting in the space is painful and you will need a strong vision and sense of purpose to attract and retain the best.
• You can use outside development-for-hire shops like new entrant Element.ai, who raised over $100M last June, or more traditional consulting firms, to fill the gaps or get the ball rolling.

Process Based Focus Rather than Function Based

One critical element differentiates AI success from AI failure: strategy. AI cannot be implemented piecemeal. It must be part of the organization’s overall business plan, along with aligned resources, structures, and processes. How a company prepares its corporate culture for this transformation is vital to its long-term success. That includes preparing people by having senior management that understands the benefits of AI; fostering the right skills, talent, and training; managing change; and creating an environment with processes that welcome innovation before, during, and after the transition.

The challenge of AI isn’t just the automation of processes—it’s about the up-front process design and governance you put in to manage the automated enterprise. The ability to trace the reasoning path AI technologies use to make decisions is important. This visibility is crucial in financial services, where auditors and regulators require firms to understand the source of a machine’s decision.

Taking down Resistance to change of Upper Management

One of the biggest challenges to digital transformation is resistance to change. The survey found that upper management is the group most strongly opposed to AI implementation. C-suite executives may not have warmed up to it either. There is such a lack of understanding about the benefits which the technology can bring that the C-suite or board members simply don’t want to invest in it, nor do they understand that failing to do so will adversely affect their bottom line and even cause them to go out of business. Regulatory uncertainty about AI, rough experiences with previous technological innovation, and a defensive posture to better protect shareholders, not stakeholders, may be contributing factors.

Pursuing AI without senior management support is difficult. Here the numbers again speak for themselves. The majority of leading AI companies (68%) strongly agree that their senior management understands the benefits AI offers. By contrast, only 7% of laggard firms agree with this view. Curiously, though, the leading group still cites the lack of senior management vision as one of the top two barriers to the adoption of AI.

Reskilling Teams and HR Redeployment

HR and corporate management will need to figure out new jobs for people to do. Redeployment is going to be a huge factor that the better companies will learn how to handle. The question of job losses is a sensitive one, most often played up in news headlines. But AI also creates numerous job opportunities in new and different areas, often enabling employees to learn higher-level skills. In healthcare for example, physicians are learning to work with AI-powered diagnostic tools to avoid mistakes and make better decisions. The question is who owns the data. If HR retains ownership of people data, it continues to have a role. If it loses that, all bets are off.

HR’s other role in an AI future will be to help make decisions about if and when to automate, whether to reskill or redeploy the human workforce, and the moral and ethical aspects of such decisions. Companies which are experimenting with bots and AI with no thought for the implications need to realize that HR should be central to the governance of AI automation.

Given the potential of AI to complement human intelligence, it is vital for top-level executives to be educated about reskilling possibilities. It is in the best interest of companies to train workers who are being moved from jobs that are automated by AI to jobs in which their work is augmented by AI.

The Dawn of System-of-Intelligence Apps & Platforms

Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future.

By 2019, AI platform services will cannibalize revenues for 30% of market leading companies    -Gartner

Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behaviour, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

The Process of Supporting Innovation

Besides developing capabilities among employees, an organization’s culture and processes must also support new approaches and technologies. Innovation waves take a lot longer because of the human element. You can’t just put posters on the walls and say, ‘Hey, we have become an AI-enabled company, so let’s change the culture.’ The way it works is to identify and drive visible examples of adoption.

Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years.

In the End, it’s about Transforming Enterprise

AI is part of a much bigger process of re-engineering enterprises. That is the major difference between the sci-fi robots of yesteryear and today’s AI: the technologies of the latter are completely integrated into the fabric of business, allowing private and public-sector organizations to transform themselves and society in profound ways. You don’t have to turn to sci-fi. The story of human/machine collaboration is already playing at an enterprise near

The Goods and Services Tax has come into effect on July 1st and is pegged to be one of the most significant economic tax reforms carried out by PM Narendra Modi. While it will usher in greater transparency and create a simplified channel for tracking through data, it has also spawned the need for ERP and data analytics solutions. Other IT solutions include building capabilities such as billing software and payment gateways, thus creating plentiful opportunities across the IT spectrum. Industry experts say there is a $1 billion opportunity for IT vendors over the next two years.

According to an industry expert, GST will a) remove tax barriers in a fragmented market b) will introduce a transparent and predictable tax regime and boost local and foreign investment in India c) integrate existing multiple taxes into a single GST.

GST – A Data Analytics Powerhouse

In terms of data analytics, the GST rollout is expected to become a “data analytics powerhouse”. According to Goods and Services Tax Network, a not-for-profit organization operated by the government and private players jointly, GST will give enormous amount of data to the tax department to work with, that will eventually rule out discrepancies and help tax sleuths to go after tax evaders. Once sufficient amount of data is generated, GSTN will be able to generate analytics based on the requirements of various stakeholders. Companies in the coming time are expected to build programmes and analytical tools as per the data requirements of both central and state tax departments. The data generated could be on real-time basis, if not near real time.

According to GSTN, the body is building the “information technology backbone for the goods and services tax (GST)” and implement analytics solutions. Here are the features:

• The platform is expected to store information related to relevant transactions
• Based on the data filed by millions of taxpayers that will migrate to the system, analytics will help in identifying leakages and ensure more focused economic-policymaking.
• As per the GST system architecture, the decision-making will be based on data rather than assumptions
• The system shall feature more meta tags so that the time taken by various functions in capturing/entering the data is verified.

Nab Tax Evaders, Boost Domestic IT Biz

The data generated through the technology backbone of the Goods and Services tax regime would, over time, be able to solve issue such as tax evasion and help compliance ratings in the country, according to the GST Network chairman. Navin Kumar, chairman of the GSTN, the entity that handles the information technology backbone of the GST, said that GSTN would soon have enough data to be able to run business analytics and find meaningful ways to interpret and help make sense of the filings in tandem with other government departments. So there is great potential for that (leveraging analytics), but that will be possible only once they have data, maybe after two or three years. GSTN will start developing the applications for that next year.

Among the potential use cases for business analytics, Kumar said GSTN would look to do a rating of the taxpayers, such as a compliance ratings, look at sectoral studies and detection of tax frauds and tax evasion. There, collaboration with income tax will be very useful, to see whether the volume of business reported here (GSTN) is reported to income tax or whether that data syncs with their data. Existing analytics tools available in the market could be used, as well as some new applications that would be developed by IT / Analytics Companies.

According to research firm Gartner, Indian business intelligence software revenue is forecast to reach USD 245 million in constant currency in 2017, a 24.4 percent increase over last year.

GST a boon for small and medium IT cos. The new tax regime would prove to be a boon for the small and medium IT companies in the country. They will have lot of opportunity to provide solutions to businesses, not just become GSPs (GST Suvidha Provider). And the wider roll-out of GST has spawned many opportunities in IT, such as developing ERP packages for the 5 million SMBs that are not yet digitally-empowered. SMBs need to record the GST transactions, upload invoices and do the return filing. This spells a big opportunity for IT vendors who are quick to fill the gap with their enterprise ready solutions. According to news sources, the government expects close to nine million returns to be filed in the first month of its roll-out.

These companies could also develop the functionalities or applications that could help GSPs better. For example, the small and medium IT firms could develop an invoicing system for taxpayers, software for inventory management, and so on, which would provide a boost to the domestic business of the IT services companies.

Here’s a look at some enterprise ready solutions:

SAP HANA:

Earlier in the year, SAP announced ‘GST in a Box’, an all-inclusive solution portfolio, to help Indian organizations of all sizes and across industry verticals to become GST compliant. The solution It also enables organizations to effectively manage suppliers, customer engagement and supply chain in the new tax regime. According to Neeraj Athalye, Head, S/4HANA & GST Adoption Drive, SAP India, businesses need to go digital.  “Out of an estimated 4-5 billion invoice uploads that will happen every month, since more than 40% of transactions will pass through an SAP system, it is upon us to not only help Indian corporates swiftly get compliant with this new law, but also ensure that businesses benefit from the GST vision,” he said.

Microsoft India:

EasemyGST, a cloud-based comprehensive GST compliance platform that integrates with ERP, and Microsoft India teamed up together last month to provide a “simple and affordable platform that will ease their GST requirements, thus, saving them from the expense of separate compliance products”. EasemyGST will integrate its solution with Microsoft’s core business products including Office 365, Dynamics Navision and Axapta. The solutions will run in Microsoft Azure, from India data centres to ensure data sovereignty.

Intuit:

infact, Intuit is betting big on GST rollout, and expects revenues to double. Intuit’s QuickBooks, a cloud-based accounting software for small businesses will help SMBs to stay on top of their business in real time and get paid faster. The company’s cloud-based accounting software QuickBooks already has a slew of big companies on board that will use the ERP system.

So how does this all start ?

The GSTN Company would be on a hiring mode over the next few weeks to cater to these new requirements. They plan to double our workforce from 50 to about 100 over the next few months. In the first phase, GSTN is in the process of building and testing the software interfaces for the taxpayers and the back-end to be used by the tax departments of the Centre and states. In the second phase, the roll out will take place and the company is working to ensure at least one critical process of approval of registration on the back-end is ready from day one. This will eventually bring GST platform to become an analytics powerhouse.